Very ML
State-of-the-art Machine Learning News Feed
/r/MachineLearning /r/MachineLearning
последний пост 24 минуты назад
[P] Facework: face attributes x gig economy game
[P] Facework: face attributes x gig economy game [P] Facework: face attributes x gig economy game

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24 минуты назад @ reddit.com
[P] [D] ML Mind Maps and more
[P] [D] ML Mind Maps and more [P] [D] ML Mind Maps and more

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2 часа назад @ reddit.com
[P] ProSPr: An open source implementation of AlphaFold
[P] ProSPr: An open source implementation of AlphaFold [P] ProSPr: An open source implementation of AlphaFold

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3 часа назад @ reddit.com
[R] Princeton Student’s AI Model Generates Chinese Landscape Paintings That Fool Human Evaluators
[R] Princeton Student’s AI Model Generates Chinese Landscape Paintings That Fool Human Evaluators [R] Princeton Student’s AI Model Generates Chinese Landscape Paintings That Fool Human Evaluators

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3 часа назад @ reddit.com
[N] Synced Tradition and Machine Learning Series | Part 1: Entropy
[N] Synced Tradition and Machine Learning Series | Part 1: Entropy [N] Synced Tradition and Machine Learning Series | Part 1: Entropy

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5 часов назад @ reddit.com
[P] seeking large natural sounds dataset
[P] seeking large natural sounds dataset [P] seeking large natural sounds dataset

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5 часов назад @ reddit.com
[D] Apple's M1 Mac Mini for deep learning
[D] Apple's M1 Mac Mini for deep learning [D] Apple's M1 Mac Mini for deep learning

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6 часов назад @ reddit.com
[P] AWS Notebooks in Emacs
[P] AWS Notebooks in Emacs [P] AWS Notebooks in Emacs

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6 часов назад @ reddit.com
[Discussion]I read this following article about Machine Learning/Neural Networks , and I am worried that how come we are using ML/NN if we don't understand the rationale behind its reasoning . Can someone explain why we are using such big black boxes and d
[Discussion]I read this following article about Machine Learning/Neural Networks , and I am worried that how come we are using ML/NN if we don't understand the rationale behind its reasoning . Can someone explain why we are using such big black boxes and d [Discussion]I read this following article about Machine Learning/Neural Networks , and I am worried that how come we are using ML/NN if we don't understand the rationale behind its reasoning . Can someone explain why we are using such big black boxes and d

https://towardsdatascience.com/the-black-box-metaphor-in-machine-learning-4e57a3a1d2b0I read this article and it made me worried.

What if the data set is biased consciously/unconsciously or someone poisons the data .

If the curator of data biased or not.

Also is it true that Google and Facebook have deployed datafarms worth of NNs where they cant interpret the reasoning behind their NNs ?

7 часов назад @ reddit.com
[R] AlphaFold 2
[R] AlphaFold 2 [R] AlphaFold 2

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8 часов назад @ reddit.com
[N] Final talks in 2020 for Laplace’s Demon: A Seminar Series about Bayesian Machine Learning at Scale
[N] Final talks in 2020 for Laplace’s Demon: A Seminar Series about Bayesian Machine Learning at Scale [N] Final talks in 2020 for Laplace’s Demon: A Seminar Series about Bayesian Machine Learning at Scale

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8 часов назад @ reddit.com
[D] AAAI 2021 Paper Acceptance Result
[D] AAAI 2021 Paper Acceptance Result [D] AAAI 2021 Paper Acceptance Result

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8 часов назад @ reddit.com
[P] Machine Learning applied in Computational Politics - Analysis Conflict
[P] Machine Learning applied in Computational Politics - Analysis Conflict [P] Machine Learning applied in Computational Politics - Analysis Conflict

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8 часов назад @ reddit.com
[P] Spektral v1.0 is out: a lot of new features to help you create GNNs in TF/Keras
[P] Spektral v1.0 is out: a lot of new features to help you create GNNs in TF/Keras [P] Spektral v1.0 is out: a lot of new features to help you create GNNs in TF/Keras

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8 часов назад @ reddit.com
[Research] Hello! can any of you smart and lovely people help with my Computer Science Degree? thanks! :)
[Research] Hello! can any of you smart and lovely people help with my Computer Science Degree? thanks! :) [Research] Hello! can any of you smart and lovely people help with my Computer Science Degree? thanks! :)

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9 часов назад @ reddit.com
Towards Data Science Towards Data Science
последний пост 2 минуты назад
The New Data Engineering Stack
The New Data Engineering Stack The New Data Engineering Stack

DATA ENGINEERINGThe New Data Engineering StackTechnologies for the Complete Data EngineerRemember the time when the software development industry realized that a single person can take on multiple technologies glued tightly with each other and came up with the notion of a Full Stack Developer — someone who does data modelling, writes backend code and also does front end work.

For analytical loads, data lakes, data warehouses, data marts, there’s another list of databases.

Geospatial data requires geospatial databases like PostGIS, time-series data sometimes requires specialised time-series databases like InfluxDB or TimescaleDB.

This means that whatever stack they are using, they should be …

2 минуты назад @ towardsdatascience.com
How to use IF-THEN-ELSE in Python the way you work in SAS
How to use IF-THEN-ELSE in Python the way you work in SAS How to use IF-THEN-ELSE in Python the way you work in SAS

First I will create a SAS data set with a random generated age around a mean of 50 and a variace of 100.

Since you can not generate the same sequence of random numbers in both SAS and Python, I want to use the SAS data set in Python too.

For this article, I have used an environment where I can run both SAS and Python in the same Jupyter notebook.

I import the SAS data set with pd.read_sas() in Python.

In this article, we have seen how you can use conditional coding in Python in a similar way as you do in SAS.

14 минут назад @ towardsdatascience.com
F-beta Score in Keras Part III
F-beta Score in Keras Part III F-beta Score in Keras Part III

Our aim is not to build a high performance model but to demonstrate how to monitor f-beta score in multi-label classification in Keras.

We will now test the rightness of our multi-label f-beta function.

According to Keras documentation, there are four methods a stateful metric should have:__init__ : we create (initialize) the state variables here.

For multi-label f-beta metric, state variables would definitely be true positives, actual positives, predicted positives, number of samples and sum of f-beta scores because they can easily be tracked across all batches.

Let’s now implement a stateful f-beta metric for our multi-label problem.

16 минут назад @ towardsdatascience.com
How to Create and Tune Your Own, Data Set for Facial Recognition using Neural Networks.
How to Create and Tune Your Own,  Data Set for Facial Recognition using Neural Networks. How to Create and Tune Your Own, Data Set for Facial Recognition using Neural Networks.

When gathering facial Embeddings, the embeddings per input image are in the form of a nx1 dimensional vector (n=number of Embeddings).

Therfore, we can create a mean distance (*std, mean error,…) for each Embedding (of each image) towards all other Embeddings (images) (Line 20–22).

Create the Embeddings on those images and then compare your license free image Embeddings to those.

Now create embeddings using the model we use here (much more info on how to create embeddings here and code here ).

Load the Embeddings (Line 2) and change the faceembedding fucntion like follwoing in Line 4–9.

24 минуты назад @ towardsdatascience.com
Why & How to use the Naive Bayes algorithms in a regulated industry with sklearn | Python + code
Why & How to use the Naive Bayes algorithms in a regulated industry with sklearn | Python + code Why & How to use the Naive Bayes algorithms in a regulated industry with sklearn | Python + code

Spoiler alert: as we will see just afterwards, doing a Multinomial Naive Bayes on one hot encoded data provides the exact same results than the Categorical Naive Bayes.

Indeed that is why we have the likelihood equals to the conditional probability of discret_X15 to the first multiplied by the conditional probability of discret_X16 cubed multiplied by the conditional probability of discret_X18 cubed.

How does Naive Bayes work for multinomial features — ComplementNBComplement Naive Bayes [2] is the last algorithm implemented in scikit-learn.

That is why it is better to use one hot encoded data like this:And this provides exactly the same results has the one of Categorical Naive Bayes & Multi…

26 минут назад @ towardsdatascience.com
DeepMind Makes History Yet Again By Solving One of the Biggest Challenges in Biology
DeepMind Makes History Yet Again By Solving One of the Biggest Challenges in Biology DeepMind Makes History Yet Again By Solving One of the Biggest Challenges in Biology

DeepMind Makes History Again By Solving One of the Biggest Challenges in BiologyDeepMind’s AlphaFold can now predict a protein’s structure to the width of an atom.

Photo by Photoholgic on UnsplashYou may have heard about “DeepMind” in the past, and if you haven’t, now you will.

The goal was exactly what AlphaFold 2 achieved, to be able to predict a protein’s structure from its amino-acid sequence.

It will change research.

It will change everything.”More tangibly, DeepMind sees this having a significant impact on drug development and finding treatments and vaccines faster.

1 час назад @ towardsdatascience.com
Churn Analysis Using Information Value and Weight of Evidence
Churn Analysis Using Information Value and Weight of Evidence Churn Analysis Using Information Value and Weight of Evidence

It includes information about customers who left within the last month, services that each customer has signed up for, customer account information and demographic information.

Continuous variables in Telco dataset | Image by authorIV and WOE methodologyInformation value (IV) and weight of evidence (WOE) are simple and powerful techniques of conducting attribute relevance analysis.

In order to answer this question, as an additional part of this analysis, p-value (chi-square test of independence of variables) and effect size (Cramers’ v effect size) were measured.

Information value vs effect sizeThere is strong, almost linear, relationship between information value and effect size.

Features …

1 час назад @ towardsdatascience.com
Variance Makes Life Fun
Variance Makes Life Fun Variance Makes Life Fun

Variance Makes Life FunApplying statistical concepts, like variance, to the real worldIn every introductory statistics class, you are forced to learn about the basic descriptive statistics — mean, median, mode, and standard deviation.

In “The Intelligent Investor” by Ben Graham, it is referenced over 200 times, but the word, “variance”, only appears twice in the novel.

The orange slot machine has a payout of 5 or 10 dollars more frequently than the blue slot machine.

On a casino floor, you could envision each slot machine with the following signs:Image by authorAs a customer, which slot machine are you more likely to play?

Next time you are bored, deviate from your normal behavior and add a…

1 час назад @ towardsdatascience.com
F-beta Score in Keras Part II
F-beta Score in Keras Part II F-beta Score in Keras Part II

F-beta Score in Keras Part IICreating custom F-beta score for multi classification problems in KerasPhoto by Edgar Chaparro on UnsplashIn the previous article (part I), we explained stateless and stateful metrics in Keras, derived the formula for f-beta score and created both stateless and stateful custom f-beta metric in Keras for binary classification problems.

In this article (part II), we will be explaining how f-beta score can be applied to multi-classification problems.

We will also create both stateful and stateless custom f-beta metric for multi classification problems in Keras.

In multi classification problems, we are predicting if an observation belongs to one of a given set of th…

3 часа назад @ towardsdatascience.com
F-beta Score in Keras Part I
F-beta Score in Keras Part I F-beta Score in Keras Part I

The f1 score is the harmonic mean of precision and recall.

A generalization of the f1 score is the f-beta score.

Making f-beta the subject of the formula, we have:We cannot talk about f-beta score without mentioning C. J.

In chapter 7 of his book[1], he laid the premise on which the f-beta score is now being calculated.

F-beta formula finally becomes:We now see that f1 score is a special case of f-beta where beta = 1.

3 часа назад @ towardsdatascience.com
You What? — Keeping track with the Python Logging Module
You What? — Keeping track with the Python Logging Module You What? — Keeping track with the Python Logging Module

Why use loggingWhen debugging a script we have two available channels, the standard output ( stdout ) and the standard error ( stderr ).

Conventionally user outputs should be given within sdout as per the print statement — now inconveniently the print function — , and progress reports are ‘logged’ within stderr .

Skip the printsThe reason we don’t use print everywhere is that if we do the end-user is then flooded with a large amount of irrelevant (for them) information about what the code is doing, and when.

Providing greater clarityAdditionally, the use of logging within python allows us to specify the information on the type of message received.

We have 6 potential logging levels, each wi…

3 часа назад @ towardsdatascience.com
React & D3: Rendering Circles On A Map
React & D3: Rendering Circles On A Map React & D3: Rendering Circles On A Map

Image by authorIn my last article React & D3: Rendering A Map I walked through the code to render a map using both the D3 within React and D3(math)/React(DOM) approaches.

In this article I will perform one last refactor to render the circles using the D3(math)/React(DOM) approach.

Image by authorThe Circles ComponentLet’s start with the Circles component.

The Map ComponentOnce the Circles component was imported into Map it required the same conditional logic to render only when the data was present.

Image by authorConclusionI have to say that I prefer this approach over using D3 to render the circles.

4 часа назад @ towardsdatascience.com
How to Land a Data Science Job in a Tier One Consulting Firm
How to Land a Data Science Job in a Tier One Consulting Firm How to Land a Data Science Job in a Tier One Consulting Firm

Within the field of data science, a nice resume is often not enough.

I used these tricks during my studies and continues to do so today to always improve my data science skills and personal brand.

If you are new to the data science community find a person who inspires you through writing or training sessions.

Enter your GitHub workspace in your resume to illustrate your great data science skills.

The field of data science is in constant development and the most important skill you can show is curiosity towards learning.

4 часа назад @ towardsdatascience.com
Was my data science degree worth it?
Was my data science degree worth it? Was my data science degree worth it?

Was my data science degree worth it?

Most data science qualifications are at a Masters or PhD level, and candidates who pursue these fields already hold a degree from a quantitative background — such as Computer Science or statistics.

This was really exciting, since we would be the first batch of students in the country to graduate with a data science degree.

Expectation VS RealityPhoto by JESHOOTS.COM on UnsplashMy data science degree experience did not match up to the expectation.

I was taking data science courses and creating my own projects, and was somewhat familiar with the data science landscape.

4 часа назад @ towardsdatascience.com
Binomial Distribution — Practical Intro with Drive-Thru Business Analytic
Binomial Distribution — Practical Intro with Drive-Thru Business Analytic Binomial Distribution — Practical Intro with Drive-Thru Business Analytic

Business ProblemThe accuracy of taking order at a drive-thru windows is important for a fast food chain business.

Just imagine if the accuracy of taking order in a fast food restaurant A is approximately 80%.

Solution (Tree Diagram Approach)Firstly, there are only two possible outcomes for every order taken by a customer, either “Order taken correctly” or “Order taken incorrectly”.

So the final solution isThe tree diagram above can be useful if our sample size is small (e.g.

While the tree diagram is a useful visual approach to obtain a probability result, we need to resort to another way that can circumvent the limitation of the tree diagram.

4 часа назад @ towardsdatascience.com
Distill.pub Distill.pub
последний пост 1 неделя, 6 дней назад
Understanding RL vision
Understanding RL vision

With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution.

1 неделя, 6 дней назад @ distill.pub
Communicating with Interactive Articles
Communicating with Interactive Articles

Examining the design of interactive articles by synthesizing theory from disciplines such as education, journalism, and visualization.

2 месяца, 2 недели назад @ distill.pub
Thread: Differentiable Self-organizing Systems
Thread: Differentiable Self-organizing Systems

A collection of articles and comments with the goal of understanding how to design robust and general purpose self-organizing systems

3 месяца назад @ distill.pub
Self-classifying MNIST Digits
Self-classifying MNIST Digits

Training an end-to-end differentiable, self-organising cellular automata for classifying MNIST digits.

3 месяца назад @ distill.pub
Curve Detectors
Curve Detectors

Part one of a three part deep dive into the curve neuron family.

5 месяцев, 2 недели назад @ distill.pub
Exploring Bayesian Optimization
Exploring Bayesian Optimization

How to tune hyperparameters for your machine learning model using Bayesian optimization.

6 месяцев, 4 недели назад @ distill.pub
An Overview of Early Vision in InceptionV1
An Overview of Early Vision in InceptionV1

An overview of all the neurons in the first five layers of InceptionV1, organized into a taxonomy of 'neuron groups.'

8 месяцев назад @ distill.pub
Visualizing Neural Networks with the Grand Tour
Visualizing Neural Networks with the Grand Tour

By focusing on linear dimensionality reduction, we show how to visualize many dynamic phenomena in neural networks.

8 месяцев, 2 недели назад @ distill.pub
Zoom In: An Introduction to Circuits
Zoom In: An Introduction to Circuits

By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.

8 месяцев, 3 недели назад @ distill.pub
Thread: Circuits
Thread: Circuits

What can we learn if we invest heavily in reverse engineering a single neural network?

8 месяцев, 3 недели назад @ distill.pub
Growing Neural Cellular Automata
Growing Neural Cellular Automata

Training an end-to-end differentiable, self-organising cellular automata model of morphogenesis, able to both grow and regenerate specific patterns.

9 месяцев, 3 недели назад @ distill.pub
The Gradient The Gradient
последний пост 1 неделя, 2 дня назад
Interpretability in Machine Learning: An Overview
Interpretability in Machine Learning: An Overview Interpretability in Machine Learning: An Overview

This essay provides a broad overview of the sub-field of machine learning interpretability.

Below, each section is operationalized by a concrete question we can ask of our machine learning model using a specific definition of interpretability.

For other conceptual surveys of the field, Definitions, methods, and applications in interpretable machine learning and Explainable Machine Learning for Scientific Insights and Discoveries.

He is interested in machine learning interpretability and, more broadly, AI safety.

"Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning."

1 неделя, 2 дня назад @ thegradient.pub
Interpretability in ML: A Broad Overview
Interpretability in ML: A Broad Overview Interpretability in ML: A Broad Overview

This essay provides a broad overview of the sub-field of machine learning interpretability.

's Mythos of Model Interpretability, which I think is the best paper for understanding the different definitions of interpretability.

UtilityThe second area is to ensure that these interpretability approaches are actually providing value.

He is interested in machine learning interpretability and, more broadly, AI safety.

CreditsFeature image from https://github.com/adebayoj/sanity_checks_saliencyCitationFor attribution in academic contexts or books, please cite this work asOwen Shen, "Interpretability in ML: A Broad Overview", The Gradient, 2020.

1 неделя, 2 дня назад @ thegradient.pub
How Can We Improve Peer Review in NLP?
How Can We Improve Peer Review in NLP? How Can We Improve Peer Review in NLP?

What can we do to improve peer review?

In a way, NLP peer review… prevents research on NLP peer review.

We need to regularly collect and regularly publish our peer review corpora, to develop mechanisms for running systematic experiments on peer review forms and policies, and for the subsequent implementation of the successful ones.

We need new roles for systematic development, testing, and implementation of peer review policies, as well as incentivizing good peer review work by increasing its prestige and visibility.

To cite our paper:@inproceedings{rogers-augenstein-2020-improve,title = "What Can We Do to Improve Peer Review in {NLP}?

2 недели, 2 дня назад @ thegradient.pub
How Can We Improve Peer Review in NLP?
How Can We Improve Peer Review in NLP? How Can We Improve Peer Review in NLP?

What can we do to improve peer review?

In a way, NLP peer review… prevents research on NLP peer review.

We need to regularly collect and regularly publish our peer review corpora, to develop mechanisms for running systematic experiments on peer review forms and policies, and for the subsequent implementation of the successful ones.

We need new roles for systematic development, testing, and implementation of peer review policies, as well as incentivizing good peer review work by increasing its prestige and visibility.

To cite our paper:@inproceedings{rogers-augenstein-2020-improve,title = "What Can We Do to Improve Peer Review in {NLP}?

2 недели, 2 дня назад @ thegradient.pub
Don’t Forget About Associative Memories
Don’t Forget About Associative Memories Don’t Forget About Associative Memories

When applied to Computer Science problems, associative memories come in two high-level forms: autoassociative and heteroassociative memories.

Research MilestonesIn computer science, associative memories were first proposed by Karl Steinbuch (1961) in the form of the Lernmatrix.

Distributed Associative MemoriesAnother intriguing offshoot of AM research involves scaling via so-called distributed associative memories.

Theoretical Foundations for the Alpha-Beta Associative Memories: 10 Years of Derived Extensions, Models, and Applications.

CitationFor attribution in academic contexts or books, please cite this work asRobert Bates, "Don’t Forget About Associative Memories", The Gradient, 2020.

3 недели, 2 дня назад @ thegradient.pub
Don’t Forget About Associative Memories
Don’t Forget About Associative Memories Don’t Forget About Associative Memories

When applied to Computer Science problems, associative memories come in two high-level forms: autoassociative and heteroassociative memories.

Research MilestonesIn computer science, associative memories were first proposed by Karl Steinbuch (1961) in the form of the Lernmatrix.

Distributed Associative MemoriesAnother intriguing offshoot of AM research involves scaling via so-called distributed associative memories.

Theoretical Foundations for the Alpha-Beta Associative Memories: 10 Years of Derived Extensions, Models, and Applications.

CitationFor attribution in academic contexts or books, please cite this work asRobert Bates, "Don’t Forget About Associative Memories", The Gradient, 2020.

3 недели, 2 дня назад @ thegradient.pub
Why Skin Lesions are Peanuts and Brain Tumors Harder Nuts
Why Skin Lesions are Peanuts and Brain Tumors Harder Nuts Why Skin Lesions are Peanuts and Brain Tumors Harder Nuts

He coined the term computer-aided diagnosis (CAD) to refer to computer systems that help radiologists interpret and quantify abnormalities from medical images.

However, medical images often come in far higher dimensional form than natural images (e.g., ImageNet has images of only 224x224).

In general, the granularity of the label used during training, does not have to be the same as the granularity generated during inference.

Modality Paper Summary Data Labels Chest X-ray Rajpurkar, Pranav, et al.

CitationFor attribution in academic contexts or books, please cite this work asThijs Kooi, "Why skin lesions are peanuts and brain tumors a harder nut", The Gradient, 2020.

1 месяц назад @ thegradient.pub
Why skin lesions are peanuts and brain tumors harder nuts
Why skin lesions are peanuts and brain tumors harder nuts Why skin lesions are peanuts and brain tumors harder nuts

Why are some problems in medical image analysis harder than others for AI, and what can we do about them?

He coined the term computer-aided diagnosis (CAD) to refer to computer systems that help radiologists interpret and quantify abnormalities from medical images.

However, medical images often come in far higher dimensional form than natural images (e.g., ImageNet has images of only 224x224).

In general, the granularity of the label used during training, does not have to be the same as the granularity generated during inference.

CitationFor attribution in academic contexts or books, please cite this work asThijs Kooi, "Why skin lesions are peanuts and brain tumors a harder nut", The Gradie…

1 месяц назад @ thegradient.pub
The Gap: Where Machine Learning Education Falls Short
The Gap: Where Machine Learning Education Falls Short The Gap: Where Machine Learning Education Falls Short

The Current State of Machine Learning EducationHaving taken the main slate of the seminal machine learning courses at one of the top universities for AI, I have found a general guideline most classes follow.

If the course instead focuses on more general machine learning principles, it introduces other avenues such as unsupervised and reinforcement learning.

The GapHaving analyzed both the current state of machine learning education as well as the skills needed to create important applied machine learning systems, we now comment on the gap between the two sides.

CitationFor attribution in academic contexts or books, please cite this work asJupinder Parmar, "The Gap: Where Machine Learning Ed…

1 месяц, 3 недели назад @ thegradient.pub
The Gap: Where Machine Learning Education Falls Short
The Gap: Where Machine Learning Education Falls Short The Gap: Where Machine Learning Education Falls Short

The Current State of Machine Learning EducationHaving taken the main slate of the seminal machine learning courses at one of the top universities for AI, I have found a general guideline most classes follow.

If the course instead focuses on more general machine learning principles, it introduces other avenues such as unsupervised and reinforcement learning.

The GapHaving analyzed both the current state of machine learning education as well as the skills needed to create important applied machine learning systems, we now comment on the gap between the two sides.

CitationFor attribution in academic contexts or books, please cite this work asJupinder Parmar, "The Gap: Where Machine Learning Ed…

1 месяц, 3 недели назад @ thegradient.pub
How the Police Use AI to Track and Identify You
How the Police Use AI to Track and Identify You How the Police Use AI to Track and Identify You

While protestors marched through the city demanding justice for George Floyd and an end to police brutality, Minneapolis police trained surveillance tools to identify them.

In a new twist, these surveillance systems are starting to seep out of metropolitan police departments and into the suburbs.

Less easily spotted, amid the pandemonium, were the automated systems law enforcement rely on to coordinate their response to the protests, spy on them, identify them, and later locate them for arrest.

But police could be confident in the dragnet surveillance systems being built across the country to help them spy on and target protestors and rioters.

CitationFor attribution in academic contexts or…

1 месяц, 4 недели назад @ thegradient.pub
How the Police Use AI to Track and Identify You
How the Police Use AI to Track and Identify You How the Police Use AI to Track and Identify You

While protestors marched through the city demanding justice for George Floyd and an end to police brutality, Minneapolis police trained surveillance tools to identify them.

In a new twist, these surveillance systems are starting to seep out of metropolitan police departments and into the suburbs.

Less easily spotted, amid the pandemonium, were the automated systems law enforcement rely on to coordinate their response to the protests, spy on them, identify them, and later locate them for arrest.

But police could be confident in the dragnet surveillance systems being built across the country to help them spy on and target protestors and rioters.

CitationFor attribution in academic contexts or…

1 месяц, 4 недели назад @ thegradient.pub
AI Democratization in the Era of GPT-3
AI Democratization in the Era of GPT-3 AI Democratization in the Era of GPT-3

To me, AI democratization means making it possible for everyone to create artificial intelligence systems.

For the purposes of this piece, I focus primarily on the "having access to powerful AI models" part of democratization since GPT-3 is such a pre-built AI model.

GPT-3 and other very very large models created at Microsoft and Google are very concerning in how they affect “democratization” of AI.

CitationFor attribution in academic contexts or books, please cite this work asMark Riedl, "AI Democratization in the Era of GPT-3", The Gradient, 2020.

BibTeX citation:@article{rield2020democratizationgpt3,author = {Riedl, Mark},title = {AI Democratization in the Era of GPT-3},journal = {The Gr…

2 месяца назад @ thegradient.pub
AI Democratization in the Era of GPT-3
AI Democratization in the Era of GPT-3 AI Democratization in the Era of GPT-3

To me, AI democratization means making it possible for everyone to create artificial intelligence systems.

For the purposes of this piece, I focus primarily on the "having access to powerful AI models" part of democratization since GPT-3 is such a pre-built AI model.

GPT-3 and other very very large models created at Microsoft and Google are very concerning in how they affect “democratization” of AI.

CitationFor attribution in academic contexts or books, please cite this work asMark Riedl, "AI Democratization in the Era of GPT-3", The Gradient, 2020.

BibTeX citation:@article{rield2020democratizationgpt3,author = {Riedl, Mark},title = {AI Democratization in the Era of GPT-3},journal = {The Gr…

2 месяца назад @ thegradient.pub
Transformers are Graph Neural Networks
Transformers are Graph Neural Networks Transformers are Graph Neural Networks

Through this post, I want to establish a link between Graph Neural Networks (GNNs) and Transformers.

Graph Neural Networks (GNNs) or Graph Convolutional Networks (GCNs) build representations of nodes and edges in graph data.

Are Transformers learning neural syntax?

Transformers are a special case of Graph Neural Networks.

CitationFor attribution in academic contexts or books, please cite this work asChaitanya K. Joshi, "Transformers are Graph Neural Networks", The Gradient, 2020.

2 месяца, 2 недели назад @ thegradient.pub
DataTau DataTau
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100+ Food Processing Machines With Price List
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Keeping up with data — Week 48 reading list
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2 дня назад @ datatau.net
3 tips for data strategy execution from a start-up chief data scientist
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Why is data science failing to solve the right problems?
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The path to learning SQL and mastering it to become a Data Engineer
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100+ Chemical Doors Designs With Price List
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Oswal Pumps Ltd
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The State of Natural Language Applications: Free 2020 Industry Survey Results
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Top Data Science Blogs
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3 дня, 2 часа назад @ datatau.net
Correlation can be confounder determination
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Partial dependence plots are a simple way to make black-box models easy to understand
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We App IT - Software Development Company
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How I Discovered The Most Common Word in Queen Lyrics
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Big Data Conference 2020: My First Ever Online Conference
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Digital Agency Services: What Exactly Various Types Of Digital Agency Do In New York?
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3 дня, 14 часов назад @ datatau.net
Synced Review
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Do We Really Need Green Screens for High-Quality Real-Time Human Matting?
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1 час назад @ medium.com
Princeton Student’s AI Model Generates Chinese Landscape Paintings That Fool Human Evaluators
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‘Biology’s ImageNet Moment’ — DeepMind Says Its AlphaFold Has Cracked a 50-Year-Old Biology…
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Synced Tradition and Machine Learning Series | Part 1: Entropy
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Facebook Proposes Free-Viewpoint Rendering on Monocular Video
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USC & Amazon ‘SLADE’ Self-Training Framework Uses Unlabelled Data to Improve Information Retrieval
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SOD It: University of Alberta’s U²-Net Builds Salient Object Detection on ReSidual U-Blocks
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McGill University, Facebook & Mila Release 14M Article NLP Pretraining Dataset for Medical…
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UP-DETR: Unsupervised ‘Random Query Patch Detection’ Pretrains Transformers for Object Detection
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1 неделя, 3 дня назад @ medium.com
Impersonator++ Human Image Synthesis — Smarten Up Your Dance Moves!
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Facebook Building Automatic Differentiation System for Kotlin Programming Language
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EMNLP 2020 Best Paper Award Goes to UC Berkeley Team
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CoRL 2020 Best System Paper Winner: Noah’s Ark Lab Multi-Agent RL Simulation for Autonomous Driving
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Automatic Image-to-Painting Translation Method Generates Vivid Paintings in Controllable Styles
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DeepMind Open-Sources Lab2D: Environmental Design for Multi-Agent RL Research
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1 неделя, 6 дней назад @ medium.com
🔬 Science
Papers With Code Papers With Code
последний пост 4 часа назад
PREDATOR: Registration of 3D Point Clouds with Low Overlap
PREDATOR: Registration of 3D Point Clouds with Low Overlap PREDATOR: Registration of 3D Point Clouds with Low Overlap

We introduce PREDATOR, a model for pairwise point-cloud registration with deep attention to the overlap region.

Different from previous work, our model is specifically designed to handle (also) point-cloud pairs with low overlap... Its key novelty is an overlap-attention block for early information exchange between the latent encodings of the two point clouds.

In this way the subsequent decoding of the latent representations into per-point features is conditioned on the respective other point cloud, and thus can predict which points are not only salient, but also lie in the overlap region between the two point clouds.

The ability to focus on points that are relevant for matching greatly imp…

4 часа назад @ paperswithcode.com
Learning Curves for Drug Response Prediction in Cancer Cell Lines
Learning Curves for Drug Response Prediction in Cancer Cell Lines Learning Curves for Drug Response Prediction in Cancer Cell Lines

Motivated by the size of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment.

The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these predictors.

The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, suggesting that the shape of these curves depends on the unique model-dataset pair.

Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs.

These observations …

9 часов назад @ paperswithcode.com
ShapeFlow: Dynamic Shape Interpreter for TensorFlow
ShapeFlow: Dynamic Shape Interpreter for TensorFlow ShapeFlow: Dynamic Shape Interpreter for TensorFlow

We present ShapeFlow, a dynamic abstract interpreter for TensorFlow which quickly catches tensor shape incompatibility errors, one of the most common bugs in deep learning code.

ShapeFlow shares the same APIs as TensorFlow but only captures and emits tensor shapes, its abstract domain... ShapeFlow constructs a custom shape computational graph, similar to the computational graph used by TensorFlow.

ShapeFlow requires no code annotation or code modification by the programmer, and therefore is convenient to use.

We evaluate ShapeFlow on 52 programs collected by prior empirical studies to show how fast and accurately it can catch shape incompatibility errors compared to TensorFlow.

ShapeFlow de…

9 часов назад @ paperswithcode.com
Polarization-driven Semantic Segmentation via Efficient Attention-bridged Fusion
Polarization-driven Semantic Segmentation via Efficient Attention-bridged Fusion Polarization-driven Semantic Segmentation via Efficient Attention-bridged Fusion

Semantic Segmentation (SS) is promising for outdoor scene perception in safety-critical applications like autonomous vehicles, assisted navigation and so on.

Thereby, in this work, we present EAFNet, an Efficient Attention-bridged Fusion Network to exploit complementary information coming from different optical sensors.

Specifically, we incorporate polarization sensing to obtain supplementary information, considering its optical characteristics for robust representation of diverse materials.

By using a single-shot polarization sensor, we build the first RGB-P dataset which consists of 394 annotated pixel-aligned RGB-Polarization images.

A comprehensive variety of experiments shows the effec…

9 часов назад @ paperswithcode.com
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space

Tactical decision making and strategic motion planning for autonomous highway driving are challenging due to the complication of predicting other road users' behaviors, diversity of environments, and complexity of the traffic interactions.

This paper presents a novel end-to-end continuous deep reinforcement learning approach towards autonomous cars' decision-making and motion planning... For the first time, we define both states and action spaces on the Frenet space to make the driving behavior less variant to the road curvatures than the surrounding actors' dynamics and traffic interactions.

The algorithm generates continuous spatiotemporal trajectories on the Frenet frame for the feedback…

9 часов назад @ paperswithcode.com
MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons
MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons

Instance segmentation of overlapping objects in biomedical images remains a largely unsolved problem.

We take up this challenge and present MultiStar, an extension to the popular instance segmentation method StarDist...

The key novelty of our method is that we identify pixels at which objects overlap and use this information to improve proposal sampling and to avoid suppressing proposals of truly overlapping objects.

This allows us to apply the ideas of StarDist to images with overlapping objects, while incurring only a small overhead compared to the established method.

MultiStar shows promising results on two datasets and has the advantage of using a simple and easy to train network archit…

16 часов назад @ paperswithcode.com
Molecular representation learning with language models and domain-relevant auxiliary tasks
Molecular representation learning with language models and domain-relevant auxiliary tasks Molecular representation learning with language models and domain-relevant auxiliary tasks

We apply a Transformer architecture, specifically BERT, to learn flexible and high quality molecular representations for drug discovery problems.

We study the impact of using different combinations of self-supervised tasks for pre-training, and present our results for the established Virtual Screening and QSAR benchmarks... We show that: i) The selection of appropriate self-supervised task(s) for pre-training has a significant impact on performance in subsequent downstream tasks such as Virtual Screening.

ii) Using auxiliary tasks with more domain relevance for Chemistry, such as learning to predict calculated molecular properties, increases the fidelity of our learnt representations.

iii) …

16 часов назад @ paperswithcode.com
SelfText Beyond Polygon: Unconstrained Text Detection with Box Supervision and Dynamic Self-Training
SelfText Beyond Polygon: Unconstrained Text Detection with Box Supervision and Dynamic Self-Training SelfText Beyond Polygon: Unconstrained Text Detection with Box Supervision and Dynamic Self-Training

Although a polygon is a more accurate representation than an upright bounding box for text detection, the annotations of polygons are extremely expensive and challenging.

Unlike existing works that employ fully-supervised training with polygon annotations, we propose a novel text detection system termed SelfText Beyond Polygon (SBP) with Bounding Box Supervision (BBS) and Dynamic Self Training (DST), where training a polygon-based text detector with only a limited set of upright bounding box annotations... For BBS, we firstly utilize the synthetic data with character-level annotations to train a Skeleton Attention Segmentation Network (SASN).

Then the box-level annotations are adopted to gu…

16 часов назад @ paperswithcode.com
SLURP: A Spoken Language Understanding Resource Package
SLURP: A Spoken Language Understanding Resource Package SLURP: A Spoken Language Understanding Resource Package

Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications.

However, publicly available SLU resources are limited...

In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement.

SLURP is available at https: //g…

16 часов назад @ paperswithcode.com
Omni-GAN: On the Secrets of cGANs and Beyond
Omni-GAN: On the Secrets of cGANs and Beyond Omni-GAN: On the Secrets of cGANs and Beyond

It has been an important problem to design a proper discriminator for conditional generative adversarial networks (cGANs).

In addition, we unify multiple targets (class, domain, reality, etc.)

into one loss function to enable a wider range of applications.

Our algorithm, named \textbf{Omni-GAN}, achieves competitive performance on a few popular benchmarks.

More importantly, Omni-GAN enjoys both high generation quality and low risks in mode collapse, offering new possibilities for optimizing cGANs.Code is available at \url{https://github.com/PeterouZh/Omni-GAN-PyTorch}.

