Very ML
State-of-the-art Machine Learning News Feed
/r/MachineLearning /r/MachineLearning
последний пост 36 минут назад
[D] Performance vs. Competence as a useful distinction for "Species Fair Comparisons"?
[D] Performance vs. Competence as a useful distinction for "Species Fair Comparisons"? [D] Performance vs. Competence as a useful distinction for "Species Fair Comparisons"?

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36 минут назад @ reddit.com
[D] Why aren’t TPUs more popular??
[D] Why aren’t TPUs more popular?? [D] Why aren’t TPUs more popular??

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53 минуты назад @ reddit.com
[D] Any recommendation for a software to manage a queue system for a cluster of machines?
[D] Any recommendation for a software to manage a queue system for a cluster of machines? [D] Any recommendation for a software to manage a queue system for a cluster of machines?

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1 час назад @ reddit.com
[P] Earn money for Data Science projects
[P] Earn money for Data Science projects [P] Earn money for Data Science projects

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2 часа назад @ reddit.com
[D] Singing Synthesis
[D] Singing Synthesis [D] Singing Synthesis

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3 часа назад @ reddit.com
[D] Implementing ML in a dating app
[D] Implementing ML in a dating app [D] Implementing ML in a dating app

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3 часа назад @ reddit.com
[D] PyTorch or TensorFlow?
[D] PyTorch or TensorFlow? [D] PyTorch or TensorFlow?

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4 часа назад @ reddit.com
Machine learning uncovers potential new TB drugs
Machine learning uncovers potential new TB drugs Machine learning uncovers potential new TB drugs

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4 часа назад @ reddit.com
How many images are necessary for YOLO? [P]
How many images are necessary for YOLO? [P] How many images are necessary for YOLO? [P]

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4 часа назад @ reddit.com
[D] Statistical Paradises and Paradoxes in Machine Learning
[D] Statistical Paradises and Paradoxes in Machine Learning [D] Statistical Paradises and Paradoxes in Machine Learning

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5 часов назад @ reddit.com
[D] Getting started as a ML/Statistics Consultant
[D] Getting started as a ML/Statistics Consultant [D] Getting started as a ML/Statistics Consultant

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7 часов назад @ reddit.com
[D] Finding the address of a known human face in a GAN that generates faces
[D] Finding the address of a known human face in a GAN that generates faces [D] Finding the address of a known human face in a GAN that generates faces

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7 часов назад @ reddit.com
[R] "Treibhaus", some old genetic optimizer experiments
[R] "Treibhaus", some old genetic optimizer experiments [R] "Treibhaus", some old genetic optimizer experiments

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7 часов назад @ reddit.com
Machine learning book recommendation [D]
Machine learning book recommendation [D] Machine learning book recommendation [D]

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8 часов назад @ reddit.com
[D] Simultaneous Object Detection & OCR
[D] Simultaneous Object Detection & OCR [D] Simultaneous Object Detection & OCR

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8 часов назад @ reddit.com
Towards Data Science Towards Data Science
последний пост 22 минуты назад
How to make your own Instagram filter with facial recognition from scratch using python
How to make your own Instagram filter with facial recognition from scratch using python How to make your own Instagram filter with facial recognition from scratch using python

Over the past 10 years, Facial recognition technology has developed rapidly and has quickly developed a variety of uses.

Facebook offers the SparkAR platform to create facial recognition filters for Facebook and Instagram.

However, we can pretty easily create one ourselves using the OpenCV package in python, so we can use facial recognition anywhere.

Once you’ve installed OpenCV, you should have access to .xml files that contain facial recognition and other image processing algorithms.

Running this new script on our stock image:Photo by Gustavo Alves on Unsplash; altered by authorWe’ve successfully built the facial recognition filter for static images!

22 минуты назад @ towardsdatascience.com
ONNX: Preventing Framework Lock in
ONNX: Preventing Framework Lock in ONNX: Preventing Framework Lock in

ONNX: Preventing Framework Lock inAn introduction to the use of the ONNX standard for the interoperability between Deep Learning frameworks.

Well, this type of interoperability is achieved thanks to the ONNX standard and the ONNX Runtime (which we will see later).