16 часов назад @ paperswithcode.com
Episodic Self-Imitation Learning with Hindsight
Episodic Self-Imitation Learning with Hindsight Episodic Self-Imitation Learning with Hindsight

Episodic self-imitation learning, a novel self-imitation algorithm with a trajectory selection module and an adaptive loss function, is proposed to speed up reinforcement learning.

Compared to the original self-imitation learning algorithm, which samples good state-action pairs from the experience replay buffer, our agent leverages entire episodes with hindsight to aid self-imitation learning... A selection module is introduced to filter uninformative samples from each episode of the update.

The proposed method overcomes the limitations of the standard self-imitation learning algorithm, a transitions-based method which performs poorly in handling continuous control environments with sparse …

16 часов назад @ paperswithcode.com
Multi-view Depth Estimation using Epipolar Spatio-Temporal Network
Multi-view Depth Estimation using Epipolar Spatio-Temporal Network Multi-view Depth Estimation using Epipolar Spatio-Temporal Network

We present a novel method for multi-view depth estimation from a single video, which is a critical task in various applications, such as perception, reconstruction and robot navigation.

Moreover, current state-of-the-art (SOTA) models mostly adopt a fully 3D convolution network for cost regularization and therefore require high computational cost, thus limiting their deployment in real-world applications.

Our method achieves temporally coherent depth estimation results by using a novel Epipolar Spatio-Temporal (EST) transformer to explicitly associate geometric and temporal correlation with multiple estimated depth maps.

Furthermore, to reduce the computational cost, inspired by recent Mixt…

16 часов назад @ paperswithcode.com
Data-Efficient Classification of Radio Galaxies
Data-Efficient Classification of Radio Galaxies Data-Efficient Classification of Radio Galaxies

The continuum emission from radio galaxies can be generally classified into different classes like FRI, FRII, Bent, or Compact.

In this paper, we explore the task of radio galaxy classification based on morphology using deep learning methods with a focus on using a small scale dataset (~ 2000 samples)... We apply few-shot learning techniques based on Siamese Networks and transfer learning techniques using a pre-trained DenseNet model with advanced techniques like cyclical learning rate, discriminative learning to train the model rapidly.

We achieve a classification accuracy of over 92% using our best performing model with the biggest source of confusion being between Bent and FRII type gala…

16 часов назад @ paperswithcode.com
TinaFace: Strong but Simple Baseline for Face Detection
TinaFace: Strong but Simple Baseline for Face Detection TinaFace: Strong but Simple Baseline for Face Detection

Face detection has received intensive attention in recent years.

Many works present lots of special methods for face detection from different perspectives like model architecture, data augmentation, label assignment and etc., which make the overall algorithm and system become more and more complex...

In this paper, we point out that \textbf{there is no gap between face detection and generic object detection}.

Then we provide a strong but simple baseline method to deal with face detection named TinaFace.

On the hard test set of the most popular and challenging face detection benchmark WIDER FACE \cite{yang2016wider}, with single-model and single-scale, our TinaFace achieves 92.1\% average pr…

16 часов назад @ paperswithcode.com
Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images

Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem.

In this paper, we propose an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs... A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships, and is further embedded in a Dense Attention Fluid (DAF) structure that enables shallow attention cues flow into deep layers to guide the generation of high-level feature attention maps.

Specifically, the GCA module is composed of two key components, where t…

16 часов назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 4 часа назад
Regularization with Latent Space Virtual Adversarial Training
Regularization with Latent Space Virtual Adversarial Training Regularization with Latent Space Virtual Adversarial Training

Virtual Adversarial Training (VAT) has shown impressive results among recently developed regularization methods called consistency regularization.

VAT utilizes adversarial samples, generated by injecting perturbation in the input space, for training and thereby enhances the generalization ability of a classifier...

To address this problem we propose LVAT (Latent space VAT), which injects perturbation in the latent space instead of the input space.

LVAT can generate adversarial samples flexibly, resulting in more adverse effects and thus more effective regularization.

The latent space is built by a generative model, and in this paper, we examine two different type of models: variational auto…

16 часов назад @ paperswithcode.com
A Python Code to Determine Orbital Parameters of Spectroscopic Binaries
A Python Code to Determine Orbital Parameters of Spectroscopic Binaries A Python Code to Determine Orbital Parameters of Spectroscopic Binaries

We present the open source Python code BinaryStarSolver that solves for the orbital elements of a spectroscopic binary system.

Given a time-series of radial velocity measurements, six orbital parameters are determined: the long-term mean, or systemic, radial velocity, the velocity amplitude, the argument of periastron, the eccentricity, the epoch of periastron, and the orbital period referred to by $\{{\gamma, K, \omega, e, T_0, P}\}$ respectively... Also returned to the user is the projected length of the semi-major axis, $a_{1}\sin(i)$, and the mass function, $f(M)$.

The determination of spectroscopic orbits and masses is an example of another important area of astrophysics, once the doma…

16 часов назад @ paperswithcode.com
Point and Ask: Incorporating Pointing into Visual Question Answering
Point and Ask: Incorporating Pointing into Visual Question Answering Point and Ask: Incorporating Pointing into Visual Question Answering

Visual Question Answering (VQA) has become one of the key benchmarks of visual recognition progress.

Multiple VQA extensions have been explored to better simulate real-world settings: different question formulations, changing training and test distributions, conversational consistency in dialogues, and explanation-based answering...

In this work, we further expand this space by considering visual questions that include a spatial point of reference.

First, we explicitly design the benchmarks to require the point input, i.e., we ensure that the visual question cannot be answered accurately without the spatial reference.

Second, we explicitly explore the more realistic point spatial input rath…

16 часов назад @ paperswithcode.com
Domain Adaptative Causality Encoder
Domain Adaptative Causality Encoder Domain Adaptative Causality Encoder

Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the shortage of publicly available labelled data.

Therefore, the resulting models perform poorly when applied to a distributionally different domain for which labelled data did not exist at the time of training... To overcome this limitation, in this paper, we leverage the characteristics of dependency trees and adversarial learning to address the tasks of adaptive causality identification and localisation.

The term adaptive is used since the training and test data come from two distributionally different datasets, which to the best of our knowledge, this work is the fir…

16 часов назад @ paperswithcode.com
Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning
Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning

Network seeding for efficient information diffusion over time-varying graphs~(TVGs) is a challenging task with many real-world applications.

There are several ways to model this spatio-temporal influence maximization problem, but the ultimate goal is to determine the best moment for a node to start the diffusion process...

STIM is also evaluated with a real-world TVG, where it also manages to efficiently propagate information through the nodes.

Finally, we also show that the STIM model has a time complexity of $O(|E|)$.

STIM, therefore, presents a novel approach for efficient information diffusion in TVGs, being highly versatile, where one can change the goal of the model by simply changing…

16 часов назад @ paperswithcode.com
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers

Formal verification of neural networks (NNs) is a challenging and important problem.

Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which splits the problem domain into sub-domains and solves each sub-domain using faster but weaker incomplete verifiers, such as Linear Programming (LP) on linearly relaxed sub-domains...

In this paper, we propose to use the backward mode linear relaxation based perturbation analysis (LiRPA) to replace LP during the BaB process, which can be efficiently implemented on the typical machine learning accelerators such as GPUs and TPUs.

However, unlike LP, LiRPA when applied naively can produce much weaker bounds and even …

16 часов назад @ paperswithcode.com
Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning
Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning

Self-supervised learning achieves superior performance in many domains by extracting useful representations from the unlabeled data.

In this paper, we present SelfTime: a general self-supervised time series representation learning framework, by exploring the inter-sample relation and intra-temporal relation of time series to learn the underlying structure feature on the unlabeled time series.

Specifically, we first generate the inter-sample relation by sampling positive and negative samples of a given anchor sample, and intra-temporal relation by sampling time pieces from this anchor.

Then, based on the sampled relation, a shared feature extraction backbone combined with two separate relati…

16 часов назад @ paperswithcode.com
Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identification
Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identification Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identification

The objective of unsupervised person re-identification (Re-ID) is to learn discriminative features without labor-intensive identity annotations.

State-of-the-art unsupervised Re-ID methods assign pseudo labels to unlabeled images in the target domain and learn from these noisy pseudo labels...

However, during the training, self-ensembled teacher-student networks quickly converge to a consensus which leads to a local minimum.

First, asymmetric branches are proposed to extract features in different manners, which enhances the feature diversity in appearance signatures.

Extensive experiments show that our proposed method can significantly surpass the performance of previous work on both unsupe…

16 часов назад @ paperswithcode.com
Efficient Scene Compression for Visual-based Localization
Efficient Scene Compression for Visual-based Localization Efficient Scene Compression for Visual-based Localization

Estimating the pose of a camera with respect to a 3D reconstruction or scene representation is a crucial step for many mixed reality and robotics applications.

While state-of-the-art methods use $K$-cover-based algorithms to compress a scene, they are slow and hard to tune.

To enhance speed and facilitate parameter tuning, this work introduces a novel approach that compresses a scene representation by means of a constrained quadratic program (QP).

Our approach uses the points corresponding to the support vectors as the subset of points to represent a scene.

Our experiments on publicly available datasets show that our approach compresses a scene representation quickly while delivering accura…

16 часов назад @ paperswithcode.com
Navigating the GAN Parameter Space for Semantic Image Editing
Navigating the GAN Parameter Space for Semantic Image Editing Navigating the GAN Parameter Space for Semantic Image Editing

Generative Adversarial Networks (GANs) are currently an indispensable tool for visual editing, being a standard component of image-to-image translation and image restoration pipelines.

In contrast to existing works, which mostly operate by latent codes, we discover interpretable directions in the space of the generator parameters.

By several simple methods, we explore this space and demonstrate that it also contains a plethora of interpretable directions, which are an excellent source of non-trivial semantic manipulations.

The discovered manipulations cannot be achieved by transforming the latent codes and can be used to edit both synthetic and real images.

We release our code and models an…

16 часов назад @ paperswithcode.com
Generalized Pose-and-Scale Estimation using 4-Point Congruence Constraints
Generalized Pose-and-Scale Estimation using 4-Point Congruence Constraints Generalized Pose-and-Scale Estimation using 4-Point Congruence Constraints

We present gP4Pc, a new method for computing the absolute pose of a generalized camera with unknown internal scale from four corresponding 3D point-and-ray pairs.

Unlike most pose-and-scale methods, gP4Pc is based on constraints arising from the congruence of shapes defined by two sets of four points related by an unknown similarity transformation... By choosing a novel parametrization for the problem, we derive a system of four quadratic equations in four scalar variables.

The variables represent the distances of 3D points along the rays from the camera centers.

After solving this system via Groebner basis-based automatic polynomial solvers, we compute the similarity transformation using a…

16 часов назад @ paperswithcode.com
Stratified and vertically-shearing streaming instabilities in protoplanetary disks
Stratified and vertically-shearing streaming instabilities in protoplanetary disks Stratified and vertically-shearing streaming instabilities in protoplanetary disks

Under the right conditions, the streaming instability between imperfectly coupled dust and gas is a powerful mechanism for planetesimal formation as it can concentrate dust grains to the point of gravitational collapse.

In its simplest form, the streaming instability can be captured by analyzing the linear stability of unstratified disk models, which represent the midplane of protoplanetary disks... We extend such studies by carrying out vertically-global linear stability analyses of dust layers in protoplanetary disks.

These vertically-shearing streaming instabilities grow on orbital timescales and occur on radial length scales $\sim10^{-3}H_\mathrm{g}$, where $H_\mathrm{g}$ is the local p…

23 часа назад @ paperswithcode.com
Adversarial Generation of Continuous Images
Adversarial Generation of Continuous Images Adversarial Generation of Continuous Images

In most existing learning systems, images are typically viewed as 2D pixel arrays.

However, in another paradigm gaining popularity, a 2D image is represented as an implicit neural representation (INR) -- an MLP that predicts an RGB pixel value given its (x,y) coordinate...

In this paper, we propose two novel architectural techniques for building INR-based image decoders: factorized multiplicative modulation and multi-scale INRs, and use them to build a state-of-the-art continuous image GAN.

Previous attempts to adapt INRs for image generation were limited to MNIST-like datasets and do not scale to complex real-world data.

Our proposed architectural design improves the performance of continu…

2 дня назад @ paperswithcode.com
An end-to-end data-driven optimisation framework for constrained trajectories
An end-to-end data-driven optimisation framework for constrained trajectories An end-to-end data-driven optimisation framework for constrained trajectories

Many real-world problems require to optimise trajectories under constraints.

In this paper, we leverage data-driven approaches to design a new end-to-end framework which is dynamics-free for optimised and realistic trajectories.

We first decompose the trajectories on function basis, trading the initial infinite dimension problem on a multivariate functional space for a parameter optimisation problem.

A maximum \emph{a posteriori} approach which incorporates information from data is used to obtain a new optimisation problem which is regularised.

We apply our data-driven approach to two settings in aeronautics and sailing routes optimisation, yielding commanding results.

2 дня, 12 часов назад @ paperswithcode.com
Improving Clinical Outcome Predictions Using Convolution overMedical Entities with Multimodal Learning
Improving Clinical Outcome Predictions Using Convolution overMedical Entities with Multimodal Learning Improving Clinical Outcome Predictions Using Convolution overMedical Entities with Multimodal Learning

However, many studies did not benefit from the clinical notes because of the sparse, and high dimensional nature.

In this work, we extract medical entities from clinical notes and use them as additional features besides time-series features to improve our predictions.

We propose a convolution based multimodal architecture, which not only learns effectively combining medical entities and time-series ICU signals of patients, but also allows us to compare the effect of different embedding techniques such as Word2vec, FastText on medical entities.

In the experiments, our proposed method robustly outperforms all other baseline models including different multimodal architectures for all clinical …

3 дня, 1 час назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 4 часа назад
Neural Representations for Modeling Variation in English Speech
Neural Representations for Modeling Variation in English Speech Neural Representations for Modeling Variation in English Speech

Variation in speech is often represented and investigated using phonetic transcriptions, but transcribing speech is time-consuming and error prone.

To create reliable representations of speech independent from phonetic transcriptions, we investigate the extraction of acoustic embeddings from several self-supervised neural models... We use these representations to compute word-based pronunciation differences between non-native and native speakers of English, and evaluate these differences by comparing them with human native-likeness judgments.

We show that Transformer-based speech representations lead to significant performance gains over the use of phonetic transcriptions, and find that fea…

3 дня, 7 часов назад @ paperswithcode.com
An Open Framework for Remote-PPG Methods and their Assessment
An Open Framework for Remote-PPG Methods and their Assessment An Open Framework for Remote-PPG Methods and their Assessment

This paper presents a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG).

There has been a remarkable development of rPPG techniques in recent years, and the publication of several surveys too, yet a sound assessment of their performance has been overlooked at best, whether not undeveloped...

The proposed framework is instantiated in the form of a Python package named pyVHR (short for Python tool for Virtual Heart Rate), which is made freely available on GitHub (github.com/phuselab/pyVHR).

Here, to substantiate our approach, we evaluate eight well-known rPPG methods, through extensive experiments across five public video datas…

3 дня, 13 часов назад @ paperswithcode.com
PeleNet: A Reservoir Computing Framework for Loihi
PeleNet: A Reservoir Computing Framework for Loihi PeleNet: A Reservoir Computing Framework for Loihi

High-level frameworks for spiking neural networks are a key factor for fast prototyping and efficient development of complex algorithms.

Such frameworks have emerged in the last years for traditional computers, but programming neuromorphic hardware is still a challenge... Often low level programming with knowledge about the hardware of the neuromorphic chip is required.

The PeleNet framework aims to simplify reservoir computing for the neuromorphic hardware Loihi.

The framework manages weight matrices, parameters and probes.

In particular, it provides an automatic and efficient distribution of networks over several cores and chips.

3 дня, 19 часов назад @ paperswithcode.com
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning

Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in the arms of spiral galaxies, etc.)

In future surveys, which are expected to collect hundreds of petabytes of data and detect billions of objects, such an approach will not be feasible.

We investigate the use of convolutional neural networks (CNNs) for the problem of separating LSBGs from artifacts in survey images.

That model, which we call DeepShadows, achieves a test accuracy of $92.0 \%$, a significant improvement relative to feature-based machine lear…

4 дня, 2 часа назад @ paperswithcode.com
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach

Generating natural language under complex constraints is a principled formulation towards controllable text generation.

We present a framework to allow specification of combinatorial constraints for sentence generation... We propose TSMH, an efficient method to generate high likelihood sentences with respect to a pre-trained language model while satisfying the constraints.

Our approach is highly flexible, requires no task-specific training, and leverages efficient constraint satisfaction solving techniques.

To better handle the combinatorial constraints, a tree search algorithm is embedded into the proposal process of the Markov chain Monte Carlo (MCMC) to explore candidates that satisfy mo…

4 дня, 2 часа назад @ paperswithcode.com
The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation
The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation

We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training.

Experiments on three fundus image datasets demonstrate that this approach achieves state-of-the-art results and can be implemented using a simple and efficient fully-convolutional network with a parameter count of less than 0.8M...

Furthermore, we show that this framework - called VLight - avoids overfitting to specific training images and generalizes well across different datasets, which makes it highly suitable for real-world applications where robustness, accuracy as well as low inference time on hig…

4 дня, 7 часов назад @ paperswithcode.com
Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methods
Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methods Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methods

Deep learning (DL) has shown promise in modeling complex spatial data for brain anomaly detection...

Here, we show that also simple statistical methods such as voxel-wise (baseline and covariance) models and a linear projection method using spatial patterns can achieve DL-equivalent (3D convolutional autoencoder) performance in unsupervised pathology detection.

We show that these simple methods can be more accurate in detecting small lesions and are considerably easier to train and comprehend.

Our results demonstrate that while DL methods may be useful, they should show a sufficiently large performance improvement over simpler methods to justify their usage.

Thus, simple statistical methods…

4 дня, 7 часов назад @ paperswithcode.com
SAR-Net: A End-to-End Deep Speech Accent Recognition Network
SAR-Net: A End-to-End Deep Speech Accent Recognition Network SAR-Net: A End-to-End Deep Speech Accent Recognition Network

This paper proposes a end-to-end deep network to recognize kinds of accents under the same language, where we develop and transfer the deep architecture in speaker-recognition area to accent classification task for learning utterance-level accent representation.

Compared with the individual-level feature in speaker-recognition, accent recognition throws a more challenging issue in acquiring compact group-level features for the speakers with the same accent, hence a good discriminative accent feature space is desired... Our deep framework adopts multitask-learning mechanism and mainly consists of three modules: a shared CNNs and RNNs based front-end encoder, a core accent recognition branch,…

4 дня, 7 часов назад @ paperswithcode.com
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation

While knowledge distillation (transfer) has been attracting attentions from the research community, the recent development in the fields has heightened the need for reproducible studies and highly generalized frameworks to lower barriers to such high-quality, reproducible deep learning research.

Several researchers voluntarily published frameworks used in their knowledge distillation studies to help other interested researchers reproduce their original work...

In this paper, we present our developed open-source framework built on PyTorch and dedicated for knowledge distillation studies.

The framework is designed to enable users to design experiments by a declarative PyYAML configuration fil…

4 дня, 7 часов назад @ paperswithcode.com
The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability
The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability

High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining.

These representations have different degrees of interpretability, with efficient distributed representations coming at the cost of the loss of feature to dimension mapping...

Its effects are seen in many representations and tasks, one particularly problematic one being in language representations where the societal biases, learned from underlying data, are captured and occluded in unknown dimensions and subspaces.

This work addresses some of these problems pertaining to the transparency and interpretability of such…

4 дня, 7 часов назад @ paperswithcode.com
The dynamics of learning with feedback alignment
The dynamics of learning with feedback alignment The dynamics of learning with feedback alignment

Direct Feedback Alignment (DFA) is emerging as an efficient and biologically plausible alternative to the ubiquitous backpropagation algorithm for training deep neural networks.

Despite relying on random feedback weights for the backward pass, DFA successfully trains state-of-the-art models such as Transformers... On the other hand, it notoriously fails to train convolutional networks.

This two-step process has a degeneracy breaking effect: out of all the low-loss solutions in the landscape, a network trained with DFA naturally converges to the solution which maximises gradient alignment.

We also identify a key quantity underlying alignment in deep linear networks: the conditioning of the a…

4 дня, 13 часов назад @ paperswithcode.com
Play Fair: Frame Attributions in Video Models
Play Fair: Frame Attributions in Video Models Play Fair: Frame Attributions in Video Models

In this paper, we introduce an attribution method for explaining action recognition models.

Such models fuse information from multiple frames within a video, through score aggregation or relational reasoning... We break down a model's class score into the sum of contributions from each frame, fairly.

Our method adapts an axiomatic solution to fair reward distribution in cooperative games, known as the Shapley value, for elements in a variable-length sequence, which we call the Element Shapley Value (ESV).

We employ ESV to explain two action recognition models (TRN and TSN) on the fine-grained dataset Something-Something.

We offer detailed analysis of supporting/distracting frames, and the r…

4 дня, 13 часов назад @ paperswithcode.com
Image Inpainting with Contextual Reconstruction Loss
Image Inpainting with Contextual Reconstruction Loss Image Inpainting with Contextual Reconstruction Loss

Recent studies in image inpainting attempt to overcome this issue by explicitly searching reference regions throughout the entire image to fill the features from reference regions in the missing regions...

Also, it often fails to find proper reference regions due to the lack of supervision in terms of the correspondence between missing regions and known regions.

We propose a novel contextual reconstruction loss (CR loss) to solve these problems.

First, a criterion of searching reference region is designed based on minimizing reconstruction and adversarial losses corresponding to the searched reference and the ground-truth image.

Experimental results demonstrate that the proposed inpainting …

4 дня, 13 часов назад @ paperswithcode.com
SurFree: a fast surrogate-free black-box attack
SurFree: a fast surrogate-free black-box attack SurFree: a fast surrogate-free black-box attack

This paper presents SurFree, a geometrical approach that achieves a similar drastic reduction in the amount of queries in the hardest setup: black box decision-based attacks (only the top-1 label is available).

We first highlight that the most recent attacks in that setup, HSJA, QEBA and GeoDA all perform costly gradient surrogate estimations.

SurFree proposes to bypass these, by instead focusing on careful trials along diverse directions, guided by precise indications of geometrical properties of the classifier decision boundaries.

We motivate this geometric approach before performing a head-to-head comparison with previous attacks with the amount of queries as a first class citizen.

We ex…

4 дня, 13 часов назад @ paperswithcode.com
Supercharging Imbalanced Data Learning With Causal Representation Transfer
Supercharging Imbalanced Data Learning With Causal Representation Transfer Supercharging Imbalanced Data Learning With Causal Representation Transfer

Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of primary interest.

Our proposal posits a meta-distributional scenario, where the data generating mechanism is invariant across the label-conditional feature distributions.

Such causal assumption enables efficient knowledge transfer from the dominant classes to their under-represented counterparts, even if the respective feature distributions show apparent disparities.

This allows us to leverage a causal data inflation procedure to enlarge the representation of minority classes.

The utility of our proposal is validate…

4 дня, 13 часов назад @ paperswithcode.com
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последний пост 1 месяц назад
Шесть степеней свободы: 3D object detection и не только
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В компьютерном зрении часто приходится работать с двумерными изображениями, и значительно реже - с 3D объектами. Из-за этого многие ML инженеры чувствуют себя неуверенно в этой области: много незнакомых слов, непонятно, куда тут применить старых друзей Resnet и Unet. Поэтому сегодня я хотел бы немного поговорить о 3D на примере задачи определения шести степеней свободы, что в каком-то виде синонимично 3D object detection. Я разберу одну из свежих работ на эту тему с некоторыми отступлениями. Кратко о задачеДля начала давайте определимся, что такое шесть степеней свободы (6 DoF - degrees of freedom). Представим себе некоторый ригидный (неизменяемый, т.е. при трансформации все точки будут ост…

1 месяц назад @ habr.com
Рубрика «Читаем статьи за вас». Июль — август 2020 года
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Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!

Статьи на сегодня: High-Resolution Neural Face Swapping for Visual Effects (Disney Research Studios, ETH Zurich, 2020)

Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (USA, 2020)

Thieves on Sesame Street! Model Extraction of BERT-based APIs (UMass & Google Research, ICLR, 2019)

Time-Aware User Embeddings as a Service (Yahoo! Research, Temple University, 2020)

Are Labels Necessary for Neural Architecture Search? (Johns Hopkins University, Facebook AI Research, 2020)

GShard: Scaling Giant Mo…

1 месяц, 2 недели назад @ habr.com
Data Fest 2020 — полностью в Online уже завтра
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Data Fest пройдет в этом году в онлайн формате 19 и 20 сентября 2020. Фестиваль организован сообществом Open Data Science и как обычно соберет исследователей, инженеров и разработчиков в области анализа данных, искусственного интеллекта и машинного обучения. Регистрация. Ну а дальше к деталям. Читать дальше →

2 месяца, 1 неделя назад @ habr.com
Рубрика «Читаем статьи за вас». Июнь 2020 года
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Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!

Статьи на сегодня: PointRend: Image Segmentation as Rendering (Facebook AI Research, 2020)

Natural- To Formal-Language Generation Using Tensor Product Representations (USA, 2019)

Linformer: Self-Attention with Linear Complexity (Facebook AI, 2020)

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution (Johns Hopkins University, Google, 2020)

Training Generative Adversarial Networks with Limited Data (NVIDIA, 2020)

Multi-Modal Dense Video Captioning (Tampere University…

3 месяца, 1 неделя назад @ habr.com
Итоговые проекты курса Deep Learning in Natural Language Processing (by DeepPavlov Lab)
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Недавно завершился «Deep Learning in Natural Language Processing», открытый образовательный курс по обработке естественного языка. По традиции кураторы курса — сотрудники проекта DeepPavlov, открытой библиотеки для разговорного искусственного интеллекта, которую разрабатывают в лаборатории нейронных систем и глубокого обучения МФТИ. Курс проводился при информационной поддержке сообщества Open Data Science. Если нужно больше деталей по формату курса, то вам сюда. Один из ключевых элементов «DL in NLP» — это возможность почувствовать себя исследователем и реализовать собственный проект. Периодически мы рассказываем на Medium о проектах, которые участники создают в рамках наших образовательных…

3 месяца, 3 недели назад @ habr.com
Нет времени объяснять, сделай автопилот
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Здравствуйте, товарищи! На выходных проходил хакасборкатон — гонки на самоуправляемых моделях автомобилей на базе комплекта donkeycar при содействии Х5, FLESS и сообщества энтузиастов self-driving. Задача заключалась в следующем: сначала надо было собрать машинку из запчастей, затем ее обучить проходить трассу. Победитель определялся по самому быстрому прохождению 3 кругов. За наезд на конус — дисквалификация. Хотя подобная задача для машинного обучения не нова, но сложности могут поджидать на всем пути: от невозможности заставить нормально работать вайфай до нежелания обученной модели пилотировать железо по треку. И все это в жестких временных рамках! Когда мы собирались на это соревновани…

3 месяца, 3 недели назад @ habr.com
Рубрика «Читаем статьи за вас». Май 2020. Часть 2
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Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!

Статьи на сегодня: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks (China, 2020)

TAPAS: Weakly Supervised Table Parsing via Pre-training (Google, 2020)

DeepFaceLab: A simple, flexible and extensible faceswapping framework (2020)

End-to-End Object Detection with Transformers (Facebook AI, 2020)

Language Models are Few-Shot Learners (OpenAI, 2020)

TabNet: Attentive Interpretable Tabular Learning (Google Cloud AI, 2020) Читать дальше →

5 месяцев, 1 неделя назад @ habr.com
Рубрика «Читаем статьи за вас». Май 2020. Часть 1
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Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!

Статьи на сегодня: Efficient Document Re-Ranking for Transformers by Precomputing Term Representations; EARL: Speedup Transformer-based Rankers with Pre-computed Representation (2020)

MakeItTalk: Speaker-Aware Talking Head Animation (Adobe, University of Massachusetts Amherst, Huya, 2020)

Jukebox: A Generative Model for Music (OpenAI, 2020)

Recipes for building an open-domain chatbot (Facebook AI Research, 2020)

One-Shot Object Detection without Fine-Tuning (HKUST, Hong Kong, Tencent, 2020)

f-BRS: Rethinki…

5 месяцев, 2 недели назад @ habr.com
Рубрика «Читаем статьи за вас». Апрель 2020. Часть 2
Рубрика «Читаем статьи за вас». Апрель 2020. Часть 2 Рубрика «Читаем статьи за вас». Апрель 2020. Часть 2

Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!

Статьи на сегодня: Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization (Georgia Institute of Technology, Atlanta, USA, 2016)

X3D: Expanding Architectures for Efficient Video Recognition (Facebook AI Research, 2020)

Adaptive Attention Span in Transformers (Facebook AI Research, 2019)

ResNeSt: Split-Attention Networks (Amazon, 2020)

Weight Standardization (Johns Hopkins University, 2019)

Supervised Contrastive Learning (Google Research, MIT, 2020)

Improved Training Speed, Accurac…

6 месяцев назад @ habr.com
Рубрика «Читаем статьи за вас». Апрель 2020. Часть 1
Рубрика «Читаем статьи за вас». Апрель 2020. Часть 1 Рубрика «Читаем статьи за вас». Апрель 2020. Часть 1

Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!

Статьи на сегодня: TResNet: High Performance GPU-Dedicated Architecture (DAMO Academy, Alibaba Group, 2020)

Controllable Person Image Synthesis with Attribute-Decomposed GAN (China, 2020)

Learning to See Through Obstructions (Taiwan, USA, 2020)

Tracking Objects as Points (UT Austin, Intel Labs, 2020)

CookGAN: Meal Image Synthesis from Ingredients (USA, UK, 2020)

Designing Network Design Spaces (FAIR, 2020)

Gradient Centralization: A New Optimization Technique for Deep Neural Networks (Hong Kong, Alibaba, 2…

6 месяцев, 1 неделя назад @ habr.com
Лекарей сжигать нельзя беречь сейчас
Лекарей сжигать нельзя беречь сейчас Лекарей сжигать нельзя беречь сейчас

TLDR: кому перестановки делают больнее — меряем свёрткой графов.

Код: RolX и ванильная трёхслойная GCN на мотифах. Выгорание на рабочем месте повстречал ещё в начале своей карьеры — и с тех пор живо интересуюсь этим вопросом. Представьте обстановку. Большой проект внедрения SAP. Высокие ставки. Амбициозные сроки. Нагрузку каждый воспринимал по-своему. Кто-то сорвался и самоустранился от выполнения обязанностей, кто-то стал токсичнее, у меня самого в какой-то момент чувство юмора пропало. Ненадолго. Управление изменениями (дисциплина, направленная на снижение напряжения во время внедрения информационных систем) многим обязана медикам. Во-первых, сам феномен эмоционального выгорания впервые з…

7 месяцев назад @ habr.com
Рубрика «Читаем статьи за вас». Март 2020. Часть 2
Рубрика «Читаем статьи за вас». Март 2020. Часть 2 Рубрика «Читаем статьи за вас». Март 2020. Часть 2

Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество! Первая часть мартовской сборки обзоров опубликована ранее.

Статьи на сегодня: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (UC Berkeley, Google Research, UC San Diego, 2020)

Scene Text Recognition via Transformer (China, 2020)

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization (Imperial College London, Google Research, 2019)

Lagrangian Neural Networks (Princeton, Oregon, Google, Flatiron, 2020)

Deformable Style Transfer (Chicago, USA, 2020)

Rethinking…

7 месяцев, 2 недели назад @ habr.com
Рубрика «Читаем статьи за вас». Март 2020. Часть 1
Рубрика «Читаем статьи за вас». Март 2020. Часть 1 Рубрика «Читаем статьи за вас». Март 2020. Часть 1

Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!

Статьи на сегодня: Fast Differentiable Sorting and Ranking (Google Brain, 2020)

MaxUp: A Simple Way to Improve Generalization of Neural Network Training (UT Austin, 2020)

Deep Nearest Neighbor Anomaly Detection (Jerusalem, Israel, 2020)

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (Google, 2020)

SpERT: Span-based Joint Entity and Relation Extraction with Transformer Pre-training (RheinMain University, Germany, 2019)

High-Resolution Daytime Translation Without Domain Labels (Samsung AI Cen…

7 месяцев, 3 недели назад @ habr.com
Машинное обучение на языке R с использованием пакета mlr3
Машинное обучение на языке R с использованием пакета mlr3 Машинное обучение на языке R с использованием пакета mlr3

Источник: https://mlr3book.mlr-org.com/ Привет, Хабр! В этом сообщении мы рассмотрим самый продуманный на сегодняшний день подход к машинному обучению на языке R — пакет mlr3 и экосистему вокруг него. Данный подход основан на «нормальном» ООП с использованием R6-классов и на представлении всех операций с данными и моделями в виде графа вычислений. Это позволяет создавать упорядоченные и гибкие пайплайны для задач машинного обучения, но на первых порах может показаться сложным и запутанным. Ниже постараемся внести определенную ясность и замотивировать к использованию mlr3 в ваших проектах. Содержание: Немного истории и сравнение с конкурирующими решениями

Технические детали: R6-классы и паке…

7 месяцев, 3 недели назад @ habr.com
Распространение сферического коня в вакууме по территории РФ
Распространение сферического коня в вакууме по территории РФ Распространение сферического коня в вакууме по территории РФ

Привет от ODS. Мы откликнулись на идею tutu.ru поработать с их датасетом пассажиропотока РФ. И если в посте Milfgard огромная таблица выводов и научпоп, то мы хотим рассказать что под капотом.

Что, опять очередной пост про COVID-19? Да, но нет. Нам это было интересно именно с точки зрения математических методов и работы с интересным набором данных. Прежде, чем вы увидите под катом красивые картинки и графики, я обязан сказать несколько вещей: любое моделирование — это очень сложный процесс, внутри которого невероятное количество ЕСЛИ и ПРЕДПОЛОЖИМ. Мы о них расскажем.

те, кто работал над этой статьей — не эпидемиологи или вирусологи. Мы просто группа любителей теории графов, практикующих ме…

8 месяцев назад @ habr.com
inFERENCe inFERENCe
последний пост 1 неделя, 3 дня назад
Some Intuition on the Neural Tangent Kernel
Some Intuition on the Neural Tangent Kernel Some Intuition on the Neural Tangent Kernel

November 20, 2020Some Intuition on the Neural Tangent KernelNeural tangent kernels are a useful tool for understanding neural network training and implicit regularization in gradient descent.

Now we have a little bit of background to start talking about this neural tangent kernel thing.

It turns out the neural tangent kernel becomes particularly useful when studying learning dynamics in infinitely wide feed-forward neural networks.

This Gaussian process has a kernel or covariance function which is not, in general, the same as the neural tangent kernel.

So I hope this post helps a bit by building some intuition about what the neural tangent kernel is.

1 неделя, 3 дня назад @ inference.vc
Notes on Causally Correct Partial Models
Notes on Causally Correct Partial Models Notes on Causally Correct Partial Models

November 12, 2020Notes on Causally Correct Partial ModelsI recently encountered this cool paper in a reading group presentation:Rezende et al (2020) Rezende Causally Correct Partial Models for Reinforcement LearningIt's frankly taken me a long time to understand what was going on, and it took me weeks to write this half-decent explanation of it.

The agent then updates their state $s_t$ based on its past state $s_{t-1}$, the new observation $y_t$, and the previous action taken $a_{t-1}$.

Therefore, we would like to get away without modelling the whole observation sequence $y_{1:T}$, which brings us to partial models.

The red path is the causal path: $a_1$ indirectly influences $y_2$ via the …

2 недели, 4 дня назад @ inference.vc
The Spectator The Spectator
последний пост 1 месяц назад
Imaginations of Good, Missions for Change
Imaginations of Good, Missions for Change Imaginations of Good, Missions for Change

The possibilities of good and imaginations of new types of futures that were now possible must have been exhilarating.

That the project of AI for good is a veneer we put over technical products as part of its marketing strategy.

The definition of good then becomes tied to the change mission.

The change mission I’d like you to consider is the eradication of neglected tropical diseases (NTDs).

And three pieces of writing I’ve contributed to: AI for Social Good: Unlocking the opportunity for positive change.

1 месяц назад @ blog.shakirm.com
Queering Machine Learning
Queering Machine Learning Queering Machine Learning

I’ve entitled this talk ‘Queering Machine Learning’, which is theme I want to explore with you today.

This is a queering of machine learning, and a powerful tool of self-refelection; an approach to machine learning research that is more critical and responsible; a tool available not only to queer researchers, but to everyone.

We can already see this same type of failure beginning to manifest in machine learning as well, with examples abound.

By organising in machine learning, and by queering machine learning, we build collective community and collective strength that makes it possible for belonging and loneliness and solitude to co-exist and strengthen each other for the benefit of our fiel…

3 месяца, 3 недели назад @ blog.shakirm.com
Queer Exceptionalism in Science
Queer Exceptionalism in Science Queer Exceptionalism in Science

Read in 5mins (800 words)Today’s queer scientist is exceptional.

Role of the Queer ScientistFor queer people to hold a recognised role in scientific life requires an acknowledgement that to be queer has consequences.

Challenges Facing Queer ScientistsFor the queer scientist, every encounter involves a conscious act of deliberation, risk assessment, and effort, well before any effort of research is begun.

For queer scientists, every new encounter—with a colleague, supervisor, possible letter-writer, examiner, moderator, student, interviewer, acquaintance, or future-friend—sets up a stressful coming-out scene.

To be queer in science is to ask to belong and to be safe.

9 месяцев, 1 неделя назад @ blog.shakirm.com
The Unofficial Google Data Science Blog The Unofficial Google Data Science Blog
последний пост 1 неделя, 6 дней назад
Adding Common Sense to Machine Learning with TensorFlow Lattice
Adding Common Sense to Machine Learning with TensorFlow Lattice Adding Common Sense to Machine Learning with TensorFlow Lattice

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

In the next section, we describe lattice models, which allow feature interactions that are guaranteed to align with common sense.

In this section, we extend the ideas of building monotonic GAMs and lattice models to construct monotonic deep learning models.

As a result, such deep learning models are inflexible, losing much of the benefit of using a deep learning model.

GAMs feed element-wise piecewise-linear layers into a monotonic linear layer, while Lattice models combine piecewise-linear layers with lattice layers.

1 неделя, 6 дней назад @ unofficialgoogledatascience.com
Changing assignment weights with time-based confounders
Changing assignment weights with time-based confounders Changing assignment weights with time-based confounders

When assignment weights change in a ramp-up experiment, there are periods of constant assignment weights that we define as epochs.

In an OCE with constant assignment weights and a representative sample, this is an unbiased estimator.However, when there are changing assignment weights, then an unweighted average of data across the epochs can be a biased estimate.

Epoch: If assignment weights are changed at times $Z^*_1, ..., Z^*_J$ then the assignment weights are constant during $[Z^*_j, Z^*_{j+1})$.

An experimenter who changes assignment weights gets the same answer as the experimenter who doesn’t change assignment weights (modulo some rounding errors) so long as they use the adjusted estim…

4 месяца, 1 неделя назад @ unofficialgoogledatascience.com
Off the Convex Path
последний пост 3 дня, 15 часов назад
Can implicit regularization in deep learning be explained by norms?
Can implicit regularization in deep learning be explained by norms? Can implicit regularization in deep learning be explained by norms?

Can implicit regularization in deep learning be explained by norms?

Understanding the implicit regularization induced by gradient-based optimization is possibly the biggest challenge facing theoretical deep learning these days.

A standard test-bed: matrix factorizationA standard test-bed for theoretically studying implicit regularization in deep learning is matrix factorization $-$ matrix completion via linear neural networks.

However, they represent opposite views on the question of whether or not norms can explain implicit regularization in matrix factorization.

Implicit regularization can drive all norms to infinityThe main result in our paper is a proof that there exist simple matrix co…

3 дня, 15 часов назад @ offconvex.org
How to allow deep learning on your data without revealing the data
How to allow deep learning on your data without revealing the data How to allow deep learning on your data without revealing the data

But privacy laws such as HIPAA forbid them from sharing the data itself, so somehow they have to train a deep net on their data without revealing their data.

This notion was adapted to machine learning by positing that “privacy” in machine learning refers to trained classifiers not being dependent on data of individuals.

(Caveat 1): In deep learning applications, DP’s provable guarantees are very weak.

Which brings us to the question we started with: Could consumers allow machine learning to be done on their data without revealing their data?

Map $n \times T$ encryptions into $n$ private images, by clustering encryptions of a same private image as a group.

2 недели, 5 дней назад @ offconvex.org
Beyond log-concave sampling
Beyond log-concave sampling

Paralleling the state of affairs in optimization, we have a variety of (provably efficient) algorithms for sampling from log-concave distributions, under a variety of access models to the distribution.

Formalizing the sampling problemThe formulation of the sampling problem we will consider is as follows:Problem: Sample from a distribution $p(x) \propto e^{-f(x)}$ given black-box access to $f$ and $abla f$.

Similarly, for sampling, when $p$ is log-concave, the distribution is unimodal and a Markov Chain which is a close relative of gradient descent — Langevin Monte Carlo — is efficient.

[](http://www.andrew.cmu.edu/user/aristesk/table_opt.jpg)Before we move on to non-log-concave distribution…

2 месяца, 1 неделя назад @ offconvex.org
Training GANs - From Theory to Practice
Training GANs - From Theory to Practice Training GANs - From Theory to Practice

Training GANs - From Theory to PracticeGANs, originally discovered in the context of unsupervised learning, have had far reaching implications to science, engineering, and society.

However, training GANs remains challenging (in part) due to the lack of convergent algorithms for nonconvex-nonconcave min-max optimization.

In this post, we present a new first-order algorithm for min-max optimization which is particularly suited to GANs.

ConclusionIn this post we have shown how to develop a practical and convergent first-order algorithm for training GANs.

Our simulations show that a version of this algorithm can lead to more stable training of GANs.

4 месяца, 3 недели назад @ offconvex.org
An equilibrium in nonconvex-nonconcave min-max optimization
An equilibrium in nonconvex-nonconcave min-max optimization An equilibrium in nonconvex-nonconcave min-max optimization

Unlike minimization, where algorithms can always be shown to converge to some local minimum, there is no notion of a local equilibrium in min-max optimization that exists for general nonconvex-nonconcave functions.

Our greedy min-max equilibriumWe use the greedy max function to define a new second-order notion of local optimality for min-max optimization, which we refer to as a greedy min-max equilibrium.

This allows us to define a notion of greedy min-max equilibrium.

Greedy min-max equilibrium: $(x^{\star}, y^{\star})$ is an $\varepsilon$-greedy min-max equilibrium if\(\|abla_y f(x^\star,y^\star)\| \leq \varepsilon, \qquadabla^2_y f(x^\star,y^\star) \preceq \sqrt{\varepsilon},\)\(\|abla_x…

5 месяцев, 1 неделя назад @ offconvex.org
Exponential Learning Rate Schedules for Deep Learning (Part 1)
Exponential Learning Rate Schedules for Deep Learning (Part 1) Exponential Learning Rate Schedules for Deep Learning (Part 1)

Exponential Learning Rate Schedules for Deep Learning (Part 1)This blog post concerns our ICLR20 paper on a surprising discovery about learning rate (LR), the most basic hyperparameter in deep learning.

These divergent approaches suggest that LR, the most basic and intuitive hyperparameter in deep learning, has not revealed all its mysteries yet.

SOTA performance with exponential LRAs mentioned, reaching state-of-the-art accuracy requires reducing the learning rate a few times.

Suppose the training has $K$ phases, and the learning rate is divided by some constant $C_I>1$ when entering phase $I$.

ConclusionWe hope that this bit of theory and supporting experiments have changed your outlook o…

7 месяцев, 1 неделя назад @ offconvex.org
Machine Learning Mastery Machine Learning Mastery
последний пост 1 день, 6 часов назад
Blending Ensemble Machine Learning With Python
Blending Ensemble Machine Learning With Python Blending Ensemble Machine Learning With Python

Tutorial OverviewThis tutorial is divided into four parts; they are:Blending Ensemble Develop a Blending Ensemble Blending Ensemble for Classification Blending Ensemble for RegressionBlending EnsembleBlending is an ensemble machine learning technique that uses a machine learning model to learn how to best combine the predictions from multiple contributing ensemble member models.

# make predictions with base models meta_X = list ( ) for name , model in models : # predict with base model yhat = model .

Each base model can be fit on the entire training dataset (unlike the blending ensemble) and evaluated on the test dataset (just like the blending ensemble).

shape ) ) # create the base models …

1 день, 6 часов назад @ machinelearningmastery.com
How to Develop Random Forest Ensembles With XGBoost
How to Develop Random Forest Ensembles With XGBoost How to Develop Random Forest Ensembles With XGBoost

Tutorial OverviewThis tutorial is divided into five parts; they are:Random Forest With XGBoost XGBoost API for Random Forest XGBoost Random Forest for Classification XGBoost Random Forest for Regression XGBoost Random Forest HyperparametersRandom Forest With XGBoostXGBoost is an open-source library that provides an efficient implementation of the gradient boosting ensemble algorithm, referred to as Extreme Gradient Boosting or XGBoost for short.

You can learn more about the random forest ensemble algorithm in the tutorial:The main benefit of using the XGBoost library to train random forest ensembles is speed.

# check xgboost version import xgboost # display version print(xgboost.__version__…

4 дня, 6 часов назад @ machinelearningmastery.com
How to Develop a Light Gradient Boosted Machine (LightGBM) Ensemble
How to Develop a Light Gradient Boosted Machine (LightGBM) Ensemble How to Develop a Light Gradient Boosted Machine (LightGBM) Ensemble

After completing this tutorial, you will know:Light Gradient Boosted Machine (LightGBM) is an efficient open-source implementation of the stochastic gradient boosting ensemble algorithm.

Tutorial OverviewThis tutorial is divided into three parts; they are:Light Gradient Boosted Machine Algorithm LightGBM Scikit-Learn API LightGBM Ensemble for Classification LightGBM Ensemble for Regression LightGBM Hyperparameters Explore Number of Trees Explore Tree Depth Explore Learning Rate Explore Boosting TypeLight Gradient Boosted Machine AlgorithmGradient boosting refers to a class of ensemble machine learning algorithms that can be used for classification or regression predictive modeling problems.…

6 дней, 6 часов назад @ machinelearningmastery.com
Extreme Gradient Boosting (XGBoost) Ensemble in Python
Extreme Gradient Boosting (XGBoost) Ensemble in Python Extreme Gradient Boosting (XGBoost) Ensemble in Python

How to explore the effect of XGBoost model hyperparameters on model performance.

For more on gradient boosting, see the tutorial:Extreme Gradient Boosting, or XGBoost for short is an efficient open-source implementation of the gradient boosting algorithm.

— Tianqi Chen, in answer to the question “What is the difference between the R gbm (gradient boosting machine) and xgboost (extreme gradient boosting)?” on QuoraThe two main reasons to use XGBoost are execution speed and model performance.

XGBoost Scikit-Learn APIXGBoost can be installed as a standalone library and an XGBoost model can be developed using the scikit-learn API.

# check xgboost version import xgboost print(xgboost.__version__…

1 неделя, 1 день назад @ machinelearningmastery.com
A Gentle Introduction to PyCaret for Machine Learning
A Gentle Introduction to PyCaret for Machine Learning A Gentle Introduction to PyCaret for Machine Learning

Tweet Share SharePyCaret is a Python open source machine learning library designed to make performing standard tasks in a machine learning project easy.

Sonar Dataset Comparing Machine Learning Models Tuning Machine Learning ModelsWhat Is PyCaret?

# check pycaret version import pycaret print('PyCaret: %s' % pycaret.__version__) 1 2 3 # check pycaret version import pycaret print ( 'PyCaret: %s' % pycaret .

PyCaret for Comparing Machine Learning ModelsIn this section, we will evaluate and compare the performance of standard machine learning models on the Sonar classification dataset.

Tuning Machine Learning ModelsIn this section, we will tune the hyperparameters of a machine learning model on…

1 неделя, 4 дня назад @ machinelearningmastery.com
How to Develop a Feature Selection Subspace Ensemble in Python
How to Develop a Feature Selection Subspace Ensemble in Python How to Develop a Feature Selection Subspace Ensemble in Python

Tutorial OverviewThis tutorial is divided into three parts; they are:Feature Selection Subspace Ensemble Single Feature Selection Method Ensembles ANOVA F-statistic Ensemble Mutual Information Ensemble Recursive Feature Selection Ensemble Combined Feature Selection Ensembles Ensemble With Fixed Number of Features Ensemble With Contiguous Number of FeaturesFeature Selection Subspace EnsembleThe random subspace method or random subspace ensemble is an approach to ensemble learning that fits a model on different groups of randomly selected columns in the training dataset.

Single Feature Selection Method EnsemblesIn this section, we will explore creating an ensemble from the features selected b…

1 неделя, 6 дней назад @ machinelearningmastery.com
Develop a Bagging Ensemble with Different Data Transformations
Develop a Bagging Ensemble with Different Data Transformations Develop a Bagging Ensemble with Different Data Transformations

How to develop a data transform ensemble for classification and confirm the ensemble performs better than any contributing member.

Data Transform Ensemble for ClassificationWe can develop a data transform approach to bagging for classification using the scikit-learn library.

For example:... # normalization norm = Pipeline([('s', MinMaxScaler()), ('m', DecisionTreeClassifier())]) models.append(('norm', norm)) ... # define the voting ensemble ensemble = VotingClassifier(estimators=models, voting='hard') 1 2 3 4 5 6 7 .

# define the voting ensemble ensemble = VotingClassifier ( estimators = models , voting = 'hard' )To make the code easier to read, we can define a function get_ensemble() to cr…

2 недели, 1 день назад @ machinelearningmastery.com
Multivariate Adaptive Regression Splines (MARS) in Python
Multivariate Adaptive Regression Splines (MARS) in Python Multivariate Adaptive Regression Splines (MARS) in Python

Tweet Share ShareMultivariate Adaptive Regression Splines, or MARS, is an algorithm for complex non-linear regression problems.

Tutorial OverviewThis tutorial is divided into three parts; they are:Multivariate Adaptive Regression Splines MARS Python API MARS Worked Example for RegressionMultivariate Adaptive Regression SplinesMultivariate Adaptive Regression Splines, or MARS for short, is an algorithm designed for multivariate non-linear regression problems.

0.1.0 1 0.1.0A MARS model can be created with default model hyperparameters by creating an instance of the Earth class.

# define the model model = Earth ( ) # fit the model on the whole dataset model .

PapersBooksSection 9.4 MARS: Multi…

2 недели, 4 дня назад @ machinelearningmastery.com
How to Identify Overfitting Machine Learning Models in Scikit-Learn
How to Identify Overfitting Machine Learning Models in Scikit-Learn How to Identify Overfitting Machine Learning Models in Scikit-Learn

We can identify if a machine learning model has overfit by first evaluating the model on the training dataset and then evaluating the same model on a holdout test dataset.

It is a tool that can help you learn more about the learning dynamics of a machine learning model.

Example of Overfitting in Scikit-LearnIn this section, we will look at an example of overfitting a machine learning model to a training dataset.

(7000, 20) (3000, 20) (7000,) (3000,) 1 (7000, 20) (3000, 20) (7000,) (3000,)Next, we can explore a machine learning model overfitting the training dataset.

I believe this is the sticking point for beginners that often ask how to fix overfitting for their scikit-learn machine learni…

2 недели, 6 дней назад @ machinelearningmastery.com
Develop an Intuition for How Ensemble Learning Works
Develop an Intuition for How Ensemble Learning Works Develop an Intuition for How Ensemble Learning Works

These elements are how and ensemble methods work in the general sense, namely:Members learn different mapping functions for the same problem.

We can see the contributing members along the top, each with different decision boundaries in the feature space.

The relationship of the feature space and the target variable dimension can then be summarized as a hyperplane, e.g.

Consider a plane or graph where the x-axis represents the input feature and the y-axis represents the target variable.

The figure below gives an example of a one-dimensional input feature space and a target space with different learned hyperplane mappings.

3 недели, 1 день назад @ machinelearningmastery.com
Stochastic Hill Climbing in Python from Scratch
Stochastic Hill Climbing in Python from Scratch Stochastic Hill Climbing in Python from Scratch

How to implement the hill climbing algorithm from scratch in Python.

Tutorial OverviewThis tutorial is divided into three parts; they are:Hill Climbing Algorithm Hill Climbing Algorithm Implementation Example of Applying the Hill Climbing AlgorithmHill Climbing AlgorithmThe stochastic hill climbing algorithm is a stochastic local search optimization algorithm.

Hill Climbing Algorithm ImplementationAt the time of writing, the SciPy library does not provide an implementation of stochastic hill climbing.

Example of Applying the Hill Climbing AlgorithmIn this section, we will apply the hill climbing optimization algorithm to an objective function.

How to implement the hill climbing algorithm fr…

3 недели, 4 дня назад @ machinelearningmastery.com
Curve Fitting With Python
Curve Fitting With Python Curve Fitting With Python

Tutorial OverviewThis tutorial is divided into three parts; they are:Curve Fitting Curve Fitting Python API Curve Fitting Worked ExampleCurve FittingCurve fitting is an optimization problem that finds a line that best fits a collection of observations.

Now that we are familiar with curve fitting, let’s look at how we might perform curve fitting in Python.

Curve Fitting Python APIWe can perform curve fitting for our dataset in Python.

Curve Fitting Worked ExampleWe will develop a curve to fit some real world observations of economic data.

show ( )Running the example performs curve fitting and finds the optimal parameters to our objective function.

3 недели, 6 дней назад @ machinelearningmastery.com
Random Forest for Time Series Forecasting
Random Forest for Time Series Forecasting Random Forest for Time Series Forecasting

Random Forest can also be used for time series forecasting, although it requires that the time series dataset be transformed into a supervised learning problem first.

Tutorial OverviewThis tutorial is divided into three parts; they are:Random Forest Ensemble Time Series Data Preparation Random Forest for Time SeriesRandom Forest EnsembleRandom forest is an ensemble of decision tree algorithms.

For more on the Random Forest algorithm, see the tutorial:Time Series Data PreparationTime series data can be phrased as supervised learning.

Random Forest for Time SeriesIn this section, we will explore how to use the Random Forest regressor for time series forecasting.

# load and plot the time serie…

4 недели, 1 день назад @ machinelearningmastery.com
How to Develop a Random Subspace Ensemble With Python
How to Develop a Random Subspace Ensemble With Python How to Develop a Random Subspace Ensemble With Python

Tutorial OverviewThis tutorial is divided into three parts; they are:Random Subspace Ensemble Random Subspace Ensemble via Bagging Random Subspace Ensemble for Classification Random Subspace Ensemble for Regression Random Subspace Ensemble Hyperparameters Explore Number of Trees Explore Number of Features Explore Alternate AlgorithmRandom Subspace EnsembleA predictive modeling problem consists of one or more input variables and a target variable.

This is referred to as a random subspace ensemble or the random subspace method.

Random Subspace Ensemble via BaggingWe can implement the random subspace ensemble using bagging in scikit-learn.

Random Subspace Ensemble for ClassificationIn this sec…

1 месяц назад @ machinelearningmastery.com
Error-Correcting Output Codes (ECOC) for Machine Learning
Error-Correcting Output Codes (ECOC) for Machine Learning Error-Correcting Output Codes (ECOC) for Machine Learning

After completing this tutorial, you will know:Error-correcting output codes is a technique for using binary classification models on multi-class classification prediction tasks.

Several machine learning algorithms, such as SVM, were originally designed to solve only binary classification tasks.

... # define the binary classification model model = LogisticRegression() # define the ecoc model ecoc = OutputCodeClassifier(model, code_size=2, random_state=1) 1 2 3 4 5 .

The example below provides a full example of how to fit and use an error-correcting output model as a final model.

Specifically, you learned:Error-correcting output codes is a technique for using binary classification models on m…

1 месяц назад @ machinelearningmastery.com
Lil'Log Lil'Log
последний пост 1 месяц назад
How to Build an Open-Domain Question Answering System?
How to Build an Open-Domain Question Answering System? How to Build an Open-Domain Question Answering System?

(Image source: Yang et al., 2019)ElasticSearch + BM25 is used by the Multi-passage BERT QA system (Wang et al., 2019).

Neural IRThere is a long history in learning a low-dimensional representation of text, denser than raw term-based vectors (Deerwester et al., 1990; Yih, et al., 2011).

How REALM computes two probabilities, \(p(z \vert x)\) and \(p(y \vert x, z)\), is the same as ORQA.

Depending whether using the same or different retrieved documents for each token generation, there are two versions of RAG:\[\begin{aligned} p_\text{RAG-seq}(y \vert x) &= \sum_{z \in \text{TOP}_k(p_\eta(.\vert x))} p_\eta(z \vert x) \prod_i^N p_\theta(y_i \vert x, z, y_{1:i-1}) \\ p_\text{RAG-token}(y \vert x…

1 месяц назад @ lilianweng.github.io
Neural Architecture Search
Neural Architecture Search Neural Architecture Search

Neural Architecture Search (NAS) automates network architecture engineering.

By dissecting the methods for NAS into three components: search space, search algorithm and child model evolution strategy, this post reviews many interesting ideas for better, faster and more cost-efficient automatic neural architecture search.

Search space: The NAS search space defines a set of operations (e.g.

So far we have seen many interesting new ideas on automating the network architecture engineering through neural architecture search and many have achieved very impressive performance.

Liu et al (2020) delve into the question “Can we find high-quality neural architecture without human-annotated labels?” an…

3 месяца, 3 недели назад @ lilianweng.github.io
Exploration Strategies in Deep Reinforcement Learning
Exploration Strategies in Deep Reinforcement Learning Exploration Strategies in Deep Reinforcement Learning

2007) sketched an idea of using a forward dynamics prediction model to estimate learning progress and assigned intrinsic exploration reward accordingly.

And by definition we have \(p(s_f, \Omega \vert s_0) = p^J(s_f \vert s_0, \Omega) p^C(\Omega \vert s_0)\).

Combining them, we get mutual information \(I(\Omega; s_f \vert s_0)\) to maximize:\[\begin{aligned} I(\Omega; s_f \vert s_0) &= H(s_f \vert s_0) - H(s_f \vert s_0, \Omega) \\ &= - \sum_{s_f} p(s_f \vert s_0) \log p(s_f \vert s_0) + \sum_{s_f, \Omega} p(s_f, \Omega \vert s_0) \log \frac{p(s_f, \Omega \vert s_0)}{p^C(\Omega \vert s_0)} \\ &= - \sum_{s_f} p(s_f \vert s_0) \log p(s_f \vert s_0) + \sum_{s_f, \Omega} p^J(s_f \vert s_0, \Ome…

5 месяцев, 3 недели назад @ lilianweng.github.io
The Transformer Family
The Transformer Family The Transformer Family

(2018) added a set of auxiliary losses to enable training a deep Transformer model on character-level language modeling which outperformed LSTMs.

Longer Attention Span (Transformer-XL)The vanilla Transformer has a fixed and limited attention span.

Image Transformer (Parmer, et al 2018) embraces a formulation of image generation similar to sequence modeling within the Transformer framework.

The top row illustrates the attention connectivity patterns in (a) Transformer, (b) Sparse Transformer with strided attention, and (c) Sparse Transformer with fixed attention.

2019)Cited as:@article{weng2020transformer, title = "The Transformer Family", author = "Weng, Lilian", journal = "lilianweng.githu…

7 месяцев, 3 недели назад @ lilianweng.github.io
Piekniewski's blog
последний пост 5 месяцев, 3 недели назад
AI - the no bullshit approach
AI - the no bullshit approach AI - the no bullshit approach

In this post I'd like share some of that agenda, in what I call the "no bullshit" approach to AI.

And since we don't see these things, we don't label datasets with them and hence these "symbols" never make it to AI, neither from the symbolic approach, nor machine learning approach.

Notably the stuff deep learning is mostly successfully used for these days is not mission critical.

The science wayThe scientific approach is really what this blog was all about, before it veered into making cynical posts about the general AI stupidity out there.

Failure of deep learning on delivering of many promises will likely lead to a similar winter.

5 месяцев, 3 недели назад @ blog.piekniewski.info
DeflAition
DeflAition DeflAition

Full loyalty to the charter is expected, to the point of even varying the compensation by the level of "faith" .

It is often better to invest resources in getting slightly better data, add one more sensor, than train some ridiculously huge deep learning model and expect miracles.

With honesty and integrity rarely found in Silicon Valley, he went in and said what many were whispering for a while - AI is not really "AI".

Deep learning in clinical applicationsThere was some buzz about deep learning replacing radiologists, nonsense initiated by Hinton and then promptly repeated by Andrew Ng.

The realization that deep learning is not going to cut it with respect to self driving cars and many oth…

7 месяцев, 3 недели назад @ blog.piekniewski.info
Autonomous vehicle safety myths and facts, 2020 update.
Autonomous vehicle safety myths and facts, 2020 update. Autonomous vehicle safety myths and facts, 2020 update.

As usual, these number are not really measuring reliably the safety of AV's and there are plenty ways to game them, or overreport.

Please refer to my last years post for a deeper discussion (and 2017 post here, 2018 post here) on why these numbers are essentially flawed.

Nevertheless these are the only official numbers we get, the only glimpse of transparency into this giant corporate endeavor called the "self driving car".

Nevertheless even Waymo and Cruise disengagements are still approximately an order of magnitude from the upper bound of human crash rate.

They finally have recorded some autonomous testing miles with the DMV, all 12.2 of them.

9 месяцев назад @ blog.piekniewski.info
Sebastian Ruder Sebastian Ruder
последний пост None
💼 University and corporation labs
DeepMind DeepMind
последний пост 1 день назад
AlphaFold: a solution to a 50-year-old grand challenge in biology
AlphaFold: a solution to a 50-year-old grand challenge in biology AlphaFold: a solution to a 50-year-old grand challenge in biology

We’ve also seen signs that protein structure prediction could be useful in future pandemic response efforts, as one of many tools developed by the scientific community.

Earlier this year, we predicted several protein structures of the SARS-CoV-2 virus, including ORF3a, whose structures were previously unknown.

Since DNA specifies the amino acid sequences that comprise protein structures, the genomics revolution has made it possible to read protein sequences from the natural world at massive scale – with 180 million protein sequences and counting in the Universal Protein database (UniProt).

In contrast, given the experimental work needed to go from sequence to structure, only around 170,000 …

1 день назад @ deepmind.com
Breaking down global barriers to access
Breaking down global barriers to access Breaking down global barriers to access

Our scholarships aim to support underrepresented students – spanning gender, race, ethnicity, and socio-economic background.

Last year, 70% of all AI-related research was published in Europe, the US, and China, while many other important regions and countries are significantly underrepresented.

We are also establishing new scholarships in Canada and France, and continuing our support for scholars in the UK and the US.

The full list of universities partnering in our scholarships programme is here.

To ensure AI is of global benefit, talent must be nurtured in regions which are currently underrepresented in AI research, and space for geographically and socially diverse, local contributions to …

3 недели, 5 дней назад @ deepmind.com
FermiNet: Quantum Physics and Chemistry from First Principles
FermiNet: Quantum Physics and Chemistry from First Principles FermiNet: Quantum Physics and Chemistry from First Principles

Representing the state of a quantum system is far more challenging.

This is exactly where we thought deep neural networks could help.

In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks.

We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant.

1 месяц, 1 неделя назад @ deepmind.com
Fast reinforcement learning through the composition of behaviours
Fast reinforcement learning through the composition of behaviours Fast reinforcement learning through the composition of behaviours

GPE and GPI in contextThe work on GPE and GPI is at the intersection of two separate branches of research related to these operations individually.

The first, related to GPE, is the work on the successor representation, initiated with Dayan’s seminal paper from 1993.

The second branch of research at the origins of GPE and GPI, related to the latter, is concerned with composing behaviours to create new behaviours.

Both the composition of behaviours and hierarchical RL are today dynamic areas of research (see further reading: "GPI, hierarchical RL, and related approaches").

The fast adaptation provided by GPE and GPI is promising for building faster learning RL agents.

1 месяц, 2 недели назад @ deepmind.com
Traffic prediction with advanced Graph Neural Networks
Traffic prediction with advanced Graph Neural Networks Traffic prediction with advanced Graph Neural Networks

Graph Neural Networks extend the learning bias imposed by Convolutional Neural Networks and Recurrent Neural Networks by generalising the concept of “proximity”, allowing us to have arbitrarily complex connections to handle not only traffic ahead or behind us, but also along adjacent and intersecting roads.

These mechanisms allow Graph Neural Networks to capitalise on the connectivity structure of the road network more effectively.

This ability of Graph Neural Networks to generalise over combinatorial spaces is what grants our modeling technique its power.

We discovered that Graph Neural Networks are particularly sensitive to changes in the training curriculum - the primary cause of this in…

2 месяца, 4 недели назад @ deepmind.com
Applying for technical roles
Applying for technical roles Applying for technical roles

What can I expect in the interview process?

Feryal: The interview process at DeepMind can vary depending on the particular role you’re applying for.

Phase two - technical interviewsThis part of the process involves several sessions - including one with a technical quiz that covers a large breadth of topics in computer science, statistics, mathematics and machine learning.

~30min] interviews with researchers and leads about your specific research background and interests.

Phase four - culture interviewTowards the end of the interview process, you will once again connect with the recruitment team to discuss DeepMind’s culture and mission.

5 месяцев, 1 неделя назад @ deepmind.com
Using AI to predict retinal disease progression
Using AI to predict retinal disease progression Using AI to predict retinal disease progression

The ‘dry’ form is relatively common among people over 65, and usually causes only mild sight loss.

Our contribution highlights the potential of using AI in preventative studies for diseases such as exAMD.

The Moorfields Eye Hospital AMD datasetWe used a dataset of anonymised retinal scans from Moorfields patients with exAMD in one eye, and at high-risk of developing exAMD in their other eye.

To address this, we worked with retinal experts to review all scans for each eye and specify the scan when exAMD was first evident.

In our previous work, now continuing in collaboration with Google Health, we developed a model capable of segmenting these eye scans into thirteen anatomical categories.

6 месяцев, 2 недели назад @ deepmind.com
Specification gaming: the flip side of AI ingenuity
Specification gaming: the flip side of AI ingenuity Specification gaming: the flip side of AI ingenuity

Specification gaming is a behaviour that satisfies the literal specification of an objective without achieving the intended outcome.

We have all had experiences with specification gaming, even if not by this name.

In this post, we review possible causes for specification gaming, share examples of where this happens in practice, and argue for further work on principled approaches to overcoming specification problems.

In a Lego stacking task, the desired outcome was for a red block to end up on top of a blue block.

The agent was rewarded for the height of the bottom face of the red block when it is not touching the block.

7 месяцев, 2 недели назад @ deepmind.com
Towards understanding glasses with graph neural networks
Towards understanding glasses with graph neural networks Towards understanding glasses with graph neural networks

The practical implications of modelling glassThe glass transition is a ubiquitous phenomenon which manifests in more than window (silica) glasses.

Understanding the glass transition may result in other applications of disordered materials, in fields as diverse as biorenewable polymers and food processing.

Our new work, published in Nature Physics, could help us gain an understanding of the structural changes that may occur near the glass transition.

Leveraging graph neural networks to model glassy dynamicsGlasses can be modelled as particles interacting via a short-range repulsive potential which essentially prevents particles from getting too close to each other.

We then trained a neural n…

7 месяцев, 4 недели назад @ deepmind.com
Agent57: Outperforming the human Atari benchmark
Agent57: Outperforming the human Atari benchmark Agent57: Outperforming the human Atari benchmark

Combining off-policy learning with memory is challenging because you need to know what you might remember when executing a different behaviour.

Within that strand, we distinguish two types of rewards: firstly, long-term novelty rewards encourage visiting many states throughout training, across many episodes.

Secondly, short-term novelty rewards encourage visiting many states over a short span of time (e.g., within a single episode of a game).

However, learning density models of high dimensional spaces is fraught with problems due to the curse of dimensionality.

For example, in Montezuma’s Revenge, unlike undirected exploration strategies, long-term novelty rewards allow the agent to surpass…

8 месяцев назад @ deepmind.com
A new model and dataset for long-range memory
A new model and dataset for long-range memory A new model and dataset for long-range memory

Modelling natural languageFinding machine learning tasks which both drive the development of better memory architectures and push us further towards artificial general intelligence is challenging.

Transferring knowledgeSuch samples would likely astound Shannon, 70 years on from his early language model experiments.

Google’s prominent natural language model, BERT, achieves state-of-the-art performance on a wide array of NLP benchmarks, and is now a part of Google Search.

Benchmarking language modelsA popular long-range language model benchmark is WikiText-103, which is comprised of English-language Wikipedia articles, and was developed by researchers at Salesforce AI.

As such, we’ve compiled…

9 месяцев, 3 недели назад @ deepmind.com
Google Google
последний пост 6 дней, 5 часов назад
Navigating Recorder Transcripts Easily, with Smart Scrolling
Navigating Recorder Transcripts Easily, with Smart Scrolling Navigating Recorder Transcripts Easily, with Smart Scrolling

), it can still be difficult for users to find specific sections, necessitating a new solution to quickly navigate such long transcripts.

The user can then scroll through the keywords or tap on them to quickly navigate to the sections of interest.

Smart Scrolling feature UXUnder the hoodThe Smart Scrolling feature is composed of two distinct tasks.

We compute the second score by running the section text through the bidirectional transformer model, which was also trained on the sections rating task.

Smart Scrolling provides additional text navigation abilities that will further improve the utility of Recorder, enabling users to rapidly surface sections of interest, even for long recordings.

6 дней, 5 часов назад @ ai.googleblog.com
Find your inner poet with help from America's greats
Find your inner poet with help from America's greats Find your inner poet with help from America's greats

Verse suggestionsVerse by Verse's suggestions are not the original lines of verse the poets had written, but novel verses generated to sound like lines of verse the poets could have written.

We did this by first training our generative models on a large collection of classic poetry, then fine tuning the models on each individual poet’s body of work to try to capture their style of writing.

Additionally, to be able to suggest relevant verses, the system was trained to have a general semantic understanding of what lines of verse would best follow a previous line of verse.

So even if you write on topics not commonly seen in classic poetry, the system will try its best to make suggestions that …

1 неделя назад @ blog.google
The Language Interpretability Tool (LIT): Interactive Exploration and Analysis of NLP Models
The Language Interpretability Tool (LIT): Interactive Exploration and Analysis of NLP Models The Language Interpretability Tool (LIT): Interactive Exploration and Analysis of NLP Models

As natural language processing (NLP) models become more powerful and are deployed in more real-world contexts, understanding their behavior is becoming increasingly critical.

But, despite the recent explosion of work on model understanding and evaluation, there is no “silver bullet” for analysis.

But there was still a need for a toolkit that would address challenges specific to NLP models.

With these challenges in mind, we built and open-sourced the Language Interpretability Tool (LIT), an interactive platform for NLP model understanding.

It can also be used as an easy and fast way to create an interactive demo for any NLP model.

1 неделя, 3 дня назад @ ai.googleblog.com
Beyond COVID-19, retail looks to transform with AI/ML
Beyond COVID-19, retail looks to transform with AI/ML Beyond COVID-19, retail looks to transform with AI/ML

The global retail industry, which has grappled with waves of change over the past decade, is facing one of its most dynamic and unpredictable periods to date.

When I speak with retail executives, some are thriving, some are surviving, and some are struggling.

At Google Cloud, we recently commissioned a survey of global retail executives to better understand which AI/ML use cases across the retail value chain drive the highest value and returns in retail, and what retailers need to keep in mind when going after these opportunities.

While the study has applicability across all of retail, the researchers focused their effort around two specific sub-segments —Food, Drug, and Mass merchants (FDM…

1 неделя, 3 дня назад @ cloud.google.com
How AI, and specifically BERT, helps the patent industry
How AI, and specifically BERT, helps the patent industry How AI, and specifically BERT, helps the patent industry

In recent years the patent industry has begun to use machine-learning (ML) algorithms to add efficiency and insights to business practices.

The white paper is accompanied by a colab notebook as well the trained model hosted in GitHub.

Patent offices interested in leveraging state-of-the-art ML approaches to assist with patent examination and prior art searching.

To learn more, you can download the full white paper, colab notebook, and trained model.

Additionally, see Google Patents Public Datasets: Connecting Public, Paid, and Private Patent Data, Expanding your patent set with ML and BigQuery, and Measuring patent claim breadth using Google Patents Public Datasets for more tutorials to hel…

1 неделя, 3 дня назад @ cloud.google.com
Music from the heart, with an AI assist
Music from the heart, with an AI assist Music from the heart, with an AI assist

The next time you hear a popular song on the radio, listen to the beat behind the lyrics.

That’s what Googler MJ Jacob predicts, as he combines his job as an engineer with his love for writing and performing rap music.

But in his free time, he’s writing lyrics, producing hip-hop tracks and creating YouTube videos detailing how he does it all.

MJ has balanced an interest in technology with a love for hip-hop since he was a 13-year-old living in Virginia.