In Figure 1, it is described the problematic addressed by ONNX and ONNX Runtime.

ONNX interoperability | Image by author | Logos taken from original sourceThe ONNX specification addresses the following three components to enable interoperability:1.

ONNX Runtime InferenceTo perform inference with ONNX Runtime, we need to import the onnxruntime library, then we just need to load the onnx model with the onnx module and generate the p…

32 минуты назад @ towardsdatascience.com
Stop Wasting Your Time and Consult a Subject Matter Expert
Stop Wasting Your Time and Consult a Subject Matter Expert Stop Wasting Your Time and Consult a Subject Matter Expert

As mentioned in the previous section, if general knowledge is insufficient to conclude your analysis with your dataset, then stop.

An SME can read through your analysis and model results and verify if your results make sense.

Having someone look over your analysis will aid you in your next steps.

Consider collecting data for a computer and then analyzing that data.

Understanding if your conclusions are making sense is why an SME can be helpful.

1 час назад @ towardsdatascience.com
Best Practices for Collaborative Data Science
Best Practices for Collaborative Data Science Best Practices for Collaborative Data Science

Here are five best practices that make distanced collaboration in data science projects work.

Increasing the success rate of data science projects requires a collaborative partnership between data science teams and decision-makers to ensure that models are appropriate and can be adopted.

Data science managers, data science teams, data engineers, deployment & validation engineers, IT administrators, data-to-business liaisons, and business customers are just some of the roles that play a part.

Data science is not like software engineering; it’s probabilistic and more research-based, which means that not every data science will succeed.

Using the best practices outlined in this article can hel…

2 часа назад @ towardsdatascience.com
String Matching With FuzzyWuzzy
String Matching With FuzzyWuzzy String Matching With FuzzyWuzzy

Instead of trying to format the strings in order to match, Fuzzywuzzy uses a some similarity ratio between two sequences and returns the similarity percentage.

#Installing FuzzyWuzzypip install fuzzywuzzy #Importimport fuzzywuzzyfrom fuzzywuzzy import fuzzfrom fuzzywuzzy import process Str_A = 'FuzzyWuzzy is a lifesaver!'

The partial ratio() function allows us to perform substring matching.

choices = ["3000m Steeplechase", "Men's 3000 meter steeplechase", "3000m STEEPLECHASE MENS", "mens 3000 meter SteepleChase"] process.extract("Men's 3000 Meter Steeplechase", choices, scorer=fuzz.token_sort_ratio) #Output[("Men's 3000 meter steeplechase", 100),('mens 3000 meter SteepleChase', 95),('3000m …

2 часа назад @ towardsdatascience.com
Organise your Jupyter Notebook with these tips
Organise your Jupyter Notebook with these tips Organise your Jupyter Notebook with these tips

Create user-defined functions and save it in a moduleYou may have heard of DRY principle: Don’t Repeat Yourself.

When documenting these functions, I have adapted a few different styles in a way it made more sense to me.

While putting stable code into a module makes sense, I think it is fine to keep experimental functions in your Notebook.

If you implement this tip, you will soon notice that your Notebook start to look less cluttered and more organised.

If you would like learn about unit testing for Data Science, this PyData talk may be a good starting point.

3 часа назад @ towardsdatascience.com
How To Make Scalable APIs Using Flask and FaunaDB
How To Make Scalable APIs Using Flask and FaunaDB How To Make Scalable APIs Using Flask and FaunaDB

Using a service like FaunaDB can help cut costs so much that the hosting capabilities of the app would be virtually free.

Thus, using a monthly billed database for serverless apps kind of kills the point.

A free stack example would be a combination of Netlify, Netlify Functions, and FaunaDB.

In my opinion, using a monthly billed database for serverless apps kind of kills the pointFlask on the other hand is a microframework written in Python.

You can make a serverless Flask app using AWS Lambda.

3 часа назад @ towardsdatascience.com
How I Made Inserts Into SQL Server 100x faster with Pyodbc
How I Made Inserts Into SQL Server 100x faster with Pyodbc How I Made Inserts Into SQL Server 100x faster with Pyodbc

Schema of my dataframe and SQL Server tableWhen I was trying to load my data into SQL Server, I got the error: “Error converting data type varchar to numeric.”This error was extremely confusing to me since the data types of my Pandas dataframe matched perfectly with those defined in the SQL Server table.