His family was struggling financially, and he found rappers’ rags-to-riches lyrics to be inspirational.

1 неделя, 3 дня назад @ blog.google
Google Cloud AI digitizes StoryCorps archive: largest collection of human voices on planet
Google Cloud AI digitizes StoryCorps archive: largest collection of human voices on planet Google Cloud AI digitizes StoryCorps archive: largest collection of human voices on planet

In this spirit, we are sharing our collaboration with StoryCorps, a national non-profit organization dedicated to preserving humanity’s stories through 1:1 interviews.

That’s when StoryCorps approached us to help make its rich archive of first-person history universally accessible and useful.

StoryCorps + Google Cloud AIIn 2019, StoryCorps and Google Cloud partnered to unlock this amazing archive using artificial intelligence (AI) and create an open, searchable and accessible audio database for everyone to find and listen to first-hand perspectives from humanity’s most important moments.

Resulting in a searchable transcript on the StoryCorps Archive.

Here is an example of how these Cloud AI…

1 неделя, 5 дней назад @ cloud.google.com
Haptics with Input: Using Linear Resonant Actuators for Sensing
Haptics with Input: Using Linear Resonant Actuators for Sensing Haptics with Input: Using Linear Resonant Actuators for Sensing

We achieve this with off-the-shelf LRAs by multiplexing the actuation with short pulses of custom waveforms that are designed to enable sensing using the back-EMF voltage.

Our technique is potentially compatible with many existing LRA drivers, as they already employ back-EMF sensing for autotuning of the vibration frequency.

A greater oscillation speed creates a larger back-EMF voltage, while a stationary mass generates zero back-EMF voltage.

By driving the LRA with small amounts of energy, we can measure this phenomenon using the back-EMF voltage.

Sensing phone surroundings.

1 неделя, 5 дней назад @ ai.googleblog.com
Introducing Google News Initiative Conversations
Introducing Google News Initiative Conversations Introducing Google News Initiative Conversations

We’ve gone from lunch meetings and large networking conferences to meeting virtually from our makeshift home offices.

The COVID-19 pandemic has certainly upended a lot of this, but that doesn’t mean sharing ideas is on hold, too.

That’s especially true for the Google News Initiative team; our commitment to helping journalism thrive is still just as strong.

That’s why we’ve launched Google News Initiative Conversations, a new video series in which we bring together industry experts and our partners from around the world to discuss the successes, challenges and opportunities facing the news industry.

Since March 2018, the GNI has worked with more than 6,250 news partners in 118 countries, sev…

1 неделя, 5 дней назад @ blog.google
Rachel Malarich is planting a better future, tree by tree
Rachel Malarich is planting a better future, tree by tree Rachel Malarich is planting a better future, tree by tree

Everyone has a tree story, Rachel Malarich says—and one of hers takes place on the limbs of a eucalyptus tree.

This goal is about more than planting trees, though: It’s about planting the seeds for social, economic and environmental equity.

These trees, Rachel says, will help advance citywide sustainability and climate goals, beautify neighborhoods, improve air quality and create shade to combat rising street-level temperatures.

Tree inventory data, which is typically collected through on-site assessments, helps city officials know where to invest resources for maintaining, preserving and planting trees.

And it also helps Rachel do what she has focused her career on: creating community-led …

1 неделя, 5 дней назад @ blog.google
How ZSL uses ML to classify gunshots to protect wildlife
How ZSL uses ML to classify gunshots to protect wildlife How ZSL uses ML to classify gunshots to protect wildlife

International conservation charity, ZSL (Zoological Society of London) has made another leap forward in its battle to protect animals using AI and machine learning (ML) from Google Cloud. We’ve been privileged to partner with ZSL for three years, co-developing custom ML models to identify and better track endangered species around the world. The next dataset in ZSL’s arsenal to tackle animal conservation is sound—specifically gunshots captured by recording devices.WWF estimates the illegal wildlife trade is worth about $20bn a year and has contributed to a catastrophic decline in some species. Technology, particularly machine learning, is at the forefront of conservation efforts, but standi…

1 неделя, 5 дней назад @ cloud.google.com
Using GANs to Create Fantastical Creatures
Using GANs to Create Fantastical Creatures Using GANs to Create Fantastical Creatures

In this example, an artist (Lee Dotson) customizes one of the creature designs that comes pre-loaded in the Chimera Painter demo.

Since our goal was to create high-quality creature card images guided by artist input, we experimented with generative adversarial networks (GANs), informed by artist feedback, to create creature images that would be appropriate for our fantasy card game prototype.

Below we show some sample assets generated using the model, including single-species creatures, as well as the more complex multi-species chimeras.

We discovered that these weights were critically important in determining what a final generated image would look like.

Creatures generated using different…

1 неделя, 6 дней назад @ ai.googleblog.com
Google Cloud, Harvard Global Public Health release improved COVID-19 Public Forecasts, share lessons learned
Google Cloud, Harvard Global Public Health release improved COVID-19 Public Forecasts, share lessons learned Google Cloud, Harvard Global Public Health release improved COVID-19 Public Forecasts, share lessons learned

“The COVID-19 Public Forecasts is an important public health tool for guiding the policy response to the COVID-19 pandemic.

We have added support for expanding the COVID-19 Public Forecasts to other countries, and today we are launching forecasts for Japan.

As with the United States, these forecasts are free and based on public data such as the public COVID-19 Situation Report in Japan.

The following sections share some background about our journey leading up to the original launch of the COVID-19 Public Forecasts.

If you have any questions about the COVID-19 Public Forecasts (g.co/covidforecast), customizations or what-if analysis, please contact us at COVID19-public-forecasts-feedback@goo…

2 недели назад @ cloud.google.com
Mitigating Unfair Bias in ML Models with the MinDiff Framework
Mitigating Unfair Bias in ML Models with the MinDiff Framework Mitigating Unfair Bias in ML Models with the MinDiff Framework

While these classifiers serve vital functions, it is also essential that they are built in ways that minimize unfair biases for users.

Today, we are announcing the release of MinDiff, a new regularization technique available in the TF Model Remediation library for effectively and efficiently mitigating unfair biases when training ML models.

When the end goal is to improve products, it’s important to be able to scale unfair bias mitigation to many models.

Quality: The method for removing unfair biases should also reduce the overall classification performance (e.g., accuracy) as little as possible.

Gaps in error rates of classifiers is an important set of unfair biases to address, but not the…

2 недели назад @ ai.googleblog.com
The democratization of data and insights: Expanding Machine Learning Access
The democratization of data and insights: Expanding Machine Learning Access The democratization of data and insights: Expanding Machine Learning Access

In the first blog in this series, we discussed how data availability, data access, and insight access have evolved over time, and what Google Cloud is doing today to help customers democratize the production of insights across organizational personas. In this blog we’ll discuss why artificial intelligence (AI) and machine learning (ML) are critical to generating insights in today’s world of big data, as well as what Google Cloud is doing to expand access to this powerful method of analysis.A report by McKinsey highlights the stakes at play: by 2030, companies that fully absorb AI could double their cash flow, while companies that don’t could see a 20% decline. ML and AI have traditionally b…

2 недели назад @ cloud.google.com
OpenAI OpenAI
последний пост 2 месяца, 1 неделя назад
OpenAI Licenses GPT-3 Technology to Microsoft
OpenAI Licenses GPT-3 Technology to Microsoft OpenAI Licenses GPT-3 Technology to Microsoft

OpenAI released its first commercial product back in June: an API for developers to access advanced technologies for building new applications and services.

The API features a powerful general purpose language model, GPT-3, and has received tens of thousands of applications to date.

In addition to offering GPT-3 and future models via the OpenAI API, and as part of a multiyear partnership announced last year, OpenAI has agreed to license GPT-3 to Microsoft for their own products and services.

GPT-3 is the most powerful model behind the API today, with 175 billion parameters.

Today, the API remains in a limited beta as OpenAI and academic partners test and assess the capabilities and limitati…

2 месяца, 1 неделя назад @ openai.com
Learning to Summarize with Human Feedback
Learning to Summarize with Human Feedback Learning to Summarize with Human Feedback

We've applied reinforcement learning from human feedback to train language models that are better at summarization.

Our approach follows directly from our previous work on learning from human feedback.

In particular, our 1.3 billion parameter (1.3B) model trained with human feedback outperforms our 12B model trained only with supervised learning.

Note that our human feedback models generate summaries that are significantly shorter than summaries from models trained on CNN/DM.

This suggests that our human feedback models have learned something more general about how to summarize text, and are not specific to Reddit posts.

2 месяца, 3 недели назад @ openai.com
OpenAI Scholars Spring 2020: Final Projects
OpenAI Scholars Spring 2020: Final Projects OpenAI Scholars Spring 2020: Final Projects

Our third class of OpenAI Scholars presented their final projects at virtual Demo Day, showcasing their research results from over the past five months.

The OpenAI Scholars program provides stipends and mentorship to individuals from underrepresented groups to study deep learning and open-source a project.

Demo Day introductions by Sam Altman and Greg BrockmanLearn more about our Scholars program.

I joined the Scholars program in order to learn from the brilliant folks at OpenAI and to immerse myself in AI research.

The OpenAI Scholars program was this magical opportunity to get started by learning from the very best minds in the field.

4 месяца, 3 недели назад @ openai.com
Image GPT
Image GPT Image GPT

However, the same broad class of models has not been successful in producing strong features for image classification.

From language GPT to image GPTIn language, unsupervised learning algorithms that rely on word prediction (like GPT-2 and BERT) have been extremely successful, achieving top performance on a wide array of language tasks.

Because masked language models like BERT have outperformed generative models on most language tasks, we also evaluate the performance of BERT on our image models.

LimitationsWhile we have shown that iGPT is capable of learning powerful image features, there are still significant limitations to our approach.

Notably, we achieved our results by directly applyi…

5 месяцев, 2 недели назад @ openai.com
OpenAI API
OpenAI API OpenAI API

We’re releasing an API for accessing new AI models developed by OpenAI.

Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose “text in, text out” interface, allowing users to try it on virtually any English language task.

Your browser does not support videoGiven any text prompt, the API will return a text completion, attempting to match the pattern you gave it.

We've designed the API to be both simple for anyone to use but also flexible enough to make machine learning teams more productive.

Today the API runs models with weights from the GPT-3 family with many speed and throughput improvements.

5 месяцев, 3 недели назад @ openai.com
Procgen and MineRL Competitions
Procgen and MineRL Competitions Procgen and MineRL Competitions

We’re excited to announce that OpenAI is co-organizing two NeurIPS 2020 competitions with AIcrowd, Carnegie Mellon University, and DeepMind, using Procgen Benchmark and MineRL.

Procgen CompetitionSign up for ProcgenThe Procgen Competition focuses on improving sample efficiency and generalization in reinforcement learning.

Since all content is procedurally generated, each Procgen environment intrinsically requires agents to generalize to never-before-seen situations.

Moreover, we designed Procgen environments to be fast and simple to use.

One well-known way to reduce the environment sample complexity is to leverage human priors and demonstrations of the desired behavior.

5 месяцев, 3 недели назад @ openai.com
AI and Efficiency
AI and Efficiency AI and Efficiency

Other measures of AI progressIn addition to efficiency, many other measures shed light on overall algorithmic progress in AI.

Shufflenet achieved AlexNet-level performance with an 18x inference efficiency increase in 5 years (15-month doubling time), which suggests that training efficiency and inference efficiency might improve at similar rates.

This efficiency analysis suggests that policymakers could develop accurate intuitions about the cost of deploying AI capabilities—and how these costs are going to alter over time—by more closely assessing the rate of improvements in efficiency for AI systems.

Our results suggest that for AI tasks with high levels of investment (researcher time and/o…

6 месяцев, 4 недели назад @ openai.com
Jukebox
Jukebox Jukebox

Curated samples Provided with genre, artist, and lyrics as input, Jukebox outputs a new music sample produced from scratch.

We can then train a model to generate audio in this compressed space, and upsample back to the raw audio space.

Now in raw audio, our models must learn to tackle high diversity as well as very long range structure, and the raw audio domain is particularly unforgiving of errors in short, medium, or long term timing.

To better understand future implications for the music community, we shared Jukebox with an initial set of 10 musicians from various genres to discuss their feedback on this work.

While Jukebox is an interesting research result, these musicians did not find …

7 месяцев назад @ openai.com
Improving Verifiability in AI Development
Improving Verifiability
in AI Development Improving Verifiability in AI Development

Can I (as an academic) conduct impartial research on the risks associated with large-scale AI systems when I lack the computing resources of industry?

Can I (as an AI developer) verify that my competitors in a given area of AI development will follow best practices rather than cut corners to gain an advantage?

AI developers should pilot bias and safety bounties for AI systems to strengthen incentives and processes for broad-based scrutiny of AI systems.

Standard setting bodies should work with academia and industry to develop audit trail requirements for safety-critical applications of AI systems.

Organizations developing AI and funding bodies should support research into the interpretabili…

7 месяцев, 2 недели назад @ openai.com
OpenAI Microscope
OpenAI Microscope OpenAI Microscope

We’re introducing OpenAI Microscope, a collection of visualizations of every significant layer and neuron of eight vision “model organisms” which are often studied in interpretability.

Microscope makes it easier to analyze the features that form inside these neural networks, and we hope it will help the research community as we move towards understanding these complicated systems.

This is the goal of the OpenAI Microscope.

Microscope systematically visualizes every neuron in several commonly studied vision models, and makes all of those neurons linkable.

Our initial release includes nine frequently studied vision models, along with several visualization techniques we’ve found particularly u…

7 месяцев, 2 недели назад @ openai.com
Microsoft Microsoft
последний пост 5 часов назад
The human side of AI for chess
The human side of AI for chess The human side of AI for chess

Chess stands as a model system for studying how people can collaborate with AI, or learn from AI, just as chess has served as a leading indicator of many central questions in AI throughout the field’s history.

There’s a lot of work out there that attempts to match AI chess play to varying human skill levels, but the result is often AI that makes decisions and plays moves differently than human players at that skill level.

Our work comprises two papers, “Aligning Superhuman AI with Human Behavior: Chess as a Model System” and “Learning Personalized Behaviors of Human Behavior in Chess,” as well as a novel chess engine, called Maia, which is trained on games played by humans to more closely m…

5 часов назад @ microsoft.com
Project InnerEye evaluation shows how AI can augment and accelerate clinicians’ ability to perform radiotherapy planning 13 times faster
Project InnerEye evaluation shows how AI can augment and accelerate clinicians’ ability  to perform radiotherapy planning 13 times faster Project InnerEye evaluation shows how AI can augment and accelerate clinicians’ ability to perform radiotherapy planning 13 times faster

Planning radiotherapy treatment can be a lengthy process.

The image segmentation model is a state-of-the-art convolutional neural network based on a 3D U-Net architecture, with approximately 39 million trainable parameters.

Our results show that the ML model greatly reduces the time it takes for end-to-end image segmentation and annotation in radiotherapy.

The time taken for the ML model to perform inference was only 23 ± 3 seconds in a full input CT scan.

Figure 4: Integration of the proposed segmentation models into radiotherapy planning workflow.

7 часов назад @ microsoft.com
Beeb, how do you build the world’s first public service voice assistant?
Beeb, how do you build the world’s first public service voice assistant?

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5 дней, 7 часов назад @ blogs.microsoft.com
The future of work, unbound: 2020 and the strange new mobility of space and time
The future of work, unbound: 2020 and the strange new mobility of space and time The future of work, unbound: 2020 and the strange new mobility of space and time

We work in the same physical spaces, but as we navigate these transitions, we’re not in the same human places.

Through this lens, we can view the essence of mobility as the transition of user activity from one place to another.

The Fleet system unbinds UI elements from not only the device but also the current application, user, and time.

Portfolios unbind tools, inputs, behaviors, and content from the current device and user.

These technologies work to build and implement applications that not only go beyond the current device, but also unbind other dimensions of mobility—the current user, the current application, the current time—as well.

1 неделя, 4 дня назад @ microsoft.com
RESTler finds security and reliability bugs through automated fuzzing
RESTler finds security and reliability bugs through automated fuzzing RESTler finds security and reliability bugs through automated fuzzing

Given an OpenAPI/Swagger specification of a cloud/web service REST API, RESTler automatically generates and executes tests that exercise the service through its REST API—no prerecorded REST API traffic or preexisting tests are needed.

In addition to RESTler, Microsoft Research has created a self-hosted REST API fuzzing service, a platform where developers can integrate continuous testing into their builds.

It can host a developer-definable set of REST API fuzzing tools, with default support for RESTler and OWASP’s (Open Web Application Security Project) ZAP.

This lightweight platform brings a developer-first approach to incorporating REST API fuzzing into the service development workflow.

R…

2 недели назад @ microsoft.com
Research Collection – Re-Inventing Storage for the Cloud Era
Research Collection – Re-Inventing Storage for the Cloud Era Research Collection – Re-Inventing Storage for the Cloud Era

In this clip from the Microsoft Research Podcast, Ant Rowstron discusses how various storage media are reaching their useful limits in the datacenter.

At Microsoft Research Cambridge, researchers began exploring optimizing enterprise storage using off-the-shelf hardware in the late 2000s.

Explore moreHolographic StorageHolographic storage research testbed at Microsoft Research CambridgeHolographic storage was first proposed back in the 1960s, shortly after the invention of the laser.

Researchers in physics, optics, machine learning and storage systems are working together to design mechanical movement free, high-endurance cloud storage that is both performant and cost-effective.

The DNA Sto…

3 недели назад @ microsoft.com
Enabling interaction between mixed reality and robots via cloud-based localization
Enabling interaction between mixed reality and robots via cloud-based localization Enabling interaction between mixed reality and robots via cloud-based localization

Mixed reality devices such as Microsoft HoloLens and mixed reality–capable mobile devices are able to build visual maps of their environments and recognize their place in them.

With the Azure Spatial Anchors Linux SDK, robots can now use Azure Spatial Anchors to localize and share information within this mixed reality ecosystem.

Enabling robots to colocalize with different types of devices, especially mixed reality devices and mixed reality–capable devices, opens up new opportunities for research and innovation in human-robot interaction.

We envision mixed reality as an important tool for robot spatial intelligence and autonomy, and our ambition is to unite humans and robots through mixed r…

1 месяц назад @ microsoft.com
Microsoft releases preview of Lobe training app for machine-learning
Microsoft releases preview of Lobe training app for machine-learning Microsoft releases preview of Lobe training app for machine-learning

In 2018, Microsoft bought Lobe, a San Francisco-based startup that made a platform for building, training and shipping custom deep-learning models.

On October 26, available a public preview of a Lobe app for training machine-learning models.

Available for both Windows and Mac, the Lobe app is free and designed to enable people with no data science experience to import images into the app and label them to create a machine learning dataset.

According to Microsoft, "Lobe automatically selects the right machine learning architecture and starts training without any setup or configuration."

Officials said the app, which trains and stores data locally, complements Microsoft's various Azure AI ser…

1 месяц назад @ zdnet.com
From beekeepers to ocean mappers, Lobe aims to make it easy for anyone to train machine learning models
From beekeepers to ocean mappers, Lobe aims to make it easy for anyone to train machine learning models From beekeepers to ocean mappers, Lobe aims to make it easy for anyone to train machine learning models

Lobe automatically selects the right machine learning architecture and starts training without any setup or configuration.

It fills them with confidence that they can actually use machine learning.

And when you have confidence you become more creative and start looking around and asking ‘What other stuff can I do with this?’”Lobe, which is available for download on Windows or Mac computers, uses open-source machine learning architectures and transfer learning to train custom machine learning models on the user’s own machine.

“We really want to empower more people to leverage machine learning and try it for the first time,” said Jake Cohen, Lobe senior program manager.

Cachor said he’d thoug…

1 месяц назад @ blogs.microsoft.com
Microsoft and MITRE release framework to help fend off adversarial AI attacks
Microsoft and MITRE release framework to help fend off adversarial AI attacks Microsoft and MITRE release framework to help fend off adversarial AI attacks

According to a Gartner report, through 2022, 30% of all AI cyberattacks will leverage training-data poisoning, model theft, or adversarial samples to attack machine learning-powered systems.

Despite these reasons to secure systems, Microsoft claims its internal studies find most industry practitioners have yet to come to terms with adversarial machine learning.

Twenty-five out of the 28 businesses responding to the Seattle company’s recent survey indicated they don’t have the right tools in place to secure their machine learning models.

“The Adversarial Machine Learning Threat Matrix will … help security analysts think holistically.

“We think that securing machine learning systems is an inf…

1 месяц, 1 неделя назад @ venturebeat.com
Quickly get started with samples in Azure Synapse Analytics
Quickly get started with samples in Azure Synapse Analytics

To further accelerate time to insight in Azure Synapse Analytics, we are introducing the Knowledge center to simplify access to pre-loaded sample data and to streamline the getting started process for data professionals.

1 месяц, 1 неделя назад @ azure.microsoft.com
Microsoft researchers develop assistive eye-tracking AI that works on any device
Microsoft researchers develop assistive eye-tracking AI that works on any device Microsoft researchers develop assistive eye-tracking AI that works on any device

But estimating a person’s gaze isn’t a trivial task owing to variables including head pose, head position, eye rotation, distance, illumination, background noise, and the presence of glasses, face coverings, and assistive medical equipment.

This challenge inspired a team of researchers at Microsoft to develop an ultra-precise, hardware-agnostic gaze tracker that works with any off-the-shelf webcam.

In a previous study, researchers at the company experimented with multiple infrared lights around a display for eye-tracking, as well as with a camera and depth sensors.

The MIT team behind GazeCapture designed iTracker, an AI model that performs gaze tracking on Apple devices using built-in came…

1 месяц, 1 неделя назад @ venturebeat.com
A holistic representation toward integrative AI
A holistic representation toward integrative AI A holistic representation toward integrative AI

At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding.

I believe the joint XYZ-code is a foundational component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks.

We can derive more powerful representations by intersecting X, Y, and Z.X-code: Text representation from big dataThe quest to achieve universal representation of monolingual text is our X-code.

Similarly, our work with XYZ-code breaks down AI capabilities into smaller building blocks that can be combined in unique ways to make integrative AI more effective.

Just as Gutenberg’s printing …

1 месяц, 1 неделя назад @ microsoft.com
Physics matters: Haptic PIVOT, an on-demand controller, simulates physical forces such as momentum and gravity
Physics matters: Haptic PIVOT, an on-demand controller, simulates physical forces such as momentum and gravity Physics matters: Haptic PIVOT, an on-demand controller, simulates physical forces such as momentum and gravity

Now, with Haptic PIVOT, we bring the physics of forces to VR controllers.

This week, we’re presenting Haptic PIVOT at the 2020 ACM Symposium on User Interface Software and Technology (UIST).

Haptic PIVOT serves on-demand control and haptic rendering of virtual objects as the hand reaches for them.

From the physical to the virtual—on demandAt the core of PIVOT’s design is its hinge mechanism and haptic handle.

The reaction time of catching a flying virtual object is significantly shorter than grabbing a stationary virtual object (we can simulate the catch of a 55.9-mph throw through visuo-motor illusions!).

1 месяц, 1 неделя назад @ microsoft.com
Microsoft Turing Universal Language Representation model, T-ULRv2, tops XTREME leaderboard
Microsoft Turing Universal Language Representation model, T-ULRv2, tops XTREME leaderboard Microsoft Turing Universal Language Representation model, T-ULRv2, tops XTREME leaderboard

Today, we are happy to announce that Turing multilingual language model (T-ULRv2) is the state of the art at the top of the Google XTREME public leaderboard.

Created by the Microsoft Turing team in collaboration with Microsoft Research, the model beat the previous best from Alibaba (VECO) by 3.5 points in average score.

Universal Language RepresentationPUBLICATION Towards Language Agnostic Universal RepresentationsThe Microsoft Turing team has long believed that language representation should be universal.

The Turing Universal Language Representation (T-ULRv2) model is our latest cross-lingual innovation, which incorporates our recent innovation of InfoXLM, to create a universal model that …

1 месяц, 1 неделя назад @ microsoft.com
Facebook Facebook
последний пост 1 месяц, 1 неделя назад
PPL Bench: Creating a standard for benchmarking probabilistic programming languages
PPL Bench: Creating a standard for benchmarking probabilistic programming languages PPL Bench: Creating a standard for benchmarking probabilistic programming languages

What’s New:PPL Bench is an open source benchmark framework for evaluating probabilistic programming languages (PPLs) used for statistical modeling.

PPL Bench does this by using predictive log likelihood as a standard measurement.

PPL Bench also reports other common metrics used to evaluate statistical models, including effective sample size, R-hat, and inference time.

We hope that community contributions will help grow and diversify PPL Bench and encourage wider industrial deployments of PPLs.

Read the full paper:PPL Bench: Evaluation framework for probabilistic programming languagesGet it on GitHub:PPL Bench

1 месяц, 1 неделя назад @ ai.facebook.com
Mark Harman elected Fellow of the Royal Academy of Engineering
Mark Harman elected Fellow of the Royal Academy of Engineering Mark Harman elected Fellow of the Royal Academy of Engineering

The U.K.’s Royal Academy of Engineering has elected Facebook Research Scientist Mark Harman as a Fellow for his achievements in academia and industry, including his work on search-based software engineering (SBSE), intelligent software testing tools, and web-enabled simulation (WES) approaches.

SBSE is an approach that uses search-based optimization algorithms to find solutions to highly complex software engineering problems.

Using the technique allows for smoother testing, design, and project management in software engineering.

For the next 25 years, he worked solely in academia, where he wrote, edited, and reviewed hundreds of papers, and authored books about software testing and programm…

2 месяца, 1 неделя назад @ engineering.fb.com
Scalable data classification for security and privacy
Scalable data classification for security and privacy Scalable data classification for security and privacy

What the research is:We’ve built a data classification system that uses multiple data signals, a scalable system architecture, and machine learning to detect semantic types within Facebook at scale.

This is important in situations where it’s necessary to detect where an organization’s data is stored in many different formats across various data stores.

In these cases, a classification system enables organizations to automatically enforce privacy- and security-related policies, such as access control policies.

Why it matters:Organizations generally have a well-defined set of privacy policies aimed at ensuring that people’s privacy is respected.

Read the full paper:Secure and scalable data cl…

4 месяца, 1 неделя назад @ engineering.fb.com
MIT AI MIT AI
последний пост 6 дней, 4 часа назад
How humans use objects in novel ways to solve problems
How humans use objects in novel ways to solve problems How humans use objects in novel ways to solve problems

When our table is shaky, we quickly find that we can put a stack of paper under the table leg to stabilize it.

But while these actions seem so natural to us, they are believed to be a hallmark of great intelligence — only a few other species use objects in novel ways to solve their problems, and none can do so as flexibly as people.

Solving the puzzles in this game requires reasoning about a number of physical principles, including launching, blocking, or supporting objects.

They built a model that instantiated these principles, called the “Sample, Simulate, Update,” or “SSUP,” model, and had it play the same game as people.

They found that SSUP solved each puzzle at similar rates and in si…

6 дней, 4 часа назад @ news.mit.edu
An antidote to “fast fashion”
An antidote to “fast fashion” An antidote to “fast fashion”

In today’s world of fast fashion, retailers sell only a fraction of their inventory, and consumers keep their clothes for about half as long as they did 15 years ago.

According to Singh, Armoire has grown 300 to 500 percent a year since its founding in 2016.

In fact, when Singh started Armoire, classmates used it as a case study for marketing and analytics research projects.

Singh credits Armoire’s leadership team with creating a welcoming work environment, noting there’s been very little turnover in Armoire’s warehouses.

“We don’t get 95 percent of our inventory rented because I’m so good at picking out clothes,” Singh says.

6 дней, 19 часов назад @ news.mit.edu
Lincoln Laboratory establishes Biotechnology and Human Systems Division
Lincoln Laboratory establishes Biotechnology and Human Systems Division Lincoln Laboratory establishes Biotechnology and Human Systems Division

MIT Lincoln Laboratory has established a new research and development division, the Biotechnology and Human Systems Division.

“We strongly believe that research and development in biology, biomedical systems, biological defense, and human systems is a critically important part of national and global security.

The new division will focus on improving human conditions on many fronts," says Eric Evans, Lincoln Laboratory director.

The new division unifies four research groups: Humanitarian Assistance and Disaster Relief (HADR) Systems, Counter-Weapons of Mass Destruction Systems, Biological and Chemical Technologies, and Human Health and Performance Systems.

Now, that strategic planning and in…

1 неделя назад @ news.mit.edu
3 Questions: Christine Walley on the evolving perception of robots in the US
3 Questions: Christine Walley on the evolving perception of robots in the US 3 Questions: Christine Walley on the evolving perception of robots in the US

Q: How are robots seen as a symbol when we think about the changing nature of work in the United States?

For an anthropologist, however, the point is not that people’s concerns are “irrational,” but that robots have become symbolic encapsulations of much broader anxieties about the changing nature of work in the United States.

My own research uses both history and ethnography to study former industrial communities in the United States.

A: Not everyone in the world is as afraid of job displacement by robots or automation as workers are in the United States.

It’s not surprising that we’re seeing declining social mobility rates in the United States in comparison to many other wealthy countries.

1 неделя назад @ news.mit.edu
Why we shouldn’t fear the future of work
Why we shouldn’t fear the future of work Why we shouldn’t fear the future of work

A lot of labor has become insecure, low-income freelance work.

Yet there is reason for optimism on behalf of workers, as scholars and business leaders outlined in an MIT conference on Wednesday.

That was the outlook of many participants at the conference, the “AI and the Work of the Future Congress,” marking the release of the final report of MIT’s Task Force on the Work of the Future.

“In the jobs of the future, not all robots are going to be serving you coffee,” said Mills.

“I really come away from this concerned about the direction [of work], but optimistic about our ability to change it,” Autor said.

1 неделя, 3 дня назад @ news.mit.edu
MIT forum examines the rise of automation in the workplace
MIT forum examines the rise of automation in the workplace MIT forum examines the rise of automation in the workplace

With the exception of some middle-skilled manufacturing jobs, automation has generally improved human productivity, not eliminated the need for it.

Narrow AIRus emphasized that current industrial applications of artificial intelligence are relatively narrow.

While the rise of artificial intelligence in industry remains gradual, multiple speakers noted how other technologies have rocketed to widespread adoption due to the Covid-19 pandemic.

“You could go a very aggressive path and say ‘the robot finally could replace human workers,’” said Denner.

Jeanne Magoulick, engineering manager for Ford Motor Company, said her team is developing artificial intelligence for predictive maintenance of mac…

1 неделя, 3 дня назад @ news.mit.edu
A neural network learns when it should not be trusted
A neural network learns when it should not be trusted A neural network learns when it should not be trusted

Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as in autonomous driving or medical diagnosis.

But previous approaches, stemming from Bayesian deep learning, have relied on running, or sampling, a neural network many times over to understand its confidence.

The researchers devised a way to estimate uncertainty from only a single run of the neural network.

This distinction can signal whether uncertainty can be reduced by tweaking the neural network itself, or whether the input data are just noisy.

They trained their neural network to analyze a monocular color image and estimate a depth va…

1 неделя, 3 дня назад @ news.mit.edu
Vibrations of coronavirus proteins may play a role in infection
Vibrations of coronavirus proteins may play a role in infection Vibrations of coronavirus proteins may play a role in infection

Using atomistic simulations, they looked at the mechanical aspects of how the spike proteins move, change shape, and vibrate.

The team found a strong direct relationship between the rate and intensity of the spikes’ vibrations and how readily the virus could penetrate the cell.

The researchers applied this technique to look at a crucial step in infection, when a virus particle with its protein spikes attaches to a human cell receptor called the ACE2 receptor.

That binding mechanism between the proteins and the receptors works something like a lock and key, and that’s why the vibrations matter, according to Buehler.

But the protein spikes are not static; “they’re vibrating and continuously c…

1 неделя, 4 дня назад @ news.mit.edu
Phiala Shanahan receives Kenneth G. Wilson Award for work in lattice field theory
Phiala Shanahan receives Kenneth G. Wilson Award for work in lattice field theory Phiala Shanahan receives Kenneth G. Wilson Award for work in lattice field theory

Class of 1957 Career Development Assistant Professor of Physics Phiala Shanahan will receive the 2020 Kenneth G. Wilson Award for Excellence in Lattice Field Theory.

The award, given by the international lattice field theory community, recognizes her research of hadrons and nuclei using the tools of lattice Quantum Chromodynamics, or lattice QCD, and her pioneering application of machine learning and artificial intelligence techniques to lattice field theory.

In recent work she has used supercomputers to reveal the role of gluons, the force carriers of the strong interactions described by QCD, in hadron and nuclear structure.

Since its inception in 2011, the annual Kenneth G. Wilson Award f…

1 неделя, 5 дней назад @ news.mit.edu
Report outlines route toward better jobs, wider prosperity
Report outlines route toward better jobs, wider prosperity Report outlines route toward better jobs, wider prosperity

That’s the conclusion of the final report from MIT’s Task Force on the Work of the Future, which summarizes over two years of research on technology and jobs.

As technology takes jobs away, it provides new opportunities; about 63 percent of jobs performed in 2018 did not exist in 1940.

Six big conclusionsThe task force, an Institute-wide group of scholars and researchers, spent over two years studying work and technology in depth.

The task force report surveys technology adoption in industries including insurance, health care, manufacturing, and autonomous vehicles, finding growth in “narrow” AI systems that complement workers.

The polarizing effects of technology on jobs would be lessened …

1 неделя, 6 дней назад @ news.mit.edu
Understanding how people make sense of information in the information age
Understanding how people make sense of information in the information age Understanding how people make sense of information in the information age

The topic area is familiar for Revel; growing up, she wanted to be a journalist to follow in her father's footsteps.

Current events had underscored the importance of understanding how information is, for better and worse, disseminated in the digital era and made sense of by the people receiving it.

Revel’s main project, together with a fellow graduate student in LIDS, Amir Tohidi, has been investigating the effect of “clickbait” ads on reader trust.

She has been working with sociologists on a project about how audiences’ perception of the news resonates with media coverage.

She sees the goal of her work as understanding, rather than judging, how people think and behave.

2 недели назад @ news.mit.edu
System brings deep learning to “internet of things” devices
System brings deep learning to “internet of things” devices System brings deep learning to “internet of things” devices

Deep learning is everywhere.

Soon, deep learning could also check your vitals or set your thermostat.

MIT researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the “internet of things” (IoT).

The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power.

So pattern-recognition tasks like deep learning are difficult to run locally on IoT devices.

2 недели, 3 дня назад @ news.mit.edu
Staying ahead of the artificial intelligence curve with help from MIT
Staying ahead of the artificial intelligence curve with help from MIT Staying ahead of the artificial intelligence curve with help from MIT

Doing so required that he stay up to date on the latest machine learning knowledge and techniques.

“When I discovered that I could take courses at MIT, I thought, ‘What better place to learn about artificial intelligence and machine learning?’” he says.

“We had a problem we wanted to solve and knew that artificial intelligence and machine learning could possibly address it.

“For professionals like me who work in information technology, innovation and artificial intelligence go hand-in-hand,” Zagni says.

While completing machine learning courses at MIT, Zagni simultaneously enrolled in MIT Professional Education’s Professional Certificate Program in Innovation and Technology.

3 недели назад @ news.mit.edu
Algorithm reduces use of riskier antibiotics for UTIs
Algorithm reduces use of riskier antibiotics for UTIs Algorithm reduces use of riskier antibiotics for UTIs

Doctors often treat UTIs using antibiotics called fluoroquinolones that are inexpensive and generally effective.

In a new paper, the researchers present a recommendation algorithm that predicts the probability that a patient’s UTI can be treated by first- or second-line antibiotics.

The team showed that the model would allow clinicians to reduce the use of second-line antibiotics 67 percent.

For patients where clinicians chose a second-line drug but the algorithm chose a first-line drug, the first-line drug ended up working more than 90 percent of the time.

When clinicians chose an inappropriate first-line drug, the algorithm chose an appropriate first-line drug almost half of the time.

3 недели, 4 дня назад @ news.mit.edu
Using machine learning to track the pandemic’s impact on mental health
Using machine learning to track the pandemic’s impact on mental health Using machine learning to track the pandemic’s impact on mental health

Dealing with a global pandemic has taken a toll on the mental health of millions of people.

Their analysis revealed several key changes in conversations about mental health, including an overall increase in discussion about anxiety and suicide.

They also discovered that the mental health groups affected the most negatively early in the pandemic were those related to ADHD and eating disorders.

The researchers also found the introduction of new topics specifically seeking mental health help or social interaction.

Mental health resourcesThis type of analysis could help mental health care providers identify segments of the population that are most vulnerable to declines in mental health caused …

3 недели, 4 дня назад @ news.mit.edu
Berkeley AI
последний пост 1 неделя, 3 дня назад
Learning State Abstractions for Long-Horizon Planning
Learning State Abstractions for Long-Horizon Planning Learning State Abstractions for Long-Horizon Planning

Learning State Abstractions for Long-Horizon PlanningMany tasks that we do on a regular basis, such as navigating a city, cooking a meal, or loading a dishwasher, require planning over extended periods of time.

Two-way consistency can be viewed as a generalization of value irrelevance to the goal-conditioned setting.

Furthermore, our main theorem tells us that we can merge nodes according to two-way consistency while preserving the graph’s quality.

Overall, we found that state aggregation with two-way consistency resulted in substantially more robust plans over the prior state-of-the-art.

How can two-way consistency be utilized beyond the scope of graphical-based planning methods?

1 неделя, 3 дня назад @ bair.berkeley.edu
EvolveGraph: Dynamic Neural Relational Reasoning for Interacting Systems
EvolveGraph: Dynamic Neural Relational Reasoning for Interacting Systems EvolveGraph: Dynamic Neural Relational Reasoning for Interacting Systems

EvolveGraph: Dynamic Neural Relational Reasoning for Interacting SystemsMulti-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems.

In this work, we took a step forward to handle these challenges and provided a generic framework for trajectory prediction with dynamic relational reasoning for multi-agent systems.

Dynamic Interaction Graph LearningIn many situations, the interaction patterns recognized from the past time steps are likely not static in the future.

The model is expected to learn the criterion by itself and perform both edge type prediction and trajectory prediction.

Summary and Broader ApplicationsWe introduce …

1 неделя, 5 дней назад @ bair.berkeley.edu
EvolveGraph: Dynamic Neural Relational Reasoning for Interacting Systems
EvolveGraph: Dynamic Neural Relational Reasoning for Interacting Systems EvolveGraph: Dynamic Neural Relational Reasoning for Interacting Systems

EvolveGraph: Dynamic Neural Relational Reasoning for Interacting SystemsMulti-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems.

In this work, we took a step forward to handle these challenges and provided a generic framework for trajectory prediction with dynamic relational reasoning for multi-agent systems.

Dynamic Interaction Graph LearningIn many situations, the interaction patterns recognized from the past time steps are likely not static in the future.

Summary and Broader ApplicationsWe introduce EvolveGraph, a generic trajectory prediction framework with dynamic relational reasoning, which can handle evolving inte…

1 неделя, 5 дней назад @ bairblog.github.io
Training on Test Inputs with Amortized Conditional Normalized Maximum Likelihood
Training on Test Inputs with Amortized Conditional Normalized Maximum Likelihood Training on Test Inputs with Amortized Conditional Normalized Maximum Likelihood

Training on Test Inputs with Amortized Conditional Normalized Maximum LikelihoodCurrent machine learning methods provide unprecedented accuracy across a range of domains, from computer vision to natural language processing.

Different classifiers that work well on the training set can give different predictions on the query point.

The minimax optimal distribution given a particular input $x$ and training set $\mathcal D$ can be explicitly computed as follows:For each label $y$, we append $(x,y)$ to our training set and compute the new optimal parameters $\hat \theta_y$ for this modified training set.

Figure 2: Here, we show the heatmap of CNML predictions (left) and the predictions of the tr…

2 недели назад @ bair.berkeley.edu
Training on Test Inputs with Amortized Conditional Normalized Maximum Likelihood
Training on Test Inputs with Amortized Conditional Normalized Maximum Likelihood Training on Test Inputs with Amortized Conditional Normalized Maximum Likelihood

Training on Test Inputs with Amortized Conditional Normalized Maximum LikelihoodCurrent machine learning methods provide unprecedented accuracy across a range of domains, from computer vision to natural language processing.

Different classifiers that work well on the training set can give different predictions on the query point.

The minimax optimal distribution given a particular input $x$ and training set $\mathcal D$ can be explicitly computed as follows:For each label $y$, we append $(x,y)$ to our training set and compute the new optimal parameters $\hat \theta_y$ for this modified training set.

Figure 2: Here, we show the heatmap of CNML predictions (left) and the predictions of the tr…

2 недели назад @ bairblog.github.io
Goodhart’s Law, Diversity and a Series of Seemingly Unrelated Toy Problems
Goodhart’s Law, Diversity and a Series of Seemingly Unrelated Toy Problems Goodhart’s Law, Diversity and a Series of Seemingly Unrelated Toy Problems

Our answer to this is to follow eigenvectors of the Hessian (‘ridges’) with negative eigenvalues from a saddle, in what we call Ridge Rider (RR).

As you see in the diagram, when we take a step along the ridge (in red) we reach a new point.

The full pictureIn the next diagram we show the full Ridge Rider algorithm.

Ridge Rider for Out of Distribution GeneralizationWe tested RR on the colored MNIST dataset, from [2].

Ridge Rider for Zero-Shot Co-ordinationFinally, we consider the zero-shot co-ordination problem.

2 недели, 3 дня назад @ bair.berkeley.edu
Goodhart’s Law, Diversity and a Series of Seemingly Unrelated Toy Problems
Goodhart’s Law, Diversity and a Series of Seemingly Unrelated Toy Problems Goodhart’s Law, Diversity and a Series of Seemingly Unrelated Toy Problems

Our answer to this is to follow eigenvectors of the Hessian (‘ridges’) with negative eigenvalues from a saddle, in what we call Ridge Rider (RR).

As you see in the diagram, when we take a step along the ridge (in red) we reach a new point.

The full pictureIn the next diagram we show the full Ridge Rider algorithm.

Ridge Rider for Out of Distribution GeneralizationWe tested RR on the colored MNIST dataset, from [2].

Ridge Rider for Zero-Shot Co-ordinationFinally, we consider the zero-shot co-ordination problem.

2 недели, 3 дня назад @ bairblog.github.io
Adapting on the Fly to Test Time Distribution Shift
Adapting on the Fly to Test Time Distribution Shift Adapting on the Fly to Test Time Distribution Shift

In this post, I will survey these works as well as other prominent frameworks for handling distribution shift.

ERM methods assume that there is no distribution shift, so the test distribution exactly matches the training distribution.

To move beyond ERM and learn models that generalize in the face of distribution shift, we must introduce additional assumptions.

If there is distribution shift, observing multiple test points can be useful either to infer the test distribution or otherwise adapt the model to this new distribution, even in the absence of labels.

Combining Training and Test AssumptionsPrior frameworks for distribution shift have assumed either training groups or test batches, bu…

3 недели, 4 дня назад @ bair.berkeley.edu
Adapting on the Fly to Test Time Distribution Shift
Adapting on the Fly to Test Time Distribution Shift Adapting on the Fly to Test Time Distribution Shift

Adapting on the Fly to Test Time Distribution ShiftImagine that you are building the next generation machine learning model for handwriting transcription.

In this post, I will survey these works as well as other prominent frameworks for handling distribution shift.

ERM methods assume that there is no distribution shift, so the test distribution exactly matches the training distribution.

To move beyond ERM and learn models that generalize in the face of distribution shift, we must introduce additional assumptions.

If there is distribution shift, observing multiple test points can be useful either to infer the test distribution or otherwise adapt the model to this new distribution, even in th…

3 недели, 4 дня назад @ bairblog.github.io
Reinforcement learning is supervised learning on optimized data
Reinforcement learning is supervised learning on optimized data Reinforcement learning is supervised learning on optimized data

Reinforcement learning is supervised learning on optimized dataThe two most common perspectives on Reinforcement learning (RL) are optimization and dynamic programming.

In contrast deep supervised learning has been extremely successful and we may hence ask: Can we use supervised learning to perform RL?

Seen from this supervised learning perspective, many RL algorithms can be viewed as alternating between finding good data and doing supervised learning on that data.

It turns out that finding “good data” is much easier in the multi-task setting, or settings that can be converted to a different problem for which obtaining “good data” is easy.

The table below compares the supervised learning pe…

1 месяц, 2 недели назад @ bairblog.github.io
Reinforcement learning is supervised learning on optimized data
Reinforcement learning is supervised learning on optimized data Reinforcement learning is supervised learning on optimized data

Reinforcement learning is supervised learning on optimized dataThe two most common perspectives on Reinforcement learning (RL) are optimization and dynamic programming.

In contrast deep supervised learning has been extremely successful and we may hence ask: Can we use supervised learning to perform RL?

Seen from this supervised learning perspective, many RL algorithms can be viewed as alternating between finding good data and doing supervised learning on that data.

The table below compares the supervised learning perspective to the optimization and dynamic programming perspectives:Â Optimization Perspective Dynamic Programming Perspective Supervised Learning Perspective What are we optimizi…

1 месяц, 2 недели назад @ bair.berkeley.edu
Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement Learning
Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement Learning Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement Learning

Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement LearningThis post is cross-listed on the CMU ML blog.

The world model captures general knowledge, allowing Plan2Explore to quickly solve new tasks through planning in its own imagination.

Learning the world modelPlan2Explore learns a world model that predicts future outcomes given past observations $o_{1:t}$ and actions $a_{1:t}$.

Most prior work on self-supervised exploration used model-free methods that reinforce past behavior that resulted in novel experience.

Future directionsPlan2Explore demonstrates that effective behavior can be learned through self-supervised exploration only.

1 месяц, 3 недели назад @ bairblog.github.io
Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement Learning
Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement Learning Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement Learning

Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement LearningTo operate successfully in unstructured open-world environments, autonomous intelligent agents need to solve many different tasks and learn new tasks quickly.

The world model captures general knowledge, allowing Plan2Explore to quickly solve new tasks through planning in its own imagination.

Learning the world modelPlan2Explore learns a world model that predicts future outcomes given past observations $o_{1:t}$ and actions $a_{1:t}$.

Most prior work on self-supervised exploration used model-free methods that reinforce past behavior that resulted in novel experience.

Future directionsPlan2Explore demonstrate…

1 месяц, 3 недели назад @ bair.berkeley.edu
Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement Learning
Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement Learning Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement Learning

Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement LearningTo operate successfully in unstructured open-world environments, autonomous intelligent agents need to solve many different tasks and learn new tasks quickly.

The world model captures general knowledge, allowing Plan2Explore to quickly solve new tasks through planning in its own imagination.

Learning the world modelPlan2Explore learns a world model that predicts future outcomes given past observations $o_{1:t}$ and actions $a_{1:t}$ (see figure below).

Most prior work on self-supervised exploration used model-free methods that reinforce past behavior that resulted in novel experience.

Solving tasks with the…

1 месяц, 3 недели назад @ bair.berkeley.edu
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

AWAC: Accelerating Online Reinforcement Learning with Offline DatasetsOur method learns complex behaviors by training offline from prior datasets (expert demonstrations, data from previous experiments, or random exploration data) and then fine-tuning quickly with online interaction.

Robots trained with reinforcement learning (RL) have the potential to be used across a huge variety of challenging real world problems.

Figure 2: On-policy methods are slow to learn compared to off-policy methods, due to the ability of off-policy methods to “stitch" good trajectories together, illustrated on the left.

We aim to study tasks representative of the difficulties of real-world robot learning, where …

2 месяца, 3 недели назад @ bair.berkeley.edu
AWS Machine Learning AWS Machine Learning
последний пост 2 часа назад
Customization, automation and scalability in customer service: Integrating Genesys Cloud and AWS Contact Center Intelligence
Customization, automation and scalability in customer service: Integrating Genesys Cloud and AWS Contact Center Intelligence Customization, automation and scalability in customer service: Integrating Genesys Cloud and AWS Contact Center Intelligence

This is a guest post authored by Rebecca Owens and Julian Hernandez, who work at Genesys Cloud.

You should first configure an Amazon Lex bot in one of the supported languages (for this post, we use Spanish-US).

The following screenshot shows a view of Genesys Cloud Resource Center, where you can get started.

This is how you can call onto any of your existing Amazon Lex bots from a Genesys Cloud Architect flow.

Genesys Cloud can use the outputs to continue processing the interaction and provide context to the human agent if the interaction is transferred.

2 часа назад @ aws.amazon.com
Amazon Transcribe streaming adds support for Japanese, Korean, and Brazilian Portuguese
Amazon Transcribe streaming adds support for Japanese, Korean, and Brazilian Portuguese Amazon Transcribe streaming adds support for Japanese, Korean, and Brazilian Portuguese

Today, we’re excited to launch Japanese, Korean, and Brazilian Portuguese language support for Amazon Transcribe streaming.

To deliver streaming transcriptions with low latency for these languages, we’re also announcing availability of Amazon Transcribe streaming in the Asia Pacific (Seoul), Asia Pacific (Tokyo), and South America (São Paulo) Regions.

Amazon Transcribe added support for Italian and German languages earlier in November 2020, and this launch continues to expand the service’s streaming footprint.

We are pleased to welcome Amazon Transcribe’s latest addition of Japanese support for streaming audio.

For the full list of supported languages and Regions for Amazon Transcribe strea…

2 часа назад @ aws.amazon.com
Real-time anomaly detection for Amazon Connect call quality using Amazon ES
Real-time anomaly detection for Amazon Connect call quality using Amazon ES Real-time anomaly detection for Amazon Connect call quality using Amazon ES

The high cardinality anomaly detection feature of Amazon ES is a machine learning (ML) approach that can solve this problem.

Anomaly detection in four stagesIf you have an Amazon Connect instance, you can deploy the solution and follow along with this post.

There are four steps to getting started with using anomaly detection to proactively monitor your data:Create an anomaly detector.

To get actionable insights and preconfigured anomaly detection for Amazon Connect metrics, you can deploy the call quality monitoring solution.

The high cardinality anomaly detection feature is available on all Amazon ES domains running Elasticsearch 7.9 or greater.

3 часа назад @ aws.amazon.com
Analyzing data stored in Amazon DocumentDB (with MongoDB compatibility) using Amazon Sagemaker
Analyzing data stored in Amazon DocumentDB (with MongoDB compatibility) using Amazon Sagemaker Analyzing data stored in Amazon DocumentDB (with MongoDB compatibility) using Amazon Sagemaker

In this post, we explore using Amazon SageMaker to analyze data stored in Amazon DocumentDB (with MongoDB compatibility).

Document Database Concepts SQL Concepts Document Row Collection Table Database Database Field ColumnWe now implement the following Amazon DocumentDB tasks using SageMaker:Connect to an Amazon DocumentDB cluster.

A SageMaker role to retrieve the Amazon DocumentDB login credentials, allowing connections to the Amazon DocumentDB cluster from a SageMaker notebook.

If you’re new to Amazon DocumentDB, see Getting Started with Amazon DocumentDB.

If you’re planning to migrate to Amazon DocumentDB, see Migrating to Amazon DocumentDB.

6 часов назад @ aws.amazon.com
Creating Amazon SageMaker Studio domains and user profiles using AWS CloudFormation
Creating Amazon SageMaker Studio domains and user profiles using AWS CloudFormation Creating Amazon SageMaker Studio domains and user profiles using AWS CloudFormation

In this post, we demonstrate how you can create a SageMaker Studio domain and user profile using AWS CloudFormation.

Because we invoke this function using an AWS CloudFormation custom resource, the custom resource request type is sent in the RequestType field from AWS CloudFormation.

For more information about invoking a Lambda function with AWS CloudFormation, see Using AWS Lambda with AWS CloudFormation.

For information about creating Studio domain inside a VPC, see Securing Amazon SageMaker Studio connectivity using a private VPC.

For more information about SageMaker Studio, see Get Started with Amazon SageMaker Studio.

3 дня, 2 часа назад @ aws.amazon.com
Your guide to artificial intelligence and machine learning at re:Invent 2020
Your guide to artificial intelligence and machine learning at re:Invent 2020 Your guide to artificial intelligence and machine learning at re:Invent 2020

As always, artificial intelligence (AI) and machine learning (ML) continue to be on the list of top topics with our customers and partners.

To help you plan your agenda for the extravaganza, here are a few highlights from the artificial intelligence and machine learning track at re:Invent 2020.

Machine Learning KeynoteThis year, we’re hosting the first ever machine learning keynote, delivered by Swami Sivasubramanian, Vice President, Machine Learning, AWS.

Register now for free and see you soon on the artificial intelligence and machine learning track at re:Invent 2020.

About the AuthorShyam Srinivasan is on the AWS Machine Learning marketing team.

5 дней, 21 час назад @ aws.amazon.com
Amazon Forecast now supports accuracy measurements for individual items
Amazon Forecast now supports accuracy measurements for individual items Amazon Forecast now supports accuracy measurements for individual items

Improving forecast accuracy for specific items—such as those with higher prices or higher costs—is often more important than optimizing for all items.

If a smaller set of items is more important for your business, achieving a high forecasting accuracy for those items is imperative.

For more information about how each metric is calculated and recommendations for the best use case for each metric, see Measuring forecast model accuracy to optimize your business objectives with Amazon Forecast.

Although Forecast provides these three industry-leading forecast accuracy measures, you might prefer to calculate accuracy using different metrics.

Forecast now supports forecast accuracy measurement for…

5 дней, 22 часа назад @ aws.amazon.com
Amazon Lex launches support for Latin American Spanish and German
Amazon Lex launches support for Latin American Spanish and German Amazon Lex launches support for Latin American Spanish and German

Starting today, Amazon Lex supports Latin American Spanish and German.

See how some of our customers are using Amazon Lex to create virtual contact center agents, chat interfaces, and knowledge management bots.

Amazon Lex is simple to use and the Contact Center team was already creating bots after just a 1-hour enablement session.

To use the new Amazon Lex languages, simply choose the language when creating a new bot via the Amazon Lex console or SDK.

See all the ways in which other customers are using Amazon Lex.

5 дней, 22 часа назад @ aws.amazon.com
How Xpertal is creating the Contact Center of the future with Amazon Lex
How Xpertal is creating the Contact Center of the future with Amazon Lex How Xpertal is creating the Contact Center of the future with Amazon Lex

In addition, they used other AI services such as Amazon Comprehend, Amazon Polly, and Amazon Connect to automate other parts of the contact center.

It was easy to integrate Amazon Lex with Amazon Connect and other third-party collaboration tools used within FEMSA to achieve an omni-channel system for support requests.

Amazon Connect then invokes Amazon Lex to identify the caller’s need.

With Amazon Lex’s easy to use interface, our Contact Center team was able to create bots after a 1-hour training session.

Depending on your contact center goals, learn more about Amazon Connect’s omni-channel, cloud-based contact center or bring your own telephony (BYOT) with AWS Contact Center Intelligence.

5 дней, 22 часа назад @ aws.amazon.com
Announcing the launch of Amazon Comprehend Events
Announcing the launch of Amazon Comprehend Events Announcing the launch of Amazon Comprehend Events

Today, Amazon Comprehend is launching Comprehend Events, a new API for event extraction from natural language text documents.

With this launch, you can use Comprehend Events to extract granular details about real-world events and associated entities expressed in unstructured text.

Comprehend Events overviewThe Comprehend Events API, under the hood converts unstructured text into structured data that answers who-what-when-where-how questions.

First, we specify Comprehend Events job parameters, just as we would with any other Amazon Comprehend feature.

This allows the SageMaker notebook instance to talk with Amazon S3 and Amazon Comprehend.

6 дней, 2 часа назад @ aws.amazon.com
Bringing your own R environment to Amazon SageMaker Studio
Bringing your own R environment to Amazon SageMaker Studio Bringing your own R environment to Amazon SageMaker Studio

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML).

This post focuses on adding a custom R image to SageMaker Studio so you can build and train your R models with SageMaker.

For more information about R on SageMaker, see Coding with R on Amazon SageMaker notebook instances and R User Guide to Amazon SageMaker.

For more information about the specifications that apply to the container image that is represented by a SageMaker image version, see Custom SageMaker image specifications.

You can see R (Custom R Image) kernel and the instance type on the upper right corner of the notebook.

6 дней, 4 часа назад @ aws.amazon.com
Building natural conversation flows using context management in Amazon Lex
Building natural conversation flows using context management in Amazon Lex Building natural conversation flows using context management in Amazon Lex

Understanding the direction and context of an ever-evolving conversation is beneficial to building natural, human-like conversational interfaces.

Starting today, Amazon Lex supports context management natively, so you can manage the context directly without the need for custom code.

Building the Amazon Lex bot FinancialPlannerIn this post, we build an Amazon Lex bot called FinancialPlanner , which is available for download.

We also use this as the input context for ExpensesFollowupIntent .

We use this context as the input context for IncomeFollowupIntent .

1 неделя назад @ aws.amazon.com
Customizing your machine translation using Amazon Translate Active Custom Translation
Customizing your machine translation using Amazon Translate Active Custom Translation Customizing your machine translation using Amazon Translate Active Custom Translation

Today, we’re excited to introduce Active Custom Translation (ACT), a feature that gives you more control over your machine translation output.

To save on model training and management costs, you may choose to delay updating your custom translation model, which means your models are always stale—negatively affecting your custom translation experience.

“Active Custom Translation allows our customers to focus on the value of their latest data and forget about the lifecycle management of custom translation models.

ConsoleTo use the Amazon Translate console, complete the following steps:On the Amazon Translate console, under Customization, choose Parallel data.

ConsoleFor instructions on running…

1 неделя назад @ aws.amazon.com
Getting started with Amazon Kendra ServiceNow Online connector
Getting started with Amazon Kendra ServiceNow Online connector Getting started with Amazon Kendra ServiceNow Online connector

Currently, Amazon Kendra has two provisioning editions: the Amazon Kendra Developer Edition for building proof of concepts (POCs), and the Amazon Kendra Enterprise Edition.

Also, Amazon Kendra requires different roles to operate:IAM roles for indexes, which are needed by Amazon Kendra to write to Amazon CloudWatch Logs.

Common errorsIn this section, we discuss errors that may occur, whether using the Amazon Kendra console or the Amazon Kendra API.

You should look at CloudWatch logs and error messages returned in the Amazon Kendra console or via the Amazon Kendra API.

For more information about Amazon Kendra, see AWS re:Invent 2019 – Keynote with Andy Jassy on YouTube, Amazon Kendra FAQs, an…

1 неделя, 2 дня назад @ aws.amazon.com
Amazon Augmented AI is now a HIPAA eligible service
Amazon Augmented AI is now a HIPAA eligible service Amazon Augmented AI is now a HIPAA eligible service

Amazon Augmented AI (Amazon A2I) is now a HIPAA eligible service.

Amazon A2I makes it easy to build the workflows required for human review of machine learning (ML) predictions.

If you have a HIPAA Business Associate Addendum (BAA) in place with AWS, you can now start using Amazon A2I for your HIPAA eligible workloads.

For information and best practices about configuring AWS HIPAA eligible services to store, process, and transmit PHI, see the Architecting for HIPAA Security and Compliance on Amazon Web Services whitepaper.

Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems and managing large numbers of …

1 неделя, 3 дня назад @ aws.amazon.com
NVIDIA
последний пост 7 часов назад
How to Avoid Speed Bumps and Stay in the AI Fast Lane with Hybrid Cloud Infrastructure
How to Avoid Speed Bumps and Stay in the AI Fast Lane with Hybrid Cloud Infrastructure How to Avoid Speed Bumps and Stay in the AI Fast Lane with Hybrid Cloud Infrastructure

That’s the question many organizations ask when building AI infrastructure.

‘Own the Base, Rent the Spike’Businesses that ultimately embrace hybrid cloud infrastructure trace a familiar trajectory.

Delivering the AI Hybrid Cloud with DGX and Google Cloud’s Anthos on Bare MetalTo help businesses embrace hybrid cloud infrastructure, NVIDIA has introduced support for Google Cloud’s Anthos on bare metal for its DGX A100 systems.

While many have implemented GPU-accelerated AI in their data centers, much of the world retains some legacy x86 compute infrastructure.

With this combination, enterprises can enjoy the rapid start and elasticity of resources offered on Google Cloud, as well as the secur…

7 часов назад @ blogs.nvidia.com
MONAI Imaging Framework Fast-Tracked to Production to Accelerate AI in Healthcare
MONAI Imaging Framework Fast-Tracked to Production to Accelerate AI in Healthcare MONAI Imaging Framework Fast-Tracked to Production to Accelerate AI in Healthcare

MONAI — the Medical Open Network for AI, a domain-optimized, open-source framework for healthcare — is now ready for production with the upcoming release of the NVIDIA Clara application framework for AI-powered healthcare and life sciences.

“Global adoption of MONAI is fostering collaboration across the globe facilitated by federated learning.”Adoption by the healthcare ecosystem of MONAI has been tremendous.

DKFZ, King’s College London, Mass General, Stanford and Vanderbilt are among those to adopt the AI framework for imaging.

“MONAI is quickly becoming the go-to deep learning framework for healthcare.

“Project MONAI, jointly with the rest of the NVIDIA Clara ecosystem, will help deliver …

10 часов назад @ blogs.nvidia.com
NVIDIA Launches Inception Alliance with GE Healthcare and Nuance to Accelerate Medical Imaging AI Startups
NVIDIA Launches Inception Alliance with GE Healthcare and Nuance to Accelerate Medical Imaging AI Startups NVIDIA Launches Inception Alliance with GE Healthcare and Nuance to Accelerate Medical Imaging AI Startups

Today we’re announcing the NVIDIA Inception Alliance for Healthcare, an initiative where medical AI startups have new opportunities to chart innovations and accelerate their success with the help of NVIDIA and its healthcare industry partners.

The Nuance AI Marketplace brings AI into the radiology workflow by connecting developers directly with radiology subscribers.

To nurture the growth of AI startups in healthcare, and ultimately the entire medical ecosystem, NVIDIA is working with healthcare giants to offer Inception members an accelerated go-to-market path.

The NVIDIA Inception Alliance for Healthcare will forge new ways to grow through targeted networking, AI training, early access to…

10 часов назад @ blogs.nvidia.com
Updating AI Product Performance from Throughput to Time-To-Solution
Updating AI Product Performance from Throughput to Time-To-Solution Updating AI Product Performance from Throughput to Time-To-Solution

To be useful and productive, the model must not only have high throughput, but it also must make correct predictions at that high throughput.

As obvious as this may sound, the AI industry continues to pay a lot of attention to just throughput.

High throughput and convergence are required to reach your destination and successfully implement AI in production.

State-of-the-art industry accuracy metrics at convergence of a sampling of AI models available from NGC (full list of models available online).

In 2017, NVIDIA launched NGC, a hub for GPU-optimized software for deep learning, machine learning, and high performance computing.

1 неделя назад @ developer.nvidia.com
Supercomputing Chops: China’s Tsinghua Takes Top Flops in SC20 Student Cluster Battle
Supercomputing Chops: China’s Tsinghua Takes Top Flops in SC20 Student Cluster Battle Supercomputing Chops: China’s Tsinghua Takes Top Flops in SC20 Student Cluster Battle

Virtual this year, the SC20 Student Cluster Competition was still all about teams vying for top supercomputing performance in the annual battle for HPC bragging rights.

That honor went to Beijing’s Tsinghua University, whose six-member undergraduate student team clocked in 300 teraflops of processing performance.

The Virtual Student Cluster Competition was this year’s battleground for 19 teams.

Real-World ScenariosIn the 72-hour competition, student teams designed and built virtual clusters running NVIDIA GPUs in the Microsoft Azure cloud.

Pan Yueyang, a junior in computer science at Peking University, joined his university’s supercomputing team before taking the leap to participate in the …

1 неделя, 3 дня назад @ blogs.nvidia.com
Bringing Enterprise Medical Imaging to Life: RSNA Highlights What’s Next for Radiology
Bringing Enterprise Medical Imaging to Life: RSNA Highlights What’s Next for Radiology Bringing Enterprise Medical Imaging to Life: RSNA Highlights What’s Next for Radiology

It will all be on display at RSNA, the Radiological Society of North America’s annual meeting, taking place Nov. 29 – Dec. 5.

Radiologists, healthcare organizations, developers and instrument makers at RSNA will share their latest advancements and what’s coming next — with an eye on the growing ability of AI models to integrate with medical-imaging workflows.

Hands-on training courses from the NVIDIA Deep Learning Institute are also available, covering medical imaging topics including image classification, coarse-to-fine contextual memory and data augmentation with generative networks.

Email to request a meeting with our deep learning experts.

Subscribe to NVIDIA healthcare news here.

1 неделя, 3 дня назад @ blogs.nvidia.com
Science Magnified: Gordon Bell Winners Combine HPC, AI
Science Magnified: Gordon Bell Winners Combine HPC, AI Science Magnified: Gordon Bell Winners Combine HPC, AI

Seven finalists including both winners of the 2020 Gordon Bell awards used supercomputers to see more clearly atoms, stars and more — all accelerated with NVIDIA technologies.

The Gordon Bell Prize is regarded as a Nobel Prize in the supercomputing community, attracting some of the most ambitious efforts of researchers worldwide.

AI Helps Scale Simulation 1,000xWinners of the traditional Gordon Bell award collaborated across universities in Beijing, Berkeley and Princeton.

The team used all 27,654 GPUs on the Summit supercomputer to get results in just 10 minutes, thanks to harnessing an estimated 105.9 petaflops of double-precision performance.

Simulating the Coronavirus with HPC+AIThe fou…

1 неделя, 4 дня назад @ blogs.nvidia.com
COVID-19 Spurs Scientific Revolution in Drug Discovery with AI
COVID-19 Spurs Scientific Revolution in Drug Discovery with AI COVID-19 Spurs Scientific Revolution in Drug Discovery with AI

Research across global academic and commercial labs to create a more efficient drug discovery process won recognition today with a special Gordon Bell Prize for work fighting COVID-19.

Relay Therapeutics uses NVIDIA GPUs and software to simulate with machine learning how proteins move, opening up new directions in the drug discovery process.

From Clara Discovery to Cambridge-1NVIDIA Clara Discovery delivers a framework with AI models, GPU-optimized code and applications to accelerate every stage in the drug discovery pipeline.

We’ve also committed to build with partners including GSK and AstraZeneca Europe’s largest supercomputer dedicated to driving drug discovery forward.

Hear NVIDIA CEO …

1 неделя, 4 дня назад @ blogs.nvidia.com
A Binding Decision: Startup Uses Microscopy Breakthrough to Speed Creation of COVID-19 Vaccines
A Binding Decision: Startup Uses Microscopy Breakthrough to Speed Creation of COVID-19 Vaccines A Binding Decision: Startup Uses Microscopy Breakthrough to Speed Creation of COVID-19 Vaccines

Doing so requires building detailed 3D models of protein molecules, which until recently has been an intensely time-consuming task.

The GPU-powered machine learning algorithms underlying Structura’s software power the image processing stage of a technology called cryo-electron microscopy, or cryo-EM, a revolutionary breakthrough in biochemistry that was the subject of the 2017 Nobel Prize in chemistry.

In fact, CEO Ali Punjani states that Structura’s software has been used by scientists to visualize COVID-19 proteins in multiple publications.

The code that runs on Structura’s GPUs is written in CUDA, while cuDNN is used for some high-end computing tasks.

“What we’re building right now is a …

1 неделя, 4 дня назад @ blogs.nvidia.com
NVIDIA RTX Real-Time Rendering Inspires Vivid Visuals, Captivating Cinematics for Film and Television
NVIDIA RTX Real-Time Rendering Inspires Vivid Visuals, Captivating Cinematics for Film and Television NVIDIA RTX Real-Time Rendering Inspires Vivid Visuals, Captivating Cinematics for Film and Television

Now, he’s working on the popular series The Mandalorian.

He’s always had a close relationship with cutting-edge technology to produce the highest-quality visuals, even when he’s working at home.

“NVIDIA RTX has allowed me to work without babysitting the geometry all along the way,” said Church.

Featuring the NVIDIA Studio driver and NVIDIA Quadro RTX GPU, the ZBook Studio has been tested and certified to work with the top creative applications.

Learn more about NVIDIA RTX.

1 неделя, 4 дня назад @ blogs.nvidia.com
GeForce NOW Streaming Comes to iOS Safari
GeForce NOW Streaming Comes to iOS Safari GeForce NOW Streaming Comes to iOS Safari

GeForce NOW transforms underpowered or incompatible hardware into high-performance GeForce gaming rigs.

Now, we’re bringing the world of PC gaming to iOS devices through Safari.

GeForce NOW is streaming on iOS Safari, in beta, starting today.

Once logged in, you’re only a couple clicks away from streaming a massive catalog of the latest and most played PC games.

GeForce NOW on iOS Safari requires a gamepad — keyboard and mouse-only games aren’t available due to hardware limitations.

1 неделя, 4 дня назад @ blogs.nvidia.com
Building a Benchmark for Human-Level Concept Learning and Reasoning
Building a Benchmark for Human-Level Concept Learning and Reasoning Building a Benchmark for Human-Level Concept Learning and Reasoning

In each test set, there are two test images (left: positive, right: negative).

The basic shape concept is a combination of a fan-like shape and trapezoid.

In this post, we introduce Bongard-LOGO, a new benchmark for human-level visual concept learning and reasoning, directly inspired by the design principles behind the BPs.

Figure 3 shows the test accuracy (%) on different dataset splits, including free-form shape test set (FF), basic shape test set (BA), combinatorial abstract shape test set (CM), and novel abstract shape test set (NV).

For more information, see the research paper, Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning, Bongard-LOGO website, or slides.

1 неделя, 6 дней назад @ developer.nvidia.com
Anatomical Adventures in VR: University’s Science Visualizations Tap NVIDIA CloudXR and 5G
Anatomical Adventures in VR: University’s Science Visualizations Tap NVIDIA CloudXR and 5G Anatomical Adventures in VR: University’s Science Visualizations Tap NVIDIA CloudXR and 5G

Operating NVIDIA CloudXR on the private 5G network, student nurses and healthcare professionals can experience lessons and simulations in virtual reality environments.

With NVIDIA CloudXR, users don’t need to be physically tethered to a high-performance computer that drives rich, immersive environments.

A member of the NVIDIA CloudXR early access program, The Grid Factory is helping organizations realize new opportunities to deliver high-quality graphics over 5G.

With 5G, CloudXR can provide lower-latency immersive experiences, and VR environments can become more natural for users.

Learn more about NVIDIA CloudXR.

1 неделя, 6 дней назад @ blogs.nvidia.com
Take the A100 Train: HPC Centers Worldwide Jump Aboard NVIDIA AI Supercomputing Fast Track
Take the A100 Train: HPC Centers Worldwide Jump Aboard NVIDIA AI Supercomputing Fast Track Take the A100 Train: HPC Centers Worldwide Jump Aboard NVIDIA AI Supercomputing Fast Track

To fuel this next research ride, NVIDIA Monday introduced the NVIDIA A100 80GB GPU with HBM2e technology.

Leonardo joins a growing pack of European systems on NVIDIA AI platforms supported by the EuroHPC initiative.

Linköping University is planning to build Sweden’s fastest AI supercomputer, dubbed BerzeLiUs, based on the NVIDIA DGX SuperPOD infrastructure.

The Japan Agency for Marine-Earth Science and Technology, or JAMSTEC, is upgrading its Earth Simulator with NVIDIA A100 GPUs and NVIDIA InfiniBand.

India’s Centre for Development of Advanced Computing, or C-DAC, is commissioning the country’s fastest and largest AI supercomputer, called PARAM Siddhi – AI.

1 неделя, 6 дней назад @ blogs.nvidia.com
NVIDIA, Ampere Computing Raise Arm 26x in Supercomputing
NVIDIA, Ampere Computing Raise Arm 26x in Supercomputing NVIDIA, Ampere Computing Raise Arm 26x in Supercomputing

In the past 18 months, researchers have witnessed a whopping 25.5x performance boost for Arm-based platforms in high performance computing, thanks to the combined efforts of the Arm and NVIDIA ecosystems.

The Arm Neoverse N1 core gave systems-on-a-chip like Ampere Computing’s Altra an estimated 2.3x improvement over last year’s designs.

Ampere Computing has already attracted support from nine original equipment and design manufacturers and systems integrators, including Gigabyte, Lenovo and Wiwynn.

We’re extending the ecosystem with Arm support built into our NVIDIA AI, HPC, networking and graphics software.

It’s the latest page in the story of an open, thriving Arm ecosystem that keeps get…

1 неделя, 6 дней назад @ blogs.nvidia.com
Apple Machine Learning Journal Apple Machine Learning Journal
последний пост None
Uber Engineering Uber Engineering
последний пост 1 месяц, 3 недели назад
Ludwig v0.3 Introduces Hyper-parameter Optimization, Transformers and TensorFlow 2 support
Ludwig v0.3 Introduces Hyper-parameter Optimization, Transformers and TensorFlow 2 support Ludwig v0.3 Introduces Hyper-parameter Optimization, Transformers and TensorFlow 2 support

Today, we are excited to release Ludwig version 0.3, featuring several updates that take our framework to the next level.