My dataframe schema:summertime booltime datetime64[ns]unique_id objectmeasurement float64entered datetime64[ns]updated datetime64[ns]The SQL Server table has a schema similar to this:Schema of the SQL server tableIf you look at the data types, they are matching perfectly.

“Error converting data type varchar to numeric”In order to load this data to the SQL Server database fast, I converted t…

3 часа назад @ towardsdatascience.com
Version Control 101: Getting Started with Git
Version Control 101: Getting Started with Git Version Control 101: Getting Started with Git

Version control systems are a particular type of software designed to help programmers track any specific application source code changes.

When dealing with code files, Git doesn’t focus on the file’s name and instead keeps track of its content.

To store code files, Git uses delta encoding — which keeps the difference in file content — to save repository contents and the version’s metadata explicitly.

Think of a repository as a folder in your computer’s memory — the folder containers different files, with different types and different editing dates.

Git consists of repositories containing the different code files as well as the version metadata.

3 часа назад @ towardsdatascience.com
EPL Analysis and Gameweek 7 Prediction
EPL Analysis and  Gameweek 7 Prediction EPL Analysis and Gameweek 7 Prediction

EPL Analysis and Gameweek 7 PredictionA data driven attempt in predicting English Premier League results using xG StatisticsThis is an article on my EPL Prediction series.

You can check out the prediction for previous Game Week and how it held against the actual performance here.

The only unbeaten sides up to Game-Week 5, Everton and Aston Villa tasted their first defeats in Game Week 6.

Game Week 7 Prediction (Image by author)In the upcoming fixture, we can expect plenty of closely contested matches.

Can’t wait for the next Game Week to find how the predictions fare against the actual performance!

3 часа назад @ towardsdatascience.com
Visualising Well Data Coverage Using Matplotlib
Visualising Well Data Coverage Using Matplotlib Visualising Well Data Coverage Using Matplotlib

Exploratory Data Analysis (EDA) is an integral part of Data Science.

In places where we have a NaN (Not a Number) value, we are going to give it a value of number - 1.

When it is false, the values will be set to 0. data_nan[col].replace(0, num, inplace=True)We can now replace any 0 values with the column number.

For example, CALI has two values to indicate data presence: 1 when there is a real value and 0 when there is a NaN.

Similarly, GR has two values: 3 when there is real data and 2 when there is a NaN.

3 часа назад @ towardsdatascience.com
Speech Emotion Recognition Using RAVDESS Audio Dataset
Speech Emotion Recognition Using RAVDESS Audio Dataset Speech Emotion Recognition Using RAVDESS Audio Dataset

It is a system through which various audio speech files are classified into different emotions such as happy, sad, anger and neutral by computers.

Speech emotion recognition can be used in areas such as the medical field or customer call centers.

My goal here is to demonstrate SER using the RAVDESS Audio Dataset provided on Kaggle.

Feature extraction is important in modeling because it converts audio files into a format that can be understood by models.

After examining waveplots for a sample of each emotion, I decided to use Log-Mel Spectrograms as the method of feature extraction.

3 часа назад @ towardsdatascience.com
Research Area 11: The Missing Data Science Discipline
Research Area 11: The Missing Data Science Discipline Research Area 11: The Missing Data Science Discipline

As a Data Engineer who works closely with many Data Scientists, I never considered that I might be a bit of a Data Scientist myself.

As the largest challenge that Data Engineers currently face, research area 11 is ultimately the largest hurdle facing data-centric companies today.

This is why larger companies hire Data Engineers who focus on turning raw data streams into analytics-friendly databases.

Data Engineers are Data Scientists turned Software Engineers, or Software Engineers turned Data Scientists, who’s brains are pre-occupied with taking the messiest of data and transforming it into logical and content-informed data structures.

For that reason I believe that it is its own Data Scie…

3 часа назад @ towardsdatascience.com
Can I transition from engineering to data science?
Can I transition from engineering to data science? Can I transition from engineering to data science?