Finding the parameters that yield the best performance on a data set is a time-consuming job that can be automated by hyper-parameter optimization techniques.

The hyper-parameter optimization architecture is easy to expand and we plan to integrate with additional samplers and executors in the near future, like RayTune.

Nonetheless, Ludwig version 0.3 ships with a revamped, more modular, and easier-to-extend backend based on TensorFlow 2, lending to greater flexibility all around.

Moving forwardWith the addition of hyper-parameter optimization, Ludwig version 0.3 has soli…

1 месяц, 3 недели назад @ eng.uber.com
Fiber: Distributed Computing for AI Made Simple
Fiber: Distributed Computing for AI Made  Simple Fiber: Distributed Computing for AI Made Simple

Instead of programming only a single desktop or laptop, users can leverage this system to program the whole computer cluster.

Fiber allows users to write programs that run on a computer cluster without needing to dive into the details of the computer cluster.

This overall architecture is summarized in Figure 2, below:Job-backed processesFiber introduces a new concept called job-backed processes (also called a Fiber processes).

When starting a new Fiber process, Fiber creates a new job with the proper Fiber back end on the current computer cluster.

Our hypothesis was that Fiber should perform similarly to multiprocessing because neither Fiber nor multiprocessing rely on complex scheduling me…

5 месяцев назад @ eng.uber.com
Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine
Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine

Unfortunately, adding support for a new deep learning framework across an entire machine learning stack is resource and time-intensive.

Using multiple deep learning frameworksDeep learning (DL) is advancing very quickly and different DL frameworks are effective at different tasks.

Over the last year, we have deployed hundreds of Neuropod models across Uber ATG, Uber AI, and the core Uber business.

Deep learning with NeuropodLet’s take a look at the overall deep learning process when using Neuropod to see how it helps make experimentation, deployment, and iteration easier.

Next stepsNeuropod has allowed Uber to quickly build and deploy new deep learning models, but that’s just the start.

5 месяцев, 3 недели назад @ eng.uber.com
Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine Learning
Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine Learning Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine Learning

The “spikes” at intersections result from the SDV crossing the same intersection multiple times as part of a “grid-coverage” driving pattern.

Data mining the scenario “pedestrian crossing the street”While the SDV perception system is designed to detect pedestrians, only a subset of pedestrians actually cross the street.

Analyzing the “pedestrian crossing the street” scenarioThe scenario of a pedestrian crossing the street has many relevant measurements, including the pedestrian crossing speed, road width, distance walked, crossing duration, distance walked on crosswalk, and traffic light state(s) at the time of crossing.

Let’s start by analyzing just one measurement: the pedestrian crossing…

6 месяцев назад @ eng.uber.com
Meta-Graph: Few-Shot Link Prediction Using Meta-Learning
Meta-Graph: Few-Shot Link Prediction Using Meta-Learning Meta-Graph: Few-Shot Link Prediction Using Meta-Learning

For instance, in a social network we may use link prediction to power a friendship recommendation system, or in the case of biological network data, we might use link prediction to infer possible relationships between drugs, proteins, and diseases.

In principle, it can be combined with a wide variety of link prediction approaches based on GNNs, but we adopted a specific GNN, variational graph autoencoders (VGAEs), as our base link prediction framework9.

Experiment setupTo test how Meta-Graph might work in a real-world setting, we designed three novel benchmarks for few-shot link prediction.

In this few-shot link prediction setting, there are train/val/test splits at both the edge level and …

6 месяцев назад @ eng.uber.com
Announcing a New Framework for Designing Optimal Experiments with Pyro
Announcing a New Framework for Designing Optimal Experiments with Pyro Announcing a New Framework for Designing Optimal Experiments with Pyro

We’ll treat working memory capacity as the length of the longest list of random digits that the participant can memorize.

InferenceWe use Bayesian inference to incorporate our new observation into an estimate of the participant’s working memory capacity.

It models the probability of correctly remembering the list of digits of different lengths for people with different working memory capacities, as shown in Figure 1, below:We also need a sense of what working memory capacities are plausible.

Computing the optimal designOur score for experimental designs, EIG, is notoriously difficult to estimate.

In our paper, we showed that this method can be remarkably accurate on a range of different exp…

6 месяцев, 3 недели назад @ eng.uber.com
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Last year we introduced the Paired Open-Ended Trailblazer (POET) to explore the idea of open-ended algorithms.

ANNECS: A new way to measure progress in open-ended systemsQuantifying the performance of open-ended algorithms has remained elusive for the field.

Compare those from Original POET in Figure 4a to those produced by Enhanced POET in Figure 4b, below.

If this piques your interest, be sure to check out videos of example Enhanced POET agents on the Uber AI YouTube channel.

Towards that end, we are not only releasing a paper with full technical details, but also have open sourced the code for Enhanced POET.

6 месяцев, 4 недели назад @ eng.uber.com
Under the Hood of Uber ATG’s Machine Learning Infrastructure and Versioning Control Platform for Self-Driving Vehicles
Under the Hood of Uber ATG’s Machine Learning Infrastructure and Versioning Control Platform for Self-Driving Vehicles Under the Hood of Uber ATG’s Machine Learning Infrastructure and Versioning Control Platform for Self-Driving Vehicles

A trained model, which requires as input the data set artifact, the model training code, and configuration files governing model training.

Example sequence of events: registering a new data setUpon user-registration of a new data set, the VerCD Data set Service stores the dependency metadata in our database.

Data set service APIThe data set service is responsible for tracking the dependencies for building a given data set.

The REST API supports the functions of creating a new data set, reading the metadata for a data set, updating the metadata of a data set, deleting a data set, and getting the artifact locations of the data set (such as in S3 or HDFS).

For instance, the VerCD data set serv…

9 месяцев назад @ eng.uber.com
Building a Backtesting Service to Measure Model Performance at Uber-scale
Building a Backtesting Service to Measure Model Performance at Uber-scale Building a Backtesting Service to Measure Model Performance at Uber-scale

To better assess the performance of our models, we built a backtesting service for measuring forecast model error rates.

The backtesting service runs in a distributed system, allowing multiple models (>10), many backtesting windows (>20), and models for different cities (>200) to run simultaneously.

Backtesting at scaleOur data science teams regularly create forecast models and statistics to better understand budget spending and project financial performance.

For the purposes of our backtesting service, we chose to leverage two primary backtesting data split mechanisms, backtesting with an expanding window and backtesting with a sliding window:Above, we showcase three windows for each metho…

9 месяцев, 3 недели назад @ eng.uber.com
neptune.ai neptune.ai
последний пост 14 часов назад
Graph Neural Network and Some of GNN Applications – Everything You Need to Know
Graph Neural Network and Some of GNN Applications – Everything You Need to Know Graph Neural Network and Some of GNN Applications – Everything You Need to Know

That’s where Graph Neural Networks (GNN) come in, which we’ll explore in this article.

READ SOME PAPERS ABOUT GNN👉 Top Research Papers from the ECML-PKDD 2020 Conference (on Graph Neural Networks)Why is it hard to analyze a graph?

SourceGraph Neural NetworkGraph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs.

ConclusionOver the past few years, graph neural networks have become powerful and practical tools for any problem that can be modeled by graphs.

In this article, we did a comprehensive overview of graph neural networks and introduced a wide range of GNN applications.

14 часов назад @ neptune.ai
Model-Based and Model-Free Reinforcement Learning – Pytennis Case Study
Model-Based and Model-Free Reinforcement Learning – Pytennis Case Study Model-Based and Model-Free Reinforcement Learning – Pytennis Case Study

Along the way, we will explore:Fundamental concepts of Reinforcement Learninga) Markov decision processes / Q-Value / Q-Learning / Deep Q Network Difference between model-based and model-free reinforcement learning.

Comparison/Evaluation References to learn moreSEE RELATED ARTICLES👉 7 Applications of Reinforcement Learning in Finance and Trading👉 10 Real-Life Applications of Reinforcement Learning👉 Best Reinforcement Learning Tutorials, Examples, Projects, and CoursesFundamental concepts of Reinforcement LearningAny reinforcement learning problem includes the following elements:Agent – the program controlling the object of concern (for instance, a robot).

Tennis game using Deep Q Network – …

3 дня, 15 часов назад @ neptune.ai
ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It
ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

Experiment tracking is the process of saving all experiment related information that you care about for every experiment you run.

Experiment tracking is the process of saving all experiment related information that you care about for every experiment you run.

With the experiment tracking system, all of your experiment results are logged to one experiment repository by design.

Read more about monitoring ML experiments live ➡️Experiment tracking best practicesSo far, we’ve covered what experiment tracking is and why it matters.

See this view in appHow to set up experiment trackingOk, those are nice guidelines, but how do you actually implement experiment tracking in your project?

4 дня, 15 часов назад @ neptune.ai
Deep Dive Into Error Analysis and Model Debugging in Machine Learning (and Deep Learning)
Deep Dive Into Error Analysis and Model Debugging in Machine Learning (and Deep Learning) Deep Dive Into Error Analysis and Model Debugging in Machine Learning (and Deep Learning)

In competitions and research, after you train a model, you do error analysis, figure out what’s wrong with the model.

It’s possible that your 99% validation accuracy is a result of poor features – features engineered using a data leak.

SourceDebugging model training pipelineWe looked for errors in the three core components, but there’s room for more.

Model training pipeline is more complicated in Deep Learning systems, mainly because of the flexibility deep learning provides.

For example, if the distribution of features is distinct from the training set features, model drift is a likely possibility.

5 дней, 11 часов назад @ neptune.ai
Pandas Plot: Deep Dive Into Plotting Directly with Pandas
Pandas Plot: Deep Dive Into Plotting Directly with Pandas Pandas Plot: Deep Dive Into Plotting Directly with Pandas

The Pandas Plot is a set of methods that can be used with a Pandas DataFrame, or a series, to plot various graphs from the data in that DataFrame.

Pandas Plot simplifies the creation of graphs and plots, so you don’t need to know the details of working with matplotlib.

df.plot(y= 'NIFTY Bank index' )Line plot showing the NIFTY Bank index performance in the year 2020As you can see above, calling the .plot() method on a dataframe returns a line plot by default.

Let’s create histograms for the NIFTY FMCG index and NIFTY Bank index only.

pd.plotting.bootstrap_plot(df[ 'NIFTY Bank index' ])A bootstrap plot in PandasConclusionIn this article, we looked at the capabilities of pandas as a plotting …

6 дней, 15 часов назад @ neptune.ai
Machine Learning Model Management in 2020 and Beyond – Everything That You Need to Know
Machine Learning Model Management in 2020 and Beyond – Everything That You Need to Know Machine Learning Model Management in 2020 and Beyond – Everything That You Need to Know

Shortcomings of ad-hoc Machine Learning model developmentDoing ML model development without a management framework gets very complicated.

Managing the lifecycle of a Machine Learning model with MLopsMachine learning model management can be thought of as a part of a broader framework called MLOps.

EDITOR’S NOTECheck How to Monitor Machine Learning and Deep Learning Experiments and The Best Tools for Monitoring Machine Learning Experiments.

Machine Learning model management frameworksNow that we understand what machine learning model management is, let’s look at some popular model management frameworks.

If you’re looking for an easy entry into the world of machine learning model management, N…

1 неделя назад @ neptune.ai
Dive Into Football Analytics With TensorFlow Object Detection API
Dive Into Football Analytics With TensorFlow Object Detection API Dive Into Football Analytics With TensorFlow Object Detection API

Installation of TensorFlow Object Detection APIThe following information and steps demonstrate how to install the TensorFlow 2 object detection API while training on Colab.

Having installed the TensorFlow object detection API, the code below helps us to confirm that this API has been installed.

Setting up the object detection architectureThe desired object detection architecture for this problem is the EfficientDet.

, 'fine_tune_checkpoint: "{}"' .format(fine_tune_checkpoint), s) s = re.sub( '(input_path: ".*?)(PATH_TO_BE_CONFIGURED/train)(.*?")'

, 'input_path: "{}"' .format(train_record_fname), s) s = re.sub( '(input_path: ".*?)(PATH_TO_BE_CONFIGURED/val)(.*?")'

1 неделя, 3 дня назад @ neptune.ai
Image Classification: Tips and Tricks From 13 Kaggle Competitions (+ Tons of References)
Image Classification: Tips and Tricks From 13 Kaggle Competitions (+ Tons of References) Image Classification: Tips and Tricks From 13 Kaggle Competitions (+ Tons of References)

Machine learning and image classification is no different, and engineers can showcase best practices by taking part in competitions like Kaggle.

Usually, when a model performs great on training data but poorly on validation data, we call this condition overfitting.

The more hyperparameters you need to tune, the slower the process, so it’s good to select a minimum subset of model hyperparameters to tune.

Not all model hyperparameters are equally important.

One simple method to solve the problems stated above is to get more training data, because a model trained on more data will naturally generalize better.

1 неделя, 4 дня назад @ neptune.ai
TensorBoard vs Neptune: How Are They ACTUALLY Different
TensorBoard vs Neptune: How Are They ACTUALLY Different TensorBoard vs Neptune: How Are They ACTUALLY Different

Comparing Neptune with the Tensorboard UI, you would notice:The Neptune UI itself is much more clean and intuitive when compared to TensorBoard.

Notebook Checkpoints : we can cache any given state of a notebook onto the Neptune UI using the neptune-notebooks extension.

Don’t worry, you can use TensorBoard + Neptune integration to get the best of both tools.

TensorBoard + NeptuneWe have designed a neptune-tensorboard extension which will pick up experiment metrics directly from the TensorBoard output.

Actually, in the next post I’ll dive into more details and show you all you can get with TensorBoard + Neptune integration.

1 неделя, 5 дней назад @ neptune.ai
The Best Tools for Reinforcement Learning in Python You Actually Want to Try
The Best Tools for Reinforcement Learning in Python You Actually Want to Try The Best Tools for Reinforcement Learning in Python You Actually Want to Try

The fast development of RL has resulted in the growing demand for easy to understand and convenient to use RL tools.

Stable BaselinesStable Baselines is a set of improved implementations of Reinforcement Learning (RL) algorithms based on OpenAI Baselines.

It enables RL experiments providing classical RL algorithms and deep RL algorithms.

Final thoughtsIn this article, we have figured out what to look out for when choosing RL tools, what RL libraries are there, and what features they have.

Hopefully, with this information, you will have no problems choosing the RL library for your next project.

1 неделя, 6 дней назад @ neptune.ai
This Week in Machine Learning: Why ML Models Fail, AI & Plastic Waste, Vatican Library, and Bears
This Week in Machine Learning: Why ML Models Fail, AI & Plastic Waste, Vatican Library, and Bears This Week in Machine Learning: Why ML Models Fail, AI & Plastic Waste, Vatican Library, and Bears

Machine learning is fascinating.

Here are the best picks from the last two weeks from the world of the machine learning.

» Why 90 percent of all machine learning models never make it into production by Rhea Moutafis on Towards Data Science | NovemberQuite a concise piece on the difficulties with AI projects and why they fail.

» When Machine Learning Knows Too Much About You by Eric Siegel on KDnuggets | November 15Machine learning is great and brigs lots of good to our world.

» Old but gold, the reliable Reddit thread on ML for more news on machine learning.

2 недели назад @ neptune.ai
This Week in Machine Learning: Why ML Models Fail, AI & Plastic Waste, Vatican Library, and Bears
This Week in Machine Learning: Why ML Models Fail, AI & Plastic Waste, Vatican Library, and Bears This Week in Machine Learning: Why ML Models Fail, AI & Plastic Waste, Vatican Library, and Bears

Machine learning is fascinating.

Here are the best picks from the last two weeks from the world of the machine learning.

» Why 90 percent of all machine learning models never make it into production by Rhea Moutafis on Towards Data Science | NovemberQuite a concise piece on the difficulties with AI projects and why they fail.

» When Machine Learning Knows Too Much About You by Eric Siegel on KDnuggets | November 15Machine learning is great and brigs lots of good to our world.

» Old but gold, the reliable Reddit thread on ML for more news on machine learning.

2 недели назад @ neptune.ai
This Week in Machine Learning: Algorithms to Know in 2021, Underspecification, Saving Food, and Removing Bias in ML
This Week in Machine Learning: Algorithms to Know in 2021, Underspecification, Saving Food, and Removing Bias in ML This Week in Machine Learning: Algorithms to Know in 2021, Underspecification, Saving Food, and Removing Bias in ML

Here’s a dose of the latest news from the world of machine learning and AI.

» All Machine Learning Algorithms You Should Know in 2021 by Terence Shin on Towards Data Science | November 22A nice list of different machine learning algorithms that may turn out to be helpful for beginners or those who want to experiment with different algorithms.

👉 Here’s the original paper Underspecification Presents Challenges for Credibility in Modern Machine Learning» Know-How to Learn Machine Learning Algorithms Effectively by Shareef Shaik on KDnuggets | November 23The author shares his approach to actually learning algorithms beyond the surface because machine learning is more than just fit and predict m…

2 недели назад @ neptune.ai
pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know
pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know

Now we will learn how to use topic modeling and pyLDAvis to categorize tweets and visualize the results.

Topic coherence evaluates a single topic by measuring the degree of semantic similarity between high scoring words in the topic.

coherence_model_lda = CoherenceModel(model=lda_model, texts=tweets, dictionary=id2word, coherence= 'c_v' ) coherence_lda = coherence_model_lda.get_coherence() print( '\Coherence Score: ' , coherence_lda) Coherence Score: 0.3536443343685833This is our baseline.

mallet_path = 'patt/to/mallet-2.0.8/bin/mallet' ldamallet = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics= 20 , id2word=id2word) pprint(ldamallet.show_topics(formatted= False )) …

2 недели назад @ neptune.ai
Best Machine Learning as a Service Platforms (MLaaS) That You Want to Check as a Data Scientist
Best Machine Learning as a Service Platforms (MLaaS) That You Want to Check as a Data Scientist Best Machine Learning as a Service Platforms (MLaaS) That You Want to Check as a Data Scientist

AWS Machine Learning (ML) provides six machine learning solutions:SourceIt is a service that turns text into lifelike speech.

SourceAmazon Comprehend is a natural language processing(NLP) service that uses machine learning to find insights and relationships in a text.

This machine learning service provides four following suites for building machine learning models:This platform provides developers, data scientists, and data engineers to streamline their ML workflows.

SourceIBM Watson Machine Learning provides a wide range of tools and services so anyone can build, train, and deploy Machine Learning models.

SourceBigML provides a comprehensive machine learning platform that provides a wide r…

2 недели, 3 дня назад @ neptune.ai
▶️ YouTube
Henry AI Labs Henry AI Labs
последний пост 6 часов назад
AI Weekly Update - November 30th, 2020 (#22)
AI Weekly Update - November 30th, 2020 (#22) AI Weekly Update - November 30th, 2020 (#22)

Thank you for watching! Sorry for the hiatus with this series, hoping to get back into it! Paper Links:

High Performance NLP (Slideslive): https://slideslive.com/38940826/t3-high-performance-natural-language-processing

High Performance NLP (Slides): http://gabrielilharco.com/publications/EMNLP_2020_Tutorial__High_Performance_NLP.pdf

Efficient Transformers Survey: https://arxiv.org/abs/2009.06732

EMNLP 2020 Best Papers (can get to any of the papers from here): https://2020.emnlp.org/blog/2020-11-19-best-papers

Learning from Language Explanations: http://ai.stanford.edu/blog/learning-from-language/

HuggingFace Dataset Sprint: https://discuss.huggingface.co/t/open-to-the-community-one-week-tea…

6 часов назад @ youtube.com
Exploring Simple Siamese Representation Learning
Exploring Simple Siamese Representation Learning Exploring Simple Siamese Representation Learning

What makes contrastive learning work so well? This paper highlights the contribution of the Siamese architecture as a compliment to data augmentation and shows how Siamese nets + a stop-gradient operation in the negative encoder is all you need for strong contrastive self-supervised learning results. The paper also presents an interesting k-Means style explanation of the optimization problem contrastive self-supervised learning solves. Thanks for watching! Please Subscribe! Paper Links:

SimSiam: https://arxiv.org/pdf/2011.10566.pdf

SimCLR: https://arxiv.org/pdf/2002.05709.pdf

MoCo: https://arxiv.org/pdf/1911.05722.pdf

SwAV: https://arxiv.org/pdf/2006.09882.pdf

BYOL: https://arxiv.org/pdf/20…

5 дней, 8 часов назад @ youtube.com
Self-Training improves Pre-Training for Natural Language Understanding
Self-Training improves Pre-Training for Natural Language Understanding Self-Training improves Pre-Training for Natural Language Understanding

This video explains a new paper that shows benefits by Self-Training after Language Modeling to improve the performance of RoBERTa-Large. The paper goes on to show Self-Training gains in Knowledge Distillation and Few-Shot Learning as well. They also introduce an interesting unlabeled data filtering algorithm, SentAugment that improves performance and reduces the computational cost of this kind of self-training looping. Thanks for watching! Please Subscribe! Paper Links:

Paper Link: https://arxiv.org/pdf/2010.02194.pdf

Distributed Representations of Words and Phrases: https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf

Rethinking…

1 месяц назад @ youtube.com
Vokenization Explained!
Vokenization Explained! Vokenization Explained!

This video explains a new approach to Visually supervise Language models that achieves performance gains on Language-Only tasks like the GLUE benchmark and SQuAD question answering. This is done by constructing a token-image matching (vokens) and classifying corresponding tokens with a a weakly supervised loss function.

Thanks for watching! Please Subscribe! Paper Links:

Vokenization: https://arxiv.org/pdf/2010.06775.pdf

ImageBERT: https://arxiv.org/pdf/2001.07966.pdf

VilBERT: https://arxiv.org/pdf/1908.02265.pdf

LXMERT: https://arxiv.org/pdf/1908.07490.pdf

UNITER: https://arxiv.org/pdf/1909.11740.pdf

Visual Genome: https://visualgenome.org/

12-in-1: Multi-task Vision and Language Represent…

1 месяц, 1 неделя назад @ youtube.com
Small Language Models Are Also Few-Shot Learners
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This video explains the latest work in Pattern-Exploiting Training. This paper shows that this distillation scheme from knowledge captured in pre-trained language models to discriminative classifiers can also work in the Few-shot setting. This is compared directly with GPT-3's performance using 32 labeled examples for different tasks like BoolQ or Winograde Schema. This is very interesting, but not a fair, apples-to-apples, comparison with GPT-3. Thanks for watching! Please Subscribe! Paper Links:

Paper Link: https://arxiv.org/abs/2009.07118

First PET Paper: https://arxiv.org/pdf/2001.07676.pdf

Next Word Prediction Demo: https://github.com/renatoviolin/next_word_prediction

Hacker News React…

1 месяц, 2 недели назад @ youtube.com
Retrieval-Augmented Generation (RAG)
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This video explains the Retrieval-Augmented Generation (RAG) model! This approach combines Dense Passage Retrieval with a Seq2Seq BART generator. This is tested out on knowledge intensive tasks like open-domain QA, jeopardy question generation, and FEVER fact verification. This looks like a really interesting paradigm for building language models that produce factually accurate generations! Thanks for watching! Please Subscribe! Paper Links:

Original Paper: https://arxiv.org/pdf/2005.11401.pdf

FB Blog Post (Animation used in Intro): https://ai.facebook.com/blog/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models

HuggingFace RAG descript…

1 месяц, 3 недели назад @ youtube.com
Well-Read Students Learn Better
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3 месяца, 2 недели назад @ youtube.com
Easy Data Augmentation for Text Classification
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3 месяца, 3 недели назад @ youtube.com
Contrastive Learning for Unpaired Image-to-Image Translation
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3 месяца, 4 недели назад @ youtube.com
Data Augmentation using Pre-trained Transformer Models
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3 месяца, 4 недели назад @ youtube.com
Momentum Predictive Representations Explained!
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4 месяца, 1 неделя назад @ youtube.com
Distribution Augmentation for Generative Modeling
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4 месяца, 1 неделя назад @ youtube.com
Contrastive Clustering with SwAV
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4 месяца, 1 неделя назад @ youtube.com
Don't Stop Pretraining!
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4 месяца, 1 неделя назад @ youtube.com
CheckList Explained! (ACL 2020 Best Paper)
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4 месяца, 1 неделя назад @ youtube.com
Machine Learning and AI Academy Machine Learning and AI Academy
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4 месяца, 1 неделя назад @ youtube.com
Wasserstein Robust RL
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Random search with linear policies is as good as TRPO on Mujoco (in 2018)!
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4 месяца, 1 неделя назад @ youtube.com
Policy Gradients Reinforcement
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Variational Inference Lecture I|Probabilistic Modelling|Machine Learning
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9 месяцев, 3 недели назад @ youtube.com
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Start with part 1: https://youtu.be/X8jsijhllIA

Ben Eater implementing Hamming codes on breadboards: https://youtu.be/h0jloehRKas

Brought to you by you: https://3b1b.co/thanks ------------------ These animations are largely made using manim, a scrappy open-source python library: https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and it has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Stream the music on Spotify: https://open.spotify.com…

2 месяца, 3 недели назад @ youtube.com
Hamming codes, h■w to ov■rco■e n■ise.
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A discovery-oriented introduction to error correction codes.

Part 2: https://youtu.be/b3NxrZOu_CE

Ben Eater:'s take: https://youtu.be/h0jloehRKas

Brought to you by you: https://3b1b.co/thanks You can read Hamming's own perspective on his discovery of these codes in chapter 12 of "The Art of Doing Science and Engineering".

https://amzn.to/3lwcnmh ------------------ These animations are largely made using manim, a scrappy open-source python library: https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and it has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by…

2 месяца, 3 недели назад @ youtube.com
Group theory and why I love 808,017,424,794,512,875,886,459,904,961,710,757,005,754,368,000,000,000
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3 месяца, 1 неделя назад @ youtube.com
The impossible chessboard puzzle
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4 месяца, 4 недели назад @ youtube.com
Tips to be a better problem solver [Last lecture] | Lockdown math ep. 10
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6 месяцев, 1 неделя назад @ youtube.com
Intuition for i to the power i | Lockdown math ep. 9
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6 месяцев, 2 недели назад @ youtube.com
Does contact tracing necessarily sacrifice privacy? (via Nicky Case)
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6 месяцев, 2 недели назад @ youtube.com
The power tower puzzle | Lockdown math ep. 8
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A fun puzzle stemming from repeated exponentiation.

Full playlist: https://www.youtube.com/playlist?list=PLZHQObOWTQDP5CVelJJ1bNDouqrAhVPev

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/ldm-thanks Notes by Ngân Vũ:

https://twitter.com/ThuyNganVu/status/1261014161464516608?s=20 Play along on Desmos:

https://www.desmos.com/calculator/nul32eaaa9 Related videos.

Calculus series:

https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr In particular look at:

https://youtu.be/CfW845LNObM Numberphile on Grahm's constant:

https://youtu.be/XTeJ64KD5cg ------------------

Video timeline (thanks to user "noonesperfect")

0:36 Question 1

1:13 Answer 1

1:29 …

6 месяцев, 3 недели назад @ youtube.com
What makes the natural log "natural"? | Lockdown math ep. 7
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6 месяцев, 3 недели назад @ youtube.com
Logarithm Fundamentals | Lockdown math ep. 6
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6 месяцев, 4 недели назад @ youtube.com
Imaginary interest rates | Lockdown math ep. 5
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7 месяцев назад @ youtube.com
What is Euler's formula actually saying? | Lockdown math ep. 4
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7 месяцев назад @ youtube.com
Complex number fundamentals | Lockdown math ep. 3
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7 месяцев, 1 неделя назад @ youtube.com
Trigonometry fundamentals | Lockdown math ep. 2
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7 месяцев, 1 неделя назад @ youtube.com
The simpler quadratic formula | Lockdown math ep. 1
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7 месяцев, 2 недели назад @ youtube.com
Two Minute Papers Two Minute Papers
последний пост 2 дня, 8 часов назад
Remember, This Meeting Never Happened! 🚶🚶‍♀️
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❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their report on this exact paper is available here: https://wandb.ai/wandb/retiming-video/reports/Retiming-Instances-in-a-Video--VmlldzozMzUwNTk 📝 The paper "Layered Neural Rendering for Retiming People in Video" is available here:

https://retiming.github.io/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, J…

2 дня, 8 часов назад @ youtube.com
AI-Based Sky Replacement Is Here! 🌓
AI-Based Sky Replacement Is Here! 🌓 AI-Based Sky Replacement Is Here! 🌓

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their report on this paper is available here: https://wandb.ai/wandb/skyAR/reports/The-Sky-Is-In-Our-Grasp---VmlldzozMjY0NDI 📝 The paper "Castle in the Sky: Dynamic Sky Replacement and Harmonization in Videos" is available here:

https://jiupinjia.github.io/skyar/ ☀️The mentioned free light transport course is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, …

6 дней, 3 часа назад @ youtube.com
Near-Perfect Virtual Hands For Virtual Reality! 👐
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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "MEgATrack: Monochrome Egocentric Articulated Hand-Tracking for Virtual Reality" is available here:

https://research.fb.com/publications/megatrack-monochrome-egocentric-articulated-hand-tracking-for-virtual-reality/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, B…

1 неделя, 2 дня назад @ youtube.com
Is Videoconferencing With Smart Glasses Possible? 👓
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❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/wandb/egocentric-video-conferencing/reports/Overview-Egocentric-Videoconferencing--VmlldzozMTY1NTA 📝 The paper "Egocentric Videoconferencing" is available here:

http://gvv.mpi-inf.mpg.de/projects/EgoChat/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Javier Bustamante, Joshua Goller, Lorin Atzbe…

1 неделя, 6 дней назад @ youtube.com
This AI Makes Puzzle Solving Look Easy! 🧩
This AI Makes Puzzle Solving Look Easy! 🧩 This AI Makes Puzzle Solving Look Easy! 🧩

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://app.wandb.ai/lavanyashukla/visualize-sklearn/reports/Visualize-Sklearn-Model-Performance--Vmlldzo0ODIzNg 📝 The paper "C-Space Tunnel Discovery for Puzzle Path Planning" is available here:

https://xinyazhang.gitlab.io/puzzletunneldiscovery/

https://github.com/xinyazhang/PuzzleTunnelDiscovery 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Ma…

2 недели, 2 дня назад @ youtube.com
Making Talking Memes With Voice DeepFakes!
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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Wav2Lip: Accurately Lip-syncing Videos In The Wild" is available here:

- Paper: https://arxiv.org/abs/2008.10010

- Try it out! - https://github.com/Rudrabha/Wav2Lip More results are available on our Instagram page! - https://www.instagram.com/twominutepapers/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew M…

2 недели, 6 дней назад @ youtube.com
Colorizing Strawberries is Hard…But Not For This AI! 🍓
Colorizing Strawberries is Hard…But Not For This AI! 🍓 Colorizing Strawberries is Hard…But Not For This AI! 🍓

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their report on this exact paper is available here: https://wandb.ai/wandb/instacolorization/reports/Overview-Instance-Aware-Image-Colorization---VmlldzoyOTk3MDI 📝 The paper "Instance-aware Image Colorization" is available here:

https://ericsujw.github.io/InstColorization/ User study results:

https://cgv.cs.nthu.edu.tw/InstColorization_data/Supplemental_Material/user_study_result.html DeOldify: https://github.com/jantic/DeOldify

Follow them on Twitter for more! - https://twitter.com/deoldify 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Ma…

3 недели, 2 дня назад @ youtube.com
This AI Creates A 3D Model of You! 🚶‍♀️
This AI Creates A 3D Model of You! 🚶‍♀️ This AI Creates A 3D Model of You! 🚶‍♀️

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://app.wandb.ai/stacey/deep-drive/reports/Image-Masks-for-Semantic-Segmentation--Vmlldzo4MTUwMw 📝 The paper "PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization" is available here:

https://shunsukesaito.github.io/PIFuHD/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic,…

3 недели, 6 дней назад @ youtube.com
Simulating Dragons Under Cloth Sheets! 🐲
Simulating Dragons Under Cloth Sheets! 🐲 Simulating Dragons Under Cloth Sheets! 🐲

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://app.wandb.ai/stacey/droughtwatch/reports/Drought-Watch-Benchmark-Progress--Vmlldzo3ODQ3OQ 📝 The paper "Local Optimization for Robust Signed Distance Field Collision" is available here:

https://mmacklin.com/sdfcontact.pdf 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

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1 месяц назад @ youtube.com
Finally, Deformation Simulation... in Real Time! 🚗
Finally, Deformation Simulation... in Real Time! 🚗 Finally, Deformation Simulation... in Real Time! 🚗

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their report about a previous paper is available here: https://app.wandb.ai/stacey/stargan/reports/Cute-Animals-and-Post-Modern-Style-Transfer%3A-Stargan-V2-for-Multi-Domain-Image-Synthesis---VmlldzoxNzcwODQ 📝 The paper "Detailed Rigid Body Simulation with Extended Position Based Dynamics" is available here:

https://matthias-research.github.io/pages/publications/PBDBodies.pdf Wish to see and hear the sound synthesis paper?

- Our video: https://www.youtube.com/watch?v=rskdLEl05KI

- Paper: https://research.cs.cornell.edu/Sound/mc/ 🙏 We would like to thank our generous Patreon supporters who make Tw…

1 месяц назад @ youtube.com
Beautiful Elastic Simulations, Now Much Faster!
Beautiful Elastic Simulations, Now Much Faster! Beautiful Elastic Simulations, Now Much Faster!

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://app.wandb.ai/safijari/dqn-tutorial/reports/Deep-Q-Networks-with-the-Cartpole-Environment--Vmlldzo4MDc2MQ 📝 The paper "IQ-MPM: An Interface Quadrature Material Point Method for Non-sticky Strongly Two-Way Coupled Nonlinear Solids and Fluids" is available here:

https://yzhu.io/publication/mpmcoupling2020siggraph/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Hadd…

1 месяц, 1 неделя назад @ youtube.com
This AI Creates An Adorable Baby DiCaprio Image!
This AI Creates An Adorable Baby DiCaprio Image! This AI Creates An Adorable Baby DiCaprio Image!

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their report for this paper is available here: https://wandb.ai/wandb/in-domain-gan/reports/In-Domain-GAN-Inversion--VmlldzoyODE5Mzk 📝 The paper "In-Domain GAN Inversion for Real Image Editing" is available here:

https://genforce.github.io/idinvert/ Check out the research group's other works, there is lots of cool stuff there:

https://genforce.github.io/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, …

1 месяц, 1 неделя назад @ youtube.com
This Is What Simulating a 100 Million Particles Looks Like!
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❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned instrumentation is available here: https://app.wandb.ai/stacey/sfmlearner/reports/See-3D-from-Video%3A-Depth-Perception-for-Self-Driving-Cars--Vmlldzo2Nzg2Nw 📝 The paper "A Massively Parallel and Scalable Multi-GPU Material Point Method " is available here:

https://sites.google.com/view/siggraph2020-multigpu 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon C…

1 месяц, 2 недели назад @ youtube.com
Remove This! ✂️ AI-Based Video Completion is Amazing!
Remove This! ✂️ AI-Based Video Completion is Amazing! Remove This! ✂️ AI-Based Video Completion is Amazing!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Flow-edge Guided Video Completion" is available here:

http://chengao.vision/FGVC/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Sunil Kim, Taras Bobrovytsky, Thomas Krcmar…

1 месяц, 2 недели назад @ youtube.com
Enhance! Neural Supersampling is Here! 🔎
Enhance! Neural Supersampling is Here! 🔎 Enhance! Neural Supersampling is Here! 🔎

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://www.wandb.com/articles/code-comparer 📝 The paper "Neural Supersampling for Real-time Rendering" is available here:

https://research.fb.com/blog/2020/07/introducing-neural-supersampling-for-real-time-rendering/

https://research.fb.com/publications/neural-supersampling-for-real-time-rendering/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric M…

1 месяц, 3 недели назад @ youtube.com
DataFest Video DataFest Video
последний пост 1 неделя, 5 дней назад
Mikhail Druzhinin: Open Data Science Open Source. Albumentations
Mikhail Druzhinin: Open Data Science Open Source. Albumentations Mikhail Druzhinin: Open Data Science Open Source. Albumentations

Data Fest Online 2020

Open Data Science Open Source track https://ods.ai/tracks/open-sourse-df2020 Project links: https://github.com/albumentations-team/albumentations Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

1 неделя, 5 дней назад @ youtube.com
Dmitry Petrov: Open Data Science Open Source. DVC & CML
Dmitry Petrov: Open Data Science Open Source. DVC & CML Dmitry Petrov: Open Data Science Open Source. DVC & CML

Data Fest Online 2020

Open Data Science Open Source https://ods.ai/tracks/open-sourse-df2020 Project links: https://dvc.org https://github.com/iterative/dvc Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

1 неделя, 5 дней назад @ youtube.com
Stepan Kudin: Reconstruction of the dental crown from single image
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Data Fest Online 2020 https://fest.ai/2020/

ML in Healthcare track https://ods.ai/tracks/ml-in-healthcare-df2020 Speaker: Stepan Kudin, Adalisk Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

1 неделя, 6 дней назад @ youtube.com
Maksim Sharaev: AI for biomedical tasks: trustworthy datasets and labeling
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Data Fest Online 2020

ML in Healthcare track https://ods.ai/tracks/ml-in-healthcare-df2020 Speaker: Dr. Maksim Sharaev, Research Scientist, Skoltech Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

1 неделя, 6 дней назад @ youtube.com
ML in Healthcare Track Premiere
ML in Healthcare Track Premiere ML in Healthcare Track Premiere

Data Fest Online 2020

ML in Healthcare track https://ods.ai/tracks/ml-in-healthcare-df2020 Speaker: Dr. Maksim Sharaev, Research Scientist, Skoltech Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

1 неделя, 6 дней назад @ youtube.com
Sergey Plis: Machine learning and neuroimaging
Sergey Plis: Machine learning and neuroimaging Sergey Plis: Machine learning and neuroimaging

Data Fest Online 2020

ML in Healthcare track https://ods.ai/tracks/ml-in-healthcare-df2020 Speaker: Prof. Sergey Plis Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

1 неделя, 6 дней назад @ youtube.com
Alexey Grigirev: + Counting - Machine Learning
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Data Fest Online 2020

DS Minus ML track https://ods.ai/tracks/ds-ml-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

2 недели назад @ youtube.com
Andrey Lukyanenko: Metro Analysis and Visualization
Andrey Lukyanenko: Metro Analysis and Visualization Andrey Lukyanenko: Metro Analysis and Visualization

Data Fest Online 2020

DS Minus ML Track: https://ods.ai/tracks/ds-ml-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

2 недели назад @ youtube.com
Roman Bunin: Style guide for dashboards
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Data Fest Online 2020

DS minus ML https://ods.ai/tracks/ds-ml-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

2 недели назад @ youtube.com
Yulia Ibragimova: Stories Told by Data or Deep Impact without Deep Learning
Yulia Ibragimova: Stories Told by Data or Deep Impact without Deep Learning Yulia Ibragimova: Stories Told by Data or Deep Impact without Deep Learning

Data Fest Online 2020

DS Minus ML Track: https://ods.ai/tracks/ds-ml-df2020 We will speak about data applications for PR and marketing activities: I’ll share some cases which prove that sometimes you just need simple and relatively small data to make a great impact. I’ll tell you how correlation can help you to increase brand awareness and why you still need people who know maths and Python for that. We’ll discuss how new look at the old data may lead to extra effectiveness, and how numbers help to write a good story. Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

2 недели назад @ youtube.com
Data Fest Online 2020 DS minus ML Track Premiere
Data Fest Online 2020 DS minus ML Track Premiere Data Fest Online 2020 DS minus ML Track Premiere

Data Fest Online 2020

DS minus ML https://ods.ai/tracks/ds-ml-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

2 недели назад @ youtube.com
Eduard Grigoryan: Metric optimization for Quality Control of A/B testing
Eduard Grigoryan: Metric optimization for Quality Control of A/B testing Eduard Grigoryan: Metric optimization for Quality Control of A/B testing

Data Fest Online 2020

A/B Testing track https://ods.ai/tracks/ab-testing-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

2 недели, 3 дня назад @ youtube.com
Nerses Bagiyan: Peeking at A/B Tests
Nerses Bagiyan: Peeking at A/B Tests Nerses Bagiyan: Peeking at A/B Tests

Data Fest Online 2020

A/B Testing track https://ods.ai/tracks/ab-testing-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

2 недели, 3 дня назад @ youtube.com
Alexander Sakhnov: Geometric interpretation of variance reduction methods on the example of CUPED
Alexander Sakhnov: Geometric interpretation of variance reduction methods on the example of CUPED Alexander Sakhnov: Geometric interpretation of variance reduction methods on the example of CUPED

Data Fest Online 2020

A/B Testing track https://ods.ai/tracks/ab-testing-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

2 недели, 3 дня назад @ youtube.com
Andrey Malinin: Uncertainty in Speech Recognition and Machine Translation
Andrey Malinin: Uncertainty in Speech Recognition and Machine Translation Andrey Malinin: Uncertainty in Speech Recognition and Machine Translation

Data Fest Online 2020

Uncertainty Estimation in ML track https://ods.ai/tracks/uncertainty-estimation-in-ml-df2020 Speaker: Andrey Malinin, Yandex & HSE This video is more heavily focused towards research. Here we introduce ensemble-based uncertainty estimation for autoregressive structured prediction models. We discuss ensemble combination and the derivation of token-level and sequence-level measures of total and knowledge uncertainty. We evaluate the proposed approach on the tasks of sequence and token level error detection and out-of-domain input detection. Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

2 недели, 5 дней назад @ youtube.com
Семинары JetBrains Research Семинары JetBrains Research
последний пост 13 часов назад
Мультилейбльная классификация биомедицинских текстов
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Обработка естественного языка (NLP) – одно из наиболее активно развивающихся направлений машинного обучения. Одним из его применений в сфере биомедицины является классификация медицинских текстов по ICD-лейблам (International Classification of Diseases). Присваивание лейблов вручную – сложная задача, требующая больших временных затрат и повышенного внимания. В то же время она является особенно важной для поддержания баз данных, а также создания единого стандартизованного языка для обмена медицинскими текстами по всему миру. Авторы статьи «Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes» (2020) ставят своей целью решить эту задачу посредством алгоритмов машинного об…

13 часов назад @ youtube.com
Closing the Reality Gap in Sim2Real
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Зачастую большинство RL-алгоритмов обучается в специально созданной для них виртуальной среде. Однако, при таком подходе возникают немалые сложности при попытке использования этих же самых агентов в реальном мире. Данная проблема носит название The Reality Gap. В ходе семинара будет небольшое введение в рассматриваемую область, а также проведен обзор некоторых ключевых работ в данной тематике. Помимо этого будет затронута тема создания беспилотной системы, способной ездить в реальном мире, но обученной целиком в симуляторе. Рассматриваемые статьи: * Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World * Using Simulation and Domain Adaptation to Improv…

1 день, 13 часов назад @ youtube.com
EfficientDet: Scalable and Efficient Object Detection
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Object Detection является одной из задач компьютерного зрения. На данный момент известно много моделей достаточно точно решающих данную задачу. Однако большинство из них не оптимальны с точки зрения используемых ресурсов и времени, что зачастую препятствует их применению. На семинаре будет рассмотрена современная модель детектирования объектов EfficientDet. Данная модель интересна тем, что в ней была использована weighted bi-directional feature pyramid network (BiFPN), которая позволяет эффективно обрабатывать карты признаков разного масштаба. Также рассмотрим Compound Scaling – способ масштабирования всех составляющих модели с помощью увеличения глубины, ширины и разрешения нейросетей. Бол…

1 неделя, 1 день назад @ youtube.com
Семантическая сегментация медицинских изображений
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*не записались первые несколько предложений доклада, надеемся, это не сильно помешает общему восприятию. Семантическая сегментация применяется к медицинским визуализациям (например МРТ) для определения точного местоположения и формы структур тела и имеет важное значение для обнаружения аномалий и их успешного лечения. С задачей семантической сегментации достаточно хорошо справляются глубокие сверточные сети, однако для их обучения необходима большая база размеченных данных. Это ограничение особенно важно при сегментации медицинских изображений, для которых разметка требует много времени. Авторы статьи “Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning”…

1 неделя, 2 дня назад @ youtube.com
RL in music: accomponement and music generation using reingorcement learning approach
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Зачастую в задаче генерации музыки используются лишь различные модификации сверточных или рекуррентных сетей. Однако зачастую музыка при таком подходе получается довольно однообразной и может звучать ненатурально. Может ли помочь RL решить эту проблему? Как вообще сформулировать задачу генерации музыки, чтобы использовать в ней RL? В ходе семинара будут рассмотрены статьи Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach, RL-Duet: Online Music Accompaniment Generation using Deep Reinforcement Learning. Также будут рассмотрены статьи, где обучение агента RL проходит в качестве дополнительного шага после обучения обычной рекуррентной сети. Такой подход может пригодиться…

1 неделя, 3 дня назад @ youtube.com
Использование спутниковых снимков в изучении экономического благосостояния
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Спутниковые снимки являются недорогим и очень богатым источником информации. Их анализ оказывается полезным во многих прикладных задачах, для решения которых нередко используются свёрточные нейронные сети. Среди таких задач, например, обнаружение стихийных бедствий, поиск пропавших людей, изучение дорожного трафика, улучшение карт при помощи сегментации объектов и предсказание экономических показателей. В каждой из них важно понимать принципы работы со спутниковыми снимками: откуда их взять, как их обработать и как их использовать для решения задачи. На этом семинаре мы рассмотрим вышеупомянутые особенности работы со спутниковыми снимками. Кроме того, чтобы продемонстрировать специфику рабо…

2 недели назад @ youtube.com
Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess
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Человечество на протяжении многих веков не только играет в шахматы, но также активно изучает их. За годы тренировок мастерство шахматистов стало измеряться, скорее, не их умением быстро анализировать ситуацию, а пониманием, когда и какие заранее заготовленные приёмы нужно применить. Поэтому постоянно предлагаются новые варианты шахмат. Один из самых популярных -- шахматы Фишера, в которых порядок фигур на доске выбирается случайно среди 960 комбинаций. Компьютерный анализ таких вариаций пока слишком сложен, но простые модификации исследовать вполне реально. Авторы статьи, которую мы обсудим, выбрали 10 простых модификаций правил, для каждой обучается модель AlphaZero, отлично показавшая себ…

2 недели, 2 дня назад @ youtube.com
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
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COVID-19 за прошедшие полгода стал очень хорошим источником интересных задач для всех областей науки - от статистики до юриспруденции. Некоторые из этих задач буквально являются жизненно важными. Например, ускорение диагностирование COVID-19 по снимкам КТ методами машинного обучения. Ввиду специфичности области, авторам статьи "Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans" пришлось не просто провести исследование по поиску оптимальной модели. Результатом их работы также стал крупнейший датасет для решения задачи этой области и метод Self-Trans, призванный повысить эффективность transfer learning'a путем интеграции в него самообучения. На данном семинаре мы подробн…

3 недели, 1 день назад @ youtube.com
Multilingual end-to-end speech translation
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На данный момент системы по распознаванию голоса уже достаточно хорошо развиты, качество машинного перевода становится все лучше и лучше, поэтому начинают внедрять новые модели, способные переводить аудио в текст на несколько языков одновременно. На семинаре будет разобрана статья, включающая модели перевода «один ко многим» и «многие ко многим». Помимо этого, слушателям будут представлены базовые компоненты модели перевода аудио в текст на другом языке: модель автоматического распознавание речи и модель машинного перевода. Будут разобраны некоторые успешные решения в области распознавания речи. Также будут освещены основные метрики оценивания качества вышеупомянутых моделей. Докладчик: Але…

3 недели, 3 дня назад @ youtube.com
Multi-agent Social Reinforcement Learning Improves Generalization
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Социальное обучение - это обучение путем наблюдения за поведением других агентов в среде. Такой метод обучения используется людьми и животными, и позволяет открывать полезные модели поведения, которые было бы трудно получить только лишь по собственному опыту. Так же наблюдение за другими помогает быстро адаптироваться к новым обстоятельствам. Что если идею социального обучения использовать для обучения RL-агентов? Во многих реальных средах, где мы бы хотели использовать RL-агентов (автопилоты, роботы), индивидуальное обучение агента дорогостоящее, неэффективное, и может быть очень не безопасным. Однако эти среды заполнены людьми-экспертами, которые знают, как выполнять задачи (вождение авто…

4 недели, 1 день назад @ youtube.com
Graph Representation Learning and Beyond
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Работа с графами продолжает оставаться важной темой современных исследований в области машинного обучения. Многие конференции представляют исследования, сделанные в этом направлении. На данном семинаре мы рассмотрим несколько статей из воркшопа Graph Representation Learning and Beyond конференции ICML 2020. Докладчик: Нина Лукашина. Слайды: https://drive.google.com/file/d/12ZUTy0X6RewSgU4or291gGoPWfEITfl7/view?usp=sharing

1 месяц назад @ youtube.com
ML + SE - Source Code
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Большая часть исследований в нашей широкой области использования данных для улучшения инструментов и процессов разработки связана с процессом написания и поддержки кода. Это неудивительно, поскольку код играет далеко не последнюю роль в процессе производства и поддержки ПО. Однако, в последнее время, с массовой миграцией методов из NLP в нашу область, перекос в сторону методов работы с кодом становится всё более заметен. В кино и литературе прижился образ программиста, проводящего весь рабочий день перед окошком редактора с кодом. Однако, согласно множеству исследований, в среднем разработчики работают с кодом не больше половины рабочего времени, и тратят большую часть оставшегося времени н…

1 месяц назад @ youtube.com
Contrastive Learning for Dreamer
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Dreamer — современный model-based метод обучения с подкреплением, позволяющий значительно сократить взаимодействие со средой при обучении. Важной его составляющей является модель, сжимающая наблюдение получаемое в виде изображения в небольшое латентное представление. Такая модель обычно реализуется в виде вариационного автокодировщика. Однако подход основаный на автокодировщике часто приводит к исчезновению маленьких, но важных при обучении объектов. В недавней статье был предложен альтернативный способ получения латентного представления, основанный на constrastive learning. На семинаре мы разберем предложенное решение и обсудим полученные результаты. Докладчик: Константин Махнев. Слайды: h…

1 месяц назад @ youtube.com
Автоматическая генерация подсказок для решения задач по программированию
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Решение задач является неотъемлемой частью процесса обучения программирования, и, как показывают исследования, именно с ней у школьников и студентов возникают наибольшие трудности. Однако иногда ресурсов преподавателя не хватает на оперативную помощь, или, в случае онлайн-курсов, преподаватели вообще отсутствуют и подсказок ждать неоткуда. Для таких случаев важна автоматическая генерация подсказок, способных заменить помощь эксперта. На следующем семинаре мы расскажем о нашем проекте по автоматической генерации подсказок для языка Python, существующих подходах в этой области, процессе сбора необходимых данных и их анализе. Докладчики Алёна Люлина и Анастасия Бирилло. Слайды: https://drive.g…

1 месяц, 1 неделя назад @ youtube.com
Предсказание липофильности при помощи молекулярных подструктур и multitask обучения
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Липофильность - свойство молекулы, влияющее на ее способность пересекать клеточные мембраны внутри организма. Поэтому предсказание липофильности важно в процессе разработки лекарств. Липофильность может быть представлена в виде двух числовых величин - logP и logD. LogP описывает липофильность неионизируемых и нейтральных форм ионизированных молекул. Для заряженных молекул липофильность представляется в виде LogD. Поскольку оба эти дескриптора относятся к одному химическому свойству молекулы, можно предсказывать их параллельно. На данный момент наиболее эффективными подходами для предсказания молекулярных свойств методами глубокого обучения являются графовые нейросети. Несмотря на общий прог…

1 месяц, 1 неделя назад @ youtube.com
Яндекс. Компьютерные науки Яндекс. Компьютерные науки
последний пост 5 месяцев, 4 недели назад
Программирование ретрокомпьютеров: сборка демо
Программирование ретрокомпьютеров: сборка демо

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5 месяцев, 4 недели назад @ youtube.com
Программирование ретрокомпьютеров: визуальные эффекты. Часть 4
Программирование ретрокомпьютеров: визуальные эффекты. Часть 4

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6 месяцев назад @ youtube.com
Программирование ретрокомпьютеров: визуальные эффекты. Часть 3
Программирование ретрокомпьютеров: визуальные эффекты. Часть 3

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6 месяцев, 1 неделя назад @ youtube.com
Программирование ретрокомпьютеров: визуальные эффекты. Часть 2
Программирование ретрокомпьютеров: визуальные эффекты. Часть 2

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6 месяцев, 2 недели назад @ youtube.com
Программирование ретрокомпьютеров: визуальные эффекты. Часть 1
Программирование ретрокомпьютеров: визуальные эффекты. Часть 1

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6 месяцев, 3 недели назад @ youtube.com
Машинное обучение. Нейронные сети и градиентные методы. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Нейронные сети и градиентные методы. К.В. Воронцов, Школа анализа данных, Яндекс.

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7 месяцев назад @ youtube.com
Машинное обучение. Заключительная лекция. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Заключительная лекция. К.В. Воронцов, Школа анализа данных, Яндекс.

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7 месяцев назад @ youtube.com
Машинное обучение. Активное обучение. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Активное обучение. К.В. Воронцов, Школа анализа данных, Яндекс.

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7 месяцев назад @ youtube.com
Машинное обучение. Обучение с подкреплением. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Обучение с подкреплением. К.В. Воронцов, Школа анализа данных, Яндекс.

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7 месяцев назад @ youtube.com
Машинное обучение. Тематическое моделирование. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Тематическое моделирование. К.В. Воронцов, Школа анализа данных, Яндекс.

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7 месяцев назад @ youtube.com
Машинное обучение. Рекомендательные системы. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Рекомендательные системы. К.В. Воронцов, Школа анализа данных, Яндекс.

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7 месяцев назад @ youtube.com
Машинное обучение. Обучение ранжированию. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Обучение ранжированию. К.В. Воронцов, Школа анализа данных, Яндекс.

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7 месяцев назад @ youtube.com
Машинное обучение. Композиции классификаторов, часть 2. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Композиции классификаторов, часть 2. К.В. Воронцов, Школа анализа данных, Яндекс.

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7 месяцев назад @ youtube.com
Машинное обучение. Линейные композиции, бустинг. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Линейные композиции, бустинг. К.В. Воронцов, Школа анализа данных, Яндекс.

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7 месяцев назад @ youtube.com
Машинное обучение. Нейронные сети глубокого обучения. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Нейронные сети глубокого обучения. К.В. Воронцов, Школа анализа данных, Яндекс.

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7 месяцев назад @ youtube.com
ML Trainings ML Trainings
последний пост 4 дня, 2 часа назад
MTS Your First RecSys Track Teaser
MTS Your First RecSys Track Teaser MTS Your First RecSys Track Teaser

За 5 занятий с Даниилом Потаповым, руководителем группы персонализации и рекомендательных систем Центра Big Data МТС, разберетесь, как правильно поставить задачу, какие данные нужно собирать, освоите полезные приемы, попробуете популярные фреймворки для построения рекомендательных систем, создадите собственный прототип и узнаете, как довести его до продакшена. В этом вам сильно поможет знание математики на уровне 2 курса технического вуза и базовые навыки программирования на Python. Однако и с этим можно будет освоиться параллельно с прохождением занятий. Страница трека "МТС. Your first recsys. Ваша первая рекомендательная система с нуля."

https://ods.ai/tracks/mts-recsys-df2020

4 дня, 2 часа назад @ youtube.com
MTS NLP Track Teaser
MTS NLP Track Teaser MTS NLP Track Teaser

В треке "МТС. Жизненный цикл решения на базе NLP" расскажем из чего состоит жизненный цикл решения на базе NLP. Как довести гипотезу до продуктивной среды и сделать так, чтобы поддержка этого решения не съедала все свободное время. Страница трека https://ods.ai/tracks/mts-df2020

4 дня, 2 часа назад @ youtube.com
MTS Your first recsys - 5
MTS Your first recsys - 5 MTS Your first recsys - 5

5. Более продвинутые методы анализа данных и моделей в рекомендациях - Даниил Потапов В этом видео разберем самые частые вопросы бизнеса к рекомендациям и научимся на них отвечать. Также обсудим, как наши пайплайны, которые мы создали, можно улучшать и оптимизировать. Остальные видео доступны на странице трека "MTC. Your first recsys. Ваша первая рекомендательная система с нуля."

https://ods.ai/tracks/mts-recsys-df2020

5 дней назад @ youtube.com
MTS Your first recsys - 3
MTS Your first recsys - 3 MTS Your first recsys - 3

3. Фильтрация на основе контента и коллаборативная фильтрация - Даниил Потапов В этом видео изучим одни из самых популярных подходов в построении рекомендательных систем – контентную и коллаборативную фильтрации.

Научимся их применять на примере двух популярных библиотек на Python: Implicit и LightFM. Остальные видео доступны на странице трека "MTC. Your first recsys. Ваша первая рекомендательная система с нуля."

https://ods.ai/tracks/mts-recsys-df2020

5 дней назад @ youtube.com
MTS Your first recsys - 4
MTS Your first recsys - 4 MTS Your first recsys - 4

4. Градиентный бустинг на деревьях и задача реранжирования - Даниил Потапов В этом видео вы научитесь применять к построению рекомендаций один из самых известных алгоритмов в машинном обучении – градиентный бустинг на деревьях. Вместе разберем, как собирать датасет, как обучать модель и какие подводные камни нас могут поджидать на этом пути. Остальные видео доступны на странице трека "MTC. Your first recsys. Ваша первая рекомендательная система с нуля."

https://ods.ai/tracks/mts-recsys-df2020

5 дней назад @ youtube.com
MTS Your first recsys - 2
MTS Your first recsys - 2 MTS Your first recsys - 2

2. Методы валидации, метрики и бейзлайны - Даниил Потапов В этом видео вы узнаете, какие бывают метрики при построении рекомендательных систем и как валидировать модели. Также рассмотрим построение самых простых моделей, или бейзлайнов - то, с чего надо начинать любую задачу в машинном обучении. Остальные видео доступны на странице трека "MTC. Your first recsys. Ваша первая рекомендательная система с нуля."

https://ods.ai/tracks/mts-recsys-df2020

5 дней назад @ youtube.com
MTS Your first recsys - 1
MTS Your first recsys - 1 MTS Your first recsys - 1

1. Введение в рекомендательные системы - Даниил Потапов В этом видео вы узнаете базовую теорию по построению рекомендательных систем: постановка задачи, типы систем и какие данные для этого нужны. Остальные видео доступны на странице трека "MTC. Your first recsys. Ваша первая рекомендательная система с нуля."

https://ods.ai/tracks/mts-recsys-df2020

5 дней назад @ youtube.com
Data Fest Online 2020: как это было
Data Fest Online 2020: как это было Data Fest Online 2020: как это было

Data Fest Online 2020 https://datafest.ru/2020/

Треки https://ods.ai/tracks Посмотреть эфир и список треков и организаторов https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам https://ods.ai/events/datafest2020

Вступить в сообщество https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

1 неделя, 4 дня назад @ youtube.com
Mikhail Burtsev: Deep Pavlov
Mikhail Burtsev: Deep Pavlov Mikhail Burtsev: Deep Pavlov

Data Fest Online 2020

Open Data Science Open Source track https://ods.ai/tracks/open-sourse-df2020 Project links: https://github.com/deepmipt/DeepPavlov Посмотреть эфир и список треков и организаторов https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам https://ods.ai/events/datafest2020

Вступить в сообщество https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

1 неделя, 5 дней назад @ youtube.com
Дмитрий Петров: Как построить коммерческий open source проект
Дмитрий Петров: Как построить коммерческий open source проект Дмитрий Петров: Как построить коммерческий open source проект

Data Fest Online 2020

Open Data Science Open Source track https://ods.ai/tracks/open-sourse-df2020 Интервью с Дмитрием Петровым (DVC, iterative.ai) Дмитрий рассказывает о том, как появился проект DVC и компания iterative, а так же делится своими мыслями о том, как построить бизнес вокруг open source проекта. Посмотреть эфир и список треков и организаторов https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам https://ods.ai/events/datafest2020

Вступить в сообщество https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

1 неделя, 5 дней назад @ youtube.com
Data Fest Open Data Science Open Source Track Premiere
Data Fest Open Data Science Open Source Track Premiere Data Fest Open Data Science Open Source Track Premiere

Data Fest Online 2020

Open Data Science Open Source track https://ods.ai/tracks/open-sourse-df2020 Посмотреть эфир и список треков и организаторов https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам https://ods.ai/events/datafest2020

Вступить в сообщество https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

1 неделя, 5 дней назад @ youtube.com
Ekaterina Kondratyeva: Assessing the performance of the AI systems
Ekaterina Kondratyeva: Assessing the performance of the AI systems Ekaterina Kondratyeva: Assessing the performance of the AI systems

Data Fest Online 2020

ML in Healthcare track https://ods.ai/tracks/ml-in-healthcare-df2020 Спикер: Kate Kondrateva (PhD student, Skoltech). Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

1 неделя, 6 дней назад @ youtube.com
Ruslan Aliev: Automatic detection of focal cortical dysplasia
Ruslan Aliev: Automatic detection of focal cortical dysplasia Ruslan Aliev: Automatic detection of focal cortical dysplasia

Data Fest Online 2020

ML in Healthcare track https://ods.ai/tracks/ml-in-healthcare-df2020 Спикер: Ruslan Aliev (MS student, Skoltech). Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

1 неделя, 6 дней назад @ youtube.com
Илья Сайтанов: Математика как мясорубка
Илья Сайтанов: Математика как мясорубка Илья Сайтанов: Математика как мясорубка

Data Fest Online 2020

DS Minus ML track https://ods.ai/tracks/ds-ml-df2020 Спикер: Илья Сайтанов, Группа ДСМ Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

2 недели назад @ youtube.com
Нерсес Багиян: Peeking at A/B Tests
Нерсес Багиян: Peeking at A/B Tests Нерсес Багиян: Peeking at A/B Tests

Data Fest Online 2020

A/B Testing Track https://ods.ai/tracks/ab-testing-df2020 Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

2 недели, 3 дня назад @ youtube.com
🎧 Podcasts
Lex Fridman AI Podcast Lex Fridman AI Podcast
последний пост 4 часа назад
#142 – Manolis Kellis: Meaning of Life, the Universe, and Everything
#142 – Manolis Kellis: Meaning of Life, the Universe, and Everything #142 – Manolis Kellis: Meaning of Life, the Universe, and Everything

Manolis Kellis is a computational biologist at MIT.

Please support this podcast by checking out our sponsors:– Grammarly: https://grammarly.com/lex to get 20% off premium– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Cash App: https://cash.app/ and use code LexPodcast to get $10EPISODE LINKS:Manolis Website: http://web.mit.edu/manoli/Manolis Twitter: https://twitter.com/manoliskellisManolis YouTube: https://www.youtube.com/channel/UCkKlJ5LHrE3C7fgbnPA5DGAPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episo…

4 часа назад @ lexfridman.com
#141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology
#141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology

Erik Brynjolfsson is an economist at Stanford.

Please support this podcast by checking out our sponsors:– Vincero: https://vincerowatches.com/lex to get up to 25% off + free shipping– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Cash App: https://cash.app/ and use code LexPodcast to get $10EPISODE LINKS:Erik’s Twitter: https://twitter.com/erikbrynErik’s Website: https://www.brynjolfsson.com/The Second Machine Age (book): https://amzn.to/33f1Pk2Machine, Platform, Crowd (book): https://amzn.to/3miJZ76PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podc…

5 дней, 6 часов назад @ lexfridman.com
#140 – Lisa Feldman Barrett: Love, Evolution, and the Human Brain
#140 – Lisa Feldman Barrett: Love, Evolution, and the Human Brain #140 – Lisa Feldman Barrett: Love, Evolution, and the Human Brain

Lisa Feldman Barrett is a neuroscientist, psychologist, and author.

Please support this podcast by checking out our sponsors:– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get $200 off– MasterClass: https://masterclass.com/lex to get 15% off annual sub– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Seven and a Half Lessons About the Brain (book): https://amzn.to/2Sp5ar9How Emotions Are Made (book): https://amzn.to/2GwAFg6Lisa’s Twitter: https://twitter.com/LFeldmanBarrettLisa’s Website: https://lisafeldmanbarrett.com/PODCAST INFO:Podcast website: https://lexfr…

1 неделя, 3 дня назад @ lexfridman.com
#139 – Andrew Huberman: Neuroscience of Optimal Performance
#139 – Andrew Huberman: Neuroscience of Optimal Performance #139 – Andrew Huberman: Neuroscience of Optimal Performance

Andrew Huberman is a neuroscientist at Stanford.

Please support this podcast by checking out our sponsors:– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get $200 off– SEMrush: https://www.semrush.com/partner/lex/ to get a free month of Guru– Cash App: https://cash.app/ and use code LexPodcast to get $10EPISODE LINKS:Andrew’s Instagram: https://www.instagram.com/hubermanlabAndrew’s Wikipedia: https://en.wikipedia.org/wiki/Andrew_D._HubermanAndrew’s Website: http://www.hubermanlab.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episo…

2 недели назад @ lexfridman.com
#138 – Yaron Brook: Ayn Rand and the Philosophy of Objectivism
#138 – Yaron Brook: Ayn Rand and the Philosophy of Objectivism #138 – Yaron Brook: Ayn Rand and the Philosophy of Objectivism

Yaron Brook is a objectivist philosopher, podcaster, and author.

Please support this podcast by checking out our sponsors:– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Cash App: https://cash.app/ and use code LexPodcast to get $10EPISODE LINKS:Yaron’s Twitter: https://twitter.com/yaronbrookYaron Brook Show (YouTube): https://www.youtube.com/user/ybrookFree Market Revolution (book): https://amzn.to/32H0oLbEqual is Unfair (book): https://amzn.to/32K3NsCPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2n…

2 недели, 3 дня назад @ lexfridman.com
#137 – Alex Filippenko: Supernovae, Dark Energy, Aliens & the Expanding Universe
#137 – Alex Filippenko: Supernovae, Dark Energy, Aliens & the Expanding Universe #137 – Alex Filippenko: Supernovae, Dark Energy, Aliens & the Expanding Universe

Alex Filippenko is an astrophysicist and professor of astronomy at Berkeley.

Please support this podcast by checking out our sponsors:– Neuro: https://www.getneuro.com and use code LEX to get 15% off– BetterHelp: https://betterhelp.com/lex to get 10% off– MasterClass: https://masterclass.com/lex to get 15% off annual sub– Cash App: https://cash.app/ and use code LexPodcast to get $10EPISODE LINKS:Alex’s Website: https://astro.berkeley.edu/people/alex-filippenko/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episodes: https://youtube.com/lexfridmanYouTub…

3 недели, 1 день назад @ lexfridman.com
#136 – Dan Carlin: Hardcore History
#136 – Dan Carlin: Hardcore History #136 – Dan Carlin: Hardcore History

Dan Carlin is a historian, political thinker, and podcaster.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(07:53) – Nature of evil(14:50) – Is violence and force fundamental to human civilization?

(2:16:31) – Elon Musk, Tesla, SpaceX(2:24:53) – Steering around the iceberg – wow do we avoid collapse of society?

(2:47:00) – Advice on podcasting(2:50:12) – Joe Rogan, Spotify, and the future of podcasting(3:05:19) – Future episodes of Hardcore History podcast(3:20:21) – Is Ben real?

3 недели, 6 дней назад @ lexfridman.com
#135 – Charles Isbell: Computing, Interactive AI, and Race in America
#135 – Charles Isbell: Computing, Interactive AI, and Race in America #135 – Charles Isbell: Computing, Interactive AI, and Race in America

Charles Isbell is the Dean of the College of Computing at Georgia Tech.

Please support this podcast by checking out our sponsors:– Neuro: https://www.getneuro.com and use code LEX to get 15% off– Decoding Digital: https://appdirect.com/decoding-digital– MasterClass: https://masterclass.com/lex to get 15% off annual sub– Cash App: https://cash.app/ and use code LexPodcast to get $10EPISODE LINKS:Charles’s Twitter: https://twitter.com/isbellHFhCharles’s Website: https://www.cc.gatech.edu/~isbell/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episodes: htt…

4 недели назад @ lexfridman.com
#134 – Eric Weinstein: On the Nature of Good and Evil, Genius and Madness
#134 – Eric Weinstein: On the Nature of Good and Evil, Genius and Madness #134 – Eric Weinstein: On the Nature of Good and Evil, Genius and Madness

Eric Weinstein is a mathematical physicist, podcaster, and intellectual.

Please support this podcast by checking out our sponsors:– Grammarly: https://grammarly.com/lex to get 20% off premium– Sun Basket: https://sunbasket.com/lex and use code LEX to get $35 off– SEMrush: https://www.semrush.com/partner/lex/ to get a free month of Guru– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months freeEPISODE LINKS:Eric’s Twitter: https://twitter.com/EricRWeinsteinEric’s YouTube: https://www.youtube.com/ericweinsteinphdThe Portal podcast: https://podcasts.apple.com/us/podcast/the-portal/id1469999563PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: h…

1 месяц назад @ lexfridman.com
#133 – Manolis Kellis: Biology of Disease
#133 – Manolis Kellis: Biology of Disease #133 – Manolis Kellis: Biology of Disease

Manolis Kellis is a computational biologist at MIT.

Please support this podcast by checking out our sponsors:– SEMrush: https://www.semrush.com/partner/lex/ to get a free month of Guru– Pessimists Archive: https://pessimists.co/– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get $200 off– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Manolis Website: http://web.mit.edu/manoli/Manolis Twitter: https://twitter.com/manoliskellisManolis YouTube: https://www.youtube.com/channel/UCkKlJ5LHrE3C7fgbnPA5DGAManolis Wikipedia: https://en.wikipedia.org/wiki/Manolis_KellisPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2l…

1 месяц назад @ lexfridman.com
#132 – George Hotz: Hacking the Simulation & Learning to Drive with Neural Nets
#132 – George Hotz: Hacking the Simulation & Learning to Drive with Neural Nets #132 – George Hotz: Hacking the Simulation & Learning to Drive with Neural Nets

George Hotz (geohot) is a programmer, hacker, and the founder of Comma.ai.

On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction07:02 – Will human civilization destroy itself?

09:49 – Where are the aliens?

14:36 – Tic Tac UFO and Bob Lazar17:04 – Conspiracy theories19:07 – The programming language of life23:28 – The games that humans play31:58 – Memory leaks in the simulation34:29 – Theories of everything36:14 – Ethereum startup story44:02 – Cryptocurrency53:28 – Self-help advice57:08 – Comma.ai59:02 – Comma two1:07:50 – Tesla vs Comma.ai1:16:53 – Driver monitoring1:30:34 – Communicating uncertainty1:32:22 – Tesla Dojo1:38:50 – Tesla Au…

1 месяц, 1 неделя назад @ lexfridman.com
#131 – Chris Lattner: The Future of Computing and Programming Languages
#131 – Chris Lattner: The Future of Computing and Programming Languages #131 – Chris Lattner: The Future of Computing and Programming Languages

Chris Lattner is a world-class software & hardware engineer, leading projects at Apple, Tesla, Google, and SiFive.

On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction07:12 – Working with Elon Musk, Steve Jobs, Jeff Dean12:42 – Why do programming languages matter?

18:42 – Python vs Swift29:35 – Design decisions34:53 – Types38:41 – Programming languages are a bicycle for the mind41:13 – Picking what language to learn47:12 – Most beautiful feature of a programming language56:36 – Walrus operator1:06:03 – LLVM1:11:15 – MLIR compiler framework1:15:21 – SiFive semiconductor design1:27:56 – Moore’s Law1:31:09 – Parallelization1:35:37 – Swift…

1 месяц, 1 неделя назад @ lexfridman.com
#130 – Scott Aaronson: Computational Complexity and Consciousness
#130 – Scott Aaronson: Computational Complexity and Consciousness #130 – Scott Aaronson: Computational Complexity and Consciousness

Scott Aaronson is a quantum computer scientist.

Please support this podcast by checking out our sponsors:– SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security camera– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get $200 off– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– BetterHelp: https://betterhelp.com/lex and use code LEX to get 10% offEPISODE LINKS:Scott’s Blog: https://www.scottaaronson.com/blog/Our previous episode: https://www.youtube.com/watch?v=uX5t8EivCaMPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https:/…

1 месяц, 2 недели назад @ lexfridman.com
#129 – Lisa Feldman Barrett: Counterintuitive Ideas About How the Brain Works
#129 – Lisa Feldman Barrett: Counterintuitive Ideas About How the Brain Works #129 – Lisa Feldman Barrett: Counterintuitive Ideas About How the Brain Works

Lisa Feldman Barrett is a neuroscientist, psychologist, and author.

On some podcast players you should be able to click the timestamp to jump to that time.

08:03 – Life on Earth12:55 – Collective intelligence of human brains21:43 – Triune brain27:52 – The predicting brain35:48 – How the brain evolved41:47 – Free will50:40 – Is anything real?

1:03:13 – Dreams1:09:00 – Emotions are human-constructed concepts1:34:29 – Are women more emotional than men?

1:43:05 – Empathy2:14:46 – Love2:18:40 – Mortality2:20:16 – Meaning of life

1 месяц, 3 недели назад @ lexfridman.com
#128 – Michael Malice: Anarchy, Democracy, Libertarianism, Love, and Trolling
#128 – Michael Malice: Anarchy, Democracy, Libertarianism, Love, and Trolling #128 – Michael Malice: Anarchy, Democracy, Libertarianism, Love, and Trolling

Michael Malice is a political thinker, podcaster, and author.

Please support this podcast by checking out our sponsors:– SEMrush: https://www.semrush.com/partner/lex/ to get a free month of Guru– DoorDash: https://doordash.com/ and use code LEX to get $5 off– MasterClass: https://masterclass.com/lex to get 15% off annual subEPISODE LINKS:Michael’s Twitter: https://twitter.com/michaelmaliceMichael’s YouTube: https://www.youtube.com/channel/UC5tj5QCpJKIl-KIa4Gib5XwMichael’s Website: http://michaelmalice.com/about/Your Welcome podcast: https://bit.ly/30q8oz1The New Right (book): https://amzn.to/34gxLo3Dear Reader (book): https://amzn.to/2HPPlHSPODCAST INFO:Podcast website: https://lexfridman.c…

1 месяц, 4 недели назад @ lexfridman.com
DeepMind: The Podcast DeepMind: The Podcast
последний пост None
Microsoft Research Podcast Microsoft Research Podcast
последний пост 4 месяца, 3 недели назад
119 - Defending DRAM for data safety and security in the cloud
119 - Defending DRAM for data safety and security in the cloud 119 - Defending DRAM for data safety and security in the cloud

Dynamic random-access memory – or DRAM – is the most popular form of volatile computer memory in the world but it’s particularly susceptible to Rowhammer, an adversarial attack that can cause data loss and security exploits in everything from smart phones to the cloud.

Today, Dr. Stefan Saroiu, a Senior Principal Researcher in MSR’s Mobility and Networking group, explains why DRAM remains vulnerable to Rowhammer attacks today, even after several years of mitigation efforts, and then tells us how a new approach involving bespoke extensibility mechanisms for DRAM might finally hammer Rowhammer in the fight to keep data safe and secure.

4 месяца, 3 недели назад @ blubrry.com
118 - Accessible systems for sign language computation with Dr. Danielle Bragg
118 - Accessible systems for sign language computation with Dr. Danielle Bragg 118 - Accessible systems for sign language computation with Dr. Danielle Bragg

Many computer science researchers set their sights on building general AI technologies that could impact hundreds of millions – or even billions – of people.

But Dr. Danielle Bragg, a senior researcher at MSR’s New England lab, has a slightly smaller and more specific population in mind: the some seventy million people worldwide who use sign languages as their primary means of communication.

Today, Dr. Bragg gives us an insightful overview of the field and talks about the unique challenges and opportunities of building systems that expand access to information in line with the needs and desires of the deaf and signing community.

https://www.microsoft.com/research

5 месяцев, 2 недели назад @ blubrry.com
117 - Provably efficient reinforcement learning with Dr. Akshay Krishnamurthy
117 - Provably efficient reinforcement learning with Dr. Akshay Krishnamurthy 117 - Provably efficient reinforcement learning with Dr. Akshay Krishnamurthy

MSR’s New York City lab is home to some of the best reinforcement learning research on the planet but if you ask any of the researchers, they’ll tell you they’re very interested in getting it out of the lab and into the real world.

One of those researchers is Dr. Akshay Krishnamurthy and today, he explains how his work on feedback-driven data collection and provably efficient reinforcement learning algorithms is helping to move the RL needle in the real-world direction.

https://www.microsoft.com/research

6 месяцев назад @ blubrry.com
116 - Harvesting randomness, HAIbrid algorithms and safe AI with Dr. Siddhartha Sen
116 - Harvesting randomness, HAIbrid algorithms and safe AI with Dr. Siddhartha Sen 116 - Harvesting randomness, HAIbrid algorithms and safe AI with Dr. Siddhartha Sen

Dr. Siddhartha Sen is a Principal Researcher in MSR’s New York City lab, and his research interests are, if not impossible, at least impossible sounding: optimal decision making, universal data structures, and verifiably safe AI.

Today, he tells us how he’s using reinforcement learning and HAIbrid algorithms to tap the best of both human and machine intelligence and develop AI that’s minimally disruptive, synergistic with human solutions, and safe.

6 месяцев, 1 неделя назад @ blubrry.com
036r - A conversation with Microsoft CTO Kevin Scott
036r - A conversation with Microsoft CTO Kevin Scott 036r - A conversation with Microsoft CTO Kevin Scott

This episode originally aired in August, 2018.

Kevin Scott has embraced many roles over the course of his illustrious career in technology: software developer, engineering executive, researcher, angel investor, philanthropist, and now, Chief Technology Officer of Microsoft.

But perhaps no role suits him so well – or has so fundamentally shaped all the others – as his self-described role of “all-around geek.”Today, in a wide-ranging interview, Kevin shares his insights on both the history and the future of computing, talks about how his impulse to celebrate the extraordinary people “behind the tech” led to an eponymous non-profit organization and a podcast, and… reveals the superpower he got…

6 месяцев, 2 недели назад @ blubrry.com
115 - Diving into Deep InfoMax with Dr. Devon Hjelm
115 - Diving into Deep InfoMax with Dr. Devon Hjelm 115 - Diving into Deep InfoMax with Dr. Devon Hjelm

Dr. Devon Hjelm is a senior researcher at the Microsoft Research lab in Montreal, and today, he joins me to dive deep into his research on Deep InfoMax, a novel self-supervised learning approach to training AI models – and getting good representations – without human annotation.

He also tells us how an interest in neural networks, first human and then machine, led to an inspiring career in deep learning research.

https://www.microsoft.com/research

6 месяцев, 3 недели назад @ blubrry.com
080r - All Data AI with Dr. Andrew Fitzgibbon
080r - All Data AI with Dr. Andrew Fitzgibbon 080r - All Data AI with Dr. Andrew Fitzgibbon

This episode originally aired in June, 2019You may not know who Dr. Andrew Fitzgibbon is, but if you’ve watched a TV show or movie in the last two decades, you’ve probably seen some of his work.

An expert in 3D computer vision and graphics, and head of the new All Data AI group at Microsoft Research Cambridge, Dr. Fitzgibbon was instrumental in the development of Boujou, an Emmy Award-winning 3D camera tracker that lets filmmakers place virtual props, like the floating candles in Hogwarts School for Witchcraft and Wizardry, into live-action footage.

But that was just his warm-up act.

On today’s podcast, Dr. Fitzgibbon tells us what he’s been working on since the Emmys in 2002, including bod…

6 месяцев, 4 недели назад @ blubrry.com
020r - Getting good VIBEs from your computer with Dr. Mary Czerwinski
020r - Getting good VIBEs from your computer with Dr. Mary Czerwinski 020r - Getting good VIBEs from your computer with Dr. Mary Czerwinski

This episode originally aired in April, 2018Emotions are fundamental to human interaction, but in a world where humans are increasingly interacting with AI systems, Dr. Mary Czerwinski, Principal Researcher and Research Manager of the Visualization and Interaction for Business and Entertainment group at Microsoft Research, believes emotions may be fundamental to our interactions with machines as well.

And through her team’s work in affective computing, the quest to bring Artificial Emotional Intelligence – or AEI – to our computers may be closer than we think.

Today, Dr. Czerwinski tells us how a cognitive psychologist found her way into the research division of the world’s largest software…

7 месяцев назад @ blubrry.com
072r - AI for Earth with Dr. Lucas Joppa
072r - AI for Earth with Dr. Lucas Joppa 072r - AI for Earth with Dr. Lucas Joppa

This episode originally aired in April, 2019.

We hear a lot these days about “AI for good” and the efforts of many companies to harness the power of artificial intelligence to solve some of our biggest environmental challenges.

It’s rare, however, that you find a company willing to bring its environmental bona fides all the way to the C Suite.

Well, meet Dr. Lucas Joppa.

A former environmental and computer science researcher at MSR who was tapped in 2017 to become the company’s first Chief Environmental Scientist, Dr. Joppa is now the Chief Environmental Officer at Microsoft, another first, and is responsible for managing the company’s overall environmental sustainability efforts from opera…

7 месяцев, 1 неделя назад @ blubrry.com
004r - Getting Virtual with Dr. Mar Gonzalez Franco
004r - Getting Virtual with Dr. Mar Gonzalez Franco 004r - Getting Virtual with Dr. Mar Gonzalez Franco

This episode originally aired in December, 2017On today’s episode, neuroscientist and virtual reality researcher, Dr. Mar Gonzalez Franco, talks about her work in VR, explains how avatars can help increase our empathy and reduce our biases via role play, and addresses the misconceptions that exist between the immersive experiences of virtual reality and psychedelic drugs.

7 месяцев, 2 недели назад @ blubrry.com
114 - Project Orleans and the distributed database future with Dr. Philip Bernstein
114 - Project Orleans and the distributed database future with Dr. Philip Bernstein 114 - Project Orleans and the distributed database future with Dr. Philip Bernstein

Forty years ago, database research was an “exotic” field and, because of its business data processing reputation, was not considered intellectually interesting in academic circles.

But that didn’t deter Dr. Philip Bernstein, now a Distinguished Scientist in MSR’s Data Management, Exploration and Mining group, and a pioneer in the field.

Today, Dr. Bernstein talks about his pioneering work in databases over the years and tells us all about Project Orleans, a distributed systems programming framework that makes life easier for programmers who aren’t distributed systems experts.

He also talks about the future of database systems in a cloud scale world, and reveals where he finds his research s…

7 месяцев, 3 недели назад @ blubrry.com
113 - An interview with Microsoft President Brad Smith
113 - An interview with Microsoft President Brad Smith 113 - An interview with Microsoft President Brad Smith

Brad Smith is the President of Microsoft and leads a team of more than 1400 employees in 56 countries.

He plays a key role in spearheading the company’s work on critical issues involving the intersection of technology and society.

In his spare time, he’s also an author!

He also gave us a peek inside the life of a person the New York Times has described a “de facto ambassador for the technology industry at large” – himself!

https://www.microsoft.com/research

8 месяцев назад @ blubrry.com
112 - Microsoft’s AI Transformation, Project Turing and smarter search with Rangan Majumder
112 - Microsoft’s AI Transformation, Project Turing and smarter search with Rangan Majumder 112 - Microsoft’s AI Transformation, Project Turing and smarter search with Rangan Majumder

Rangan Majumder is the Partner Group Program Manager of Microsoft’s Search and AI, and he has a simple goal: to make the world smarter and more productive.

But nobody said simple was easy, so he and his team are working on better – and faster – ways to help you find the information you’re looking for, anywhere you’re looking for it.

Today, Rangan talks about how three big trends have changed the way Microsoft is building – and sharing – AI stacks across product groups.

He also tells us about Project Turing, an internal deep learning moonshot that aims to harness the resources of the web and bring the power of deep learning to a search box near you.

https://www.microsoft.com/research

8 месяцев, 1 неделя назад @ blubrry.com
111 - Auto ML and the future of self-managing networks with Dr. Behnaz Arzani
111 - Auto ML and the future of self-managing networks with Dr. Behnaz Arzani 111 - Auto ML and the future of self-managing networks with Dr. Behnaz Arzani

Dr. Behnaz Arzani is a senior researcher in the Mobility and Networking group at MSR, and she feels your pain.

At least, that is, if you’re a network operator trying to troubleshoot an incident in a datacenter.

Her research is all about getting networks to manage themselves, so your life is as pain-free as possible.

On today’s podcast, Dr. Arzani tells us why it’s so hard to identify and resolve networking problems and then explains how content-aware, or domain-customized, auto ML frameworks might help.

https://www.microsoft.com/research

8 месяцев, 2 недели назад @ blubrry.com
110 - Engineering research to life with Gavin Jancke
110 - Engineering research to life with Gavin Jancke 110 - Engineering research to life with Gavin Jancke

If you want an inside look at how a research idea goes from project to prototype to product, you should hang out with Gavin Jancke for a while.

He’s the General Manager of Engineering for MSR Redmond where he created – and runs – the Central Engineering Group.

Over the past two decades, he’s overseen more than seven hundred software and hardware engineering projects, from internal MSR innovations to Microsoft product group partnerships.

Today, Gavin takes us on a guided tour of the research engineering landscape and the engineering pipeline, recounting some of Central Engineering’s greatest hits.

He also explains how the lab determines which projects get engineering resources, and reveals h…

8 месяцев, 3 недели назад @ blubrry.com
NLP Highlights NLP Highlights
последний пост 2 недели, 4 дня назад
122 - Statutory Reasoning in Tax Law, with Nils Holzenberger
122 - Statutory Reasoning in Tax Law, with Nils Holzenberger 122 - Statutory Reasoning in Tax Law, with Nils Holzenberger

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2 недели, 4 дня назад @ soundcloud.com
121 - Language and the Brain, with Alona Fyshe
121 - Language and the Brain, with Alona Fyshe 121 - Language and the Brain, with Alona Fyshe

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1 месяц назад @ soundcloud.com
120 - Evaluation of Text Generation, with Asli Celikyilmaz
120 - Evaluation of Text Generation, with Asli Celikyilmaz 120 - Evaluation of Text Generation, with Asli Celikyilmaz

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1 месяц, 4 недели назад @ soundcloud.com
119 - Social NLP, with Diyi Yang
119 - Social NLP, with Diyi Yang 119 - Social NLP, with Diyi Yang

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2 месяца, 4 недели назад @ soundcloud.com
118 - Coreference Resolution, with Marta Recasens
118 - Coreference Resolution, with Marta Recasens 118 - Coreference Resolution, with Marta Recasens

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3 месяца назад @ soundcloud.com
117 - Interpreting NLP Model Predictions, with Sameer Singh
117 - Interpreting NLP Model Predictions, with Sameer Singh 117 - Interpreting NLP Model Predictions, with Sameer Singh

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3 месяца, 2 недели назад @ soundcloud.com
116 - Grounded Language Understanding, with Yonatan Bisk
116 - Grounded Language Understanding, with Yonatan Bisk 116 - Grounded Language Understanding, with Yonatan Bisk

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5 месяцев назад @ soundcloud.com
115 - AllenNLP, interviewing Matt Gardner
115 - AllenNLP, interviewing Matt Gardner 115 - AllenNLP, interviewing Matt Gardner

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5 месяцев, 2 недели назад @ soundcloud.com
114 - Behavioral Testing of NLP Models, with Marco Tulio Ribeiro
114 - Behavioral Testing of NLP Models, with Marco Tulio Ribeiro 114 - Behavioral Testing of NLP Models, with Marco Tulio Ribeiro

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6 месяцев, 1 неделя назад @ soundcloud.com
113 - Managing Industry Research Teams, with Fernando Pereira
113 - Managing Industry Research Teams, with Fernando Pereira 113 - Managing Industry Research Teams, with Fernando Pereira

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6 месяцев, 1 неделя назад @ soundcloud.com
112 - Alignment of Multilingual Contextual Representations, with Steven Cao
112 - Alignment of Multilingual Contextual Representations, with Steven Cao 112 - Alignment of Multilingual Contextual Representations, with Steven Cao

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6 месяцев, 3 недели назад @ soundcloud.com
111 - Typologically diverse, multi-lingual, information-seeking questions, with Jon Clark
111 - Typologically diverse, multi-lingual, information-seeking questions, with Jon Clark 111 - Typologically diverse, multi-lingual, information-seeking questions, with Jon Clark

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7 месяцев, 1 неделя назад @ soundcloud.com
110 - Natural Questions, with Tom Kwiatkowski and Michael Collins
110 - Natural Questions, with Tom Kwiatkowski and Michael Collins 110 - Natural Questions, with Tom Kwiatkowski and Michael Collins

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7 месяцев, 4 недели назад @ soundcloud.com
109 - What Does Your Model Know About Language, with Ellie Pavlick
109 - What Does Your Model Know About Language, with Ellie Pavlick 109 - What Does Your Model Know About Language, with Ellie Pavlick

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8 месяцев назад @ soundcloud.com
108 - Data-To-Text Generation, with Verena Rieser and Ondřej Dušek
108 - Data-To-Text Generation, with Verena Rieser and Ondřej Dušek 108 - Data-To-Text Generation, with Verena Rieser and Ondřej Dušek

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8 месяцев, 1 неделя назад @ soundcloud.com
Data Skeptic
последний пост 3 дня, 5 часов назад
Face Mask Sentiment Analysis
Face Mask Sentiment Analysis Face Mask Sentiment Analysis

Face Mask Sentiment AnalysisAs the COVID-19 parndemic continues, the public (or at least those with Twitter accounts) are sharing their personal opinions about mask wearing via Twitter.

What does this data tell us about public opinion?

How does it vary by demographic?

Meil Yeung, Jonathan Lai, and Jiebo Luo join us this week to discuss their recent paper.

Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic.

3 дня, 5 часов назад @ dataskeptic.com
Counting Briberies in Elections
Counting Briberies in Elections Counting Briberies in Elections

Niclas Boehmer, second year PhD student at Berlin Institute of Technology, comes on today to discuss the computational complexity of bribery in elections through the paper “On the Robustness of Winners: Counting Briberies in Elections.” Links Mentioned: https://www.akt.tu-berlin.de/menue/team/boehmer_niclas/ Works Mentioned: “On the Robustness of Winners: Counting Briberies in Elections.” by Niclas Boehmer, Robert Bredereck, Piotr Faliszewski. Rolf Niedermier Thanks to our sponsors: Springboard School of Data: Springboard is a comprehensive end-to-end online data career program. Create a portfolio of projects to spring your career into action. Learn more about how you can be one of twenty $…

1 неделя, 3 дня назад @ dataskeptic.com
Sybil Attacks on Federated Learning
Sybil Attacks on Federated Learning Sybil Attacks on Federated Learning

Clement Fung joins us to discuss sybil attacks on federated learning.

2 недели, 3 дня назад @ dataskeptic.com
Differential Privacy at the US Census
Differential Privacy at the US Census Differential Privacy at the US Census

Differential Privacy at the US CensusSimson Garfinkel joins us to discuss using differential privacy at the US Census Bureau.

Some of the discussion resolves around the topics in the paper Randomness Concerns When Deploying Differential Privacy.

3 недели, 3 дня назад @ dataskeptic.com
Distributed Consensus
Distributed Consensus Distributed Consensus

Heidi Howard joins us to discuss distributed consensus with Paxos.

1 месяц назад @ dataskeptic.com
ACID Compliance
ACID Compliance ACID Compliance

ACID ComplianceLinhda joins us to discuss the topic of ACID Compliance.

1 месяц, 1 неделя назад @ dataskeptic.com
National Popular Vote Interstate Compact
National Popular Vote Interstate Compact National Popular Vote Interstate Compact

The National Popular Vote Interstate CompactPatrick Rosenstiel joins us to discuss the The National Popular Vote.

1 месяц, 2 недели назад @ dataskeptic.com
Defending the p-value
Defending the p-value Defending the p-value

Defending the p-valueYudi Pawitan joins us to discuss his paper Defending the P-value.

1 месяц, 2 недели назад @ dataskeptic.com
Retraction Watch
Retraction Watch Retraction Watch

Ivan Oransky joins us to discuss his work documenting the scientific peer-review process at retractionwatch.com.

1 месяц, 3 недели назад @ dataskeptic.com
Crowdsourced Expertise
Crowdsourced Expertise Crowdsourced Expertise

Derek Lim joins us to discuss the paper Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform.

2 месяца, 1 неделя назад @ dataskeptic.com
The Spread of Misinformation Online
The Spread of Misinformation Online The Spread of Misinformation Online

Neil Johnson joins us to discuss the paper The online competition between pro- and anti-vaccination views.

2 месяца, 2 недели назад @ dataskeptic.com
Consensus Voting
Consensus Voting Consensus Voting

Consensus VotingMashbat Suzuki joins us to discuss the paper How Many Freemasons Are There?

The Consensus Voting Mechanism in Metric Spaces.

Check out Mashbat’s and many other great talks at the 13th Symposium on Algorithmic Game Theory (SAGT 2020)

2 месяца, 3 недели назад @ dataskeptic.com
Voting Mechanisms
Voting Mechanisms Voting Mechanisms

Voting MechanismsSteven Heilman joins us to discuss his paper Designing Stable Elections.

3 месяца назад @ dataskeptic.com
False Consensus
False Consensus False Consensus

False ConcensusSami Yousif joins us to discuss the paper The Illusion of Consensus: A Failure to Distinguish Between True and False Consensus.

This work empirically explores how individuals evaluate concensus under different experimental conditions reviewing online news articles.

More from Sami at samiyousif.org.

3 месяца, 1 неделя назад @ dataskeptic.com
Fraud Detection in Real Time
Fraud Detection in Real Time Fraud Detection in Real Time

Fraud Detection in Real TimeIn this solo episode, Kyle overviews the field of fraud detection with eCommerce as a use case.

He discusses some of the techniques and system architectures used by companies to fight fraud.

3 месяца, 2 недели назад @ dataskeptic.com
Linear Digressions Linear Digressions
последний пост 4 месяца, 1 неделя назад
So long, and thanks for all the fish
So long, and thanks for all the fish So long, and thanks for all the fish

All good things must come to an end, including this podcast.

This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!

), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week.

Thanks, best wishes, and good night!

4 месяца, 1 неделя назад @ lineardigressions.com
A reality check on AI-driven medical assistants
A reality check on AI-driven medical assistants

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be…

4 месяца, 2 недели назад @ lineardigressions.com
A Data Science Take on Open Policing Data
A Data Science Take on Open Policing Data

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.Relevant Links:Stanford Open Policing ProjectProject ZeroTodd’s LinkedIn PageTodd’s email: hendricks.ta@gmail.com

4 месяца, 2 недели назад @ lineardigressions.com
Procella: YouTube's super-system for analytics data storage
Procella: YouTube's super-system for analytics data storage

This is a re-release of an episode that originally ran in October 2019.If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this…

4 месяца, 3 недели назад @ lineardigressions.com
The Data Science Open Source Ecosystem
The Data Science Open Source Ecosystem The Data Science Open Source Ecosystem

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another.

Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis.

The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

Relevant links:

5 месяцев назад @ lineardigressions.com
Rock the ROC Curve
Rock the ROC Curve

This is a re-release of an episode that first ran on January 29, 2017.This week: everybody's favorite WWII-era classifier metric! But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.

5 месяцев, 1 неделя назад @ lineardigressions.com
Criminology and data science
Criminology and data science

This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason University. Zach specializes in bringing data science methods to studies of criminal behavior, and got in touch after our last episode (about racially complicated recidivism algorithms). Our conversation covers a wide range of topics—common misconceptions around race and crime statistics, how methodologically-driven criminology scholars think about building crime prediction models, and how to think about policy changes when we don’t have a complete understanding of cause and effect in criminology. For the many of us currently re-thinking race and criminal …

5 месяцев, 2 недели назад @ lineardigressions.com
Racism, the criminal justice system, and data science
Racism, the criminal justice system, and data science

As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into one of the ways that data science perpetuates and amplifies racism in the American criminal justice system. COMPAS is an algorithm that claims to give a prediction about the likelihood of an offender to re-offend if released, based on the attributes of the individual, and guess what: it shows disparities in the predictions for black and white offenders that would nudge judges toward giving harsher sentences to black individuals. We dig into this algorithm a little more deeply, unpacking how different metrics give different pictures into the “fai…

5 месяцев, 3 недели назад @ lineardigressions.com
An interstitial word from Ben
An interstitial word from Ben An interstitial word from Ben

A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.

5 месяцев, 4 недели назад @ lineardigressions.com
Convolutional neural networks
Convolutional neural networks Convolutional neural networks

This is a re-release of an episode that originally aired on April 1, 2018If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net.

This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so good at image tasks.

Relevant links:

6 месяцев назад @ lineardigressions.com
Stein's Paradox
Stein's Paradox Stein's Paradox

This is a re-release of an episode that was originally released on February 26, 2017.

When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball player, who belongs to a baseball team), how should you estimate it: use measurements of the individual, or get some extra information from the group?

The James-Stein estimator tells you how to combine individual and group information make predictions that, taken over the whole group, are more accurate than if you treated each individual, well, individually.

Relevant links:

6 месяцев, 1 неделя назад @ lineardigressions.com
Protecting Individual-Level Census Data with Differential Privacy
Protecting Individual-Level Census Data with Differential Privacy

The power of finely-grained, individual-level data comes with a drawback: it compromises the privacy of potentially anyone and everyone in the dataset. Even for de-identified datasets, there can be ways to re-identify the records or otherwise figure out sensitive personal information. That problem has motivated the study of differential privacy, a set of techniques and definitions for keeping personal information private when datasets are released or used for study. Differential privacy is getting a big boost this year, as it’s being implemented across the 2020 US Census as a way of protecting the privacy of census respondents while still opening up the dataset for research and policy use. …

6 месяцев, 2 недели назад @ lineardigressions.com
Causal Trees
Causal Trees Causal Trees

What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning?

Usually these two don’t go well together (deriving causal conclusions from naive data methods leads to biased answers) but economists Susan Athey and Guido Imbens are on the case.

This episodes explores their algorithm for recursively partitioning a dataset to find heterogeneous treatment effects, or for you ML nerds, applying decision trees to causal inference problems.

It’s not a free lunch, but for those (like us!)

who love crossover topics, causal trees are a smart approach from one field hopping the fence to another.

6 месяцев, 3 недели назад @ lineardigressions.com
The Grammar of Graphics
The Grammar of Graphics The Grammar of Graphics

You may not realize it consciously, but beautiful visualizations have rules.

The rules are often implict and manifest themselves as expectations about how the data is summarized, presented, and annotated so you can quickly extract the information in the underlying data using just visual cues.

It’s a bit abstract but very profound, and these principles underlie the ggplot2 package in R that makes famously beautiful plots with minimal code.

This episode covers a paper by Hadley Wickham (author of ggplot2, among other R packages) that unpacks the layered approach to graphics taken in ggplot2, and makes clear the assumptions and structure of many familiar data visualizations.

Relevant links:

7 месяцев назад @ lineardigressions.com
Gaussian Processes
Gaussian Processes

It’s pretty common to fit a function to a dataset when you’re a data scientist. But in many cases, it’s not clear what kind of function might be most appropriate—linear? quadratic? sinusoidal? some combination of these, and perhaps others? Gaussian processes introduce a nonparameteric option where you can fit over all the possible types of functions, using the data points in your datasets as constraints on the results that you get (the idea being that, no matter what the “true” underlying function is, it produced the data points you’re trying to fit). What this means is a very flexible, but depending on your parameters not-too-flexible, way to fit complex datasets.The math underlying GPs ge…

7 месяцев, 1 неделя назад @ lineardigressions.com
SuperDataScience SuperDataScience
последний пост 3 дня, 11 часов назад
SDS 422: Pain Vs. Suffering
SDS 422: Pain Vs. Suffering SDS 422: Pain Vs. Suffering

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3 дня, 11 часов назад @ soundcloud.com
SDS 421: Real-World Applications of Digital Twins
SDS 421: Real-World Applications of Digital Twins SDS 421: Real-World Applications of Digital Twins

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5 дней назад @ soundcloud.com
SDS 420: Wheel of Life
SDS 420: Wheel of Life SDS 420: Wheel of Life

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1 неделя, 3 дня назад @ soundcloud.com
SDS 419: Unlocking the Architecture of Innovation
SDS 419: Unlocking the Architecture of Innovation SDS 419: Unlocking the Architecture of Innovation

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1 неделя, 5 дней назад @ soundcloud.com
SDS 418: Play With Feeling
SDS 418: Play With Feeling SDS 418: Play With Feeling

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2 недели, 3 дня назад @ soundcloud.com
SDS 417: Data Engineering and Product Development
SDS 417: Data Engineering and Product Development SDS 417: Data Engineering and Product Development

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2 недели, 5 дней назад @ soundcloud.com
SDS 416: My Advice for Career Success
SDS 416: My Advice for Career Success SDS 416: My Advice for Career Success

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3 недели, 3 дня назад @ soundcloud.com
SDS 415: Developing and Maintaining Your Technical and Soft Skills
SDS 415: Developing and Maintaining Your Technical and Soft Skills SDS 415: Developing and Maintaining Your Technical and Soft Skills

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3 недели, 5 дней назад @ soundcloud.com
SDS 414: Needs vs. Wants
SDS 414: Needs vs. Wants SDS 414: Needs vs. Wants

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1 месяц назад @ soundcloud.com
SDS 413: Changing The World With Data
SDS 413: Changing The World With Data SDS 413: Changing The World With Data

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1 месяц назад @ soundcloud.com
SDS 412: Stand More - Sit Less
SDS 412: Stand More - Sit Less SDS 412: Stand More - Sit Less

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1 месяц, 1 неделя назад @ soundcloud.com
SDS 411: Succeeding in Analytics by Thinking Outside the Data
SDS 411: Succeeding in Analytics by Thinking Outside the Data SDS 411: Succeeding in Analytics by Thinking Outside the Data

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1 месяц, 1 неделя назад @ soundcloud.com
SDS 410: Communicate Your Needs
SDS 410: Communicate Your Needs SDS 410: Communicate Your Needs

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1 месяц, 2 недели назад @ soundcloud.com
SDS 409: Succeeding & Networking In The Virtual Space
SDS 409: Succeeding & Networking In The Virtual Space SDS 409: Succeeding & Networking In The Virtual Space

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1 месяц, 2 недели назад @ soundcloud.com
SDS 408: Meaning is Everything
SDS 408: Meaning is Everything SDS 408: Meaning is Everything

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1 месяц, 3 недели назад @ soundcloud.com
Data Science at Home Data Science at Home
последний пост 6 дней, 15 часов назад
Similarity in Machine Learning (Ep. 129)
Similarity in Machine Learning (Ep. 129) Similarity in Machine Learning (Ep. 129)

November 24, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonSubscribe to the official Newsletter and never miss an episodeOur Sponsors

6 дней, 15 часов назад @ datascienceathome.com
Distill data and train faster, better, cheaper (Ep. 128)
Distill data and train faster, better, cheaper (Ep. 128) Distill data and train faster, better, cheaper (Ep. 128)

November 17, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonOur SponsorAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

ReferencesDataset distillation (official paper)GitHub repo

1 неделя, 6 дней назад @ datascienceathome.com
Machine Learning in Rust: Amadeus with Alec Mocatta [RB] (Ep. 127)
Machine Learning in Rust: Amadeus with Alec Mocatta [RB] (Ep. 127) Machine Learning in Rust: Amadeus with Alec Mocatta [RB] (Ep. 127)

November 11, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonOur Sponsors

2 недели, 5 дней назад @ datascienceathome.com
Top-3 ways to put machine learning models into production (Ep. 126)
Top-3 ways to put machine learning models into production (Ep. 126) Top-3 ways to put machine learning models into production (Ep. 126)

November 7, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonOur Sponsors

3 недели, 2 дня назад @ datascienceathome.com
Remove noise from data with deep learning (Ep.125)
Remove noise from data with deep learning (Ep.125) Remove noise from data with deep learning (Ep.125)

November 3, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonOur SponsorsProtonMail is a secure and private email provider that protects yourmessages with end-to-end encryption and zero-access encryption so that besides you, noone can access them.

is a secure and private email provider that protects yourmessages with end-to-end encryption and zero-access encryption so that besides you, noone can access them.

Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logis…

3 недели, 6 дней назад @ datascienceathome.com
What is contrastive learning and why it is so powerful? (Ep. 124)
What is contrastive learning and why it is so powerful? (Ep. 124) What is contrastive learning and why it is so powerful? (Ep. 124)

October 30, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonOur SponsorsThe Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.comApps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

A…

1 месяц назад @ datascienceathome.com
Neural Search (Ep. 123)
Neural Search (Ep. 123) Neural Search (Ep. 123)

October 23, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonThis episode is supported by monday.comThe Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com

1 месяц, 1 неделя назад @ datascienceathome.com
Let’s talk about federated learning (Ep. 122)
Let’s talk about federated learning (Ep. 122) Let’s talk about federated learning (Ep. 122)

October 18, 2020 podcastLet’s talk about federated learning.

Why is it important?

Why large organizations are not ready yet?

Come join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonThis episode is supported by Monday.comThe Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com.

1 месяц, 1 неделя назад @ datascienceathome.com
How to test machine learning in production (Ep. 121)
How to test machine learning in production (Ep. 121) How to test machine learning in production (Ep. 121)

October 12, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonThis episode is supported by Monday.comMonday.com bring teams together so you can plan, manage and track everything your team is working on in one centralized placeThe Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com.

1 месяц, 2 недели назад @ datascienceathome.com
Why synthetic data cannot boost machine learning (Ep. 120)
Why synthetic data cannot boost machine learning (Ep. 120) Why synthetic data cannot boost machine learning (Ep. 120)

September 26, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonThis episode is supported by Women in Tech by Manning Conferences

2 месяца назад @ datascienceathome.com
Machine learning in production: best practices [LIVE from twitch.tv]
Machine learning in production: best practices [LIVE from twitch.tv] Machine learning in production: best practices [LIVE from twitch.tv]

September 16, 2020 podcastHey there!

Having the best time of my life 😉This is the first episode I record while I am live on my new Twitch channel 🙂 So much fun!

Feel free to follow me for the next live streaming.

You can also see me coding machine learning stuff in Rust :))Don’t forget to jump on the usual Discord and have a chatI’ll see you there!

2 месяца, 2 недели назад @ datascienceathome.com
Testing in machine learning: checking deep learning models (Ep. 118)
Testing in machine learning: checking deep learning models (Ep. 118) Testing in machine learning: checking deep learning models (Ep. 118)

September 4, 2020 podcastIn this episode I speak with Adam Leon Smith, CTO at DragonFly and expert in testing strategies for software and machine learning.

We cover testing with deep learning (neuron coverage, threshold coverage, sign change coverage, layer coverage, etc.

On September 15th there will be a live@Manning Rust conference.

In one Rust-full day you will attend many talks about what’s special about rust, building high performance web services or video game, about web assembly and much more.

If you want to meet the tribe, tune in september 15th to the live@manning rust conference.

2 месяца, 3 недели назад @ datascienceathome.com
Testing in machine learning: generating tests and data (Ep. 117)
Testing in machine learning: generating tests and data (Ep. 117) Testing in machine learning: generating tests and data (Ep. 117)

August 29, 2020 podcastIn this episode I speak with Adam Leon Smith, CTO at DragonFly and expert in testing strategies for software and machine learning.

On September 15th there will be a live@Manning Rust conference.

In one Rust-full day you will attend many talks about what’s special about rust, building high performance web services or video game, about web assembly and much more.

If you want to meet the tribe, tune in September 15th to the live@manning Rust conference.

3 месяца назад @ datascienceathome.com
Why you care about homomorphic encryption (Ep. 116)
Why you care about homomorphic encryption (Ep. 116) Why you care about homomorphic encryption (Ep. 116)

August 12, 2020 podcastAfter deep learning, a new entry is about ready to go on stage.

The usual journalists are warming up their keyboards for blogs, news feeds, tweets, in one word, hype.

The new words, homomorphic encryption.

They are a consulting firm focused on data science, machine learning, and artificial intelligence.

ReferencesTowards a Homomorphic Machine Learning Big Data Pipeline for the Financial Services SectorIBM Fully Homomorphic Encryption Toolkit for Linux

3 месяца, 2 недели назад @ datascienceathome.com
Test-First machine learning (Ep. 115)
Test-First machine learning (Ep. 115) Test-First machine learning (Ep. 115)

August 3, 2020 podcastIn this episode I speak about a testing methodology for machine learning models that are supposed to be integrated in production environments.

Don’t forget to come chat with us in our Discord channelEnjoy the show!

—This episode is supported by Amethix Technologies.

Amethix works to create and maximize the impact of the world’s leading corporations, startups, and nonprofits, so they can create a better future for everyone they serve.

They are a consulting firm focused on data science, machine learning, and artificial intelligence.

3 месяца, 4 недели назад @ datascienceathome.com