Can I transition from engineering to data science?

But here’s the thing, not all engineering majors are created equal and not all are as valuable technically when it comes to transitioning to data science.

Programming to data science is like calculus 1 to engineering.

The first jobI was delighted to see the tide of recruiters contacting me on LinkedIn after I added the data science masters program to my profile; it was indeed indicative of how strong the job market for data science majors is.

As you progress upwards on the corporate data science ladder, you should move from one position to another.

3 часа назад @ towardsdatascience.com
Uprooting anomalies in online user behavior
Uprooting anomalies in online user behavior Uprooting anomalies in online user behavior

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

4 часа назад @ medium.com
Distill.pub Distill.pub
последний пост 1 месяц, 2 недели назад
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.

1 месяц, 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

2 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.

2 месяца назад @ distill.pub
Curve Detectors
Curve Detectors

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

4 месяца, 1 неделя назад @ distill.pub
Exploring Bayesian Optimization
Exploring Bayesian Optimization

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

5 месяцев, 3 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.'

6 месяцев, 4 недели 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.

7 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.

7 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?

7 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.

8 месяцев, 2 недели назад @ distill.pub
Visualizing the Impact of Feature Attribution Baselines
Visualizing the Impact of Feature Attribution Baselines

Exploring the baseline input hyperparameter, and how it impacts interpretations of neural network behavior.

9 месяцев, 3 недели назад @ distill.pub
The Gradient The Gradient
последний пост 2 недели, 3 дня назад
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…

2 недели, 3 дня 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…

3 недели, 3 дня 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…

1 месяц 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.

1 месяц, 2 недели назад @ thegradient.pub
Shortcuts: How Neural Networks Love to Cheat
Shortcuts: How Neural Networks Love to Cheat Shortcuts: How Neural Networks Love to Cheat

The result described above is true, with one little twist: instead of using state-of-the-art artificial deep neural networks, researchers trained “natural” neural networks - more precisely, a flock of four pigeons - to diagnose breast cancer.

In the end, neural networks perhaps aren’t that different from (lazy) humans after all ...

Shortcut Learning in Deep Neural Networks.

Shortcut Learning in Deep Neural Networks.

Jörn-Henrik Jacobsen et al., "Shortcuts: Neural Networks Love to Cheat", The Gradient, 2020.

3 месяца назад @ thegradient.pub
How to Stop Worrying About Compositionality
How to Stop Worrying About Compositionality How to Stop Worrying About Compositionality

The real problem is that language does productivity in a very particular way, and it remains unclear how.

It may also provide fresh ideas, or simply the relief of knowing that compositionality does not have to be tackled entirely in one go.

The compositionality principle says that the meaning of the sentence Dogs sleep is made of the meaning of dogs and the meaning of sleep.

Adhering to purely bottom-up compositionality does make for a somewhat cumbersome semantics, though.

CitationFor attribution in academic contexts or books, please cite this work asAurelie Herbelot, "How to Stop Worrying About Compositionality", The Gradient, 2020.

3 месяца, 1 неделя назад @ thegradient.pub
Challenges of Comparing Human and Machine Perception
Challenges of Comparing Human and Machine Perception Challenges of Comparing Human and Machine Perception

Given these apparent similarities, many questions arise: How similar are human and machine vision really?

Geirhos et al.

The following figure shows two examples of the Synthetic Visual Reasoning Test (SVRT) (Fleuret et al., 2011).

A large recognition gap was identifiable for our DNN when testing machine-selected stimuli - unlike for the machine algorithms tested by Ullman et al.

Human and machine illustration taken from https://www.flickr.com/photos/gleonhard/33661762360 under the license https://creativecommons.org/licenses/by-sa/2.0/CitationFor attribution in academic contexts or books, please cite this work asJudy Borowski and Christina Funke, "Challenges of Comparing Human and Machine P…

3 месяца, 3 недели назад @ thegradient.pub
Lessons from the PULSE Model and Discussion
Lessons from the PULSE Model and Discussion Lessons from the PULSE Model and Discussion

— 🔥Kareem Carr🔥 (@kareem_carr) June 23, 2020Further discussion on the subject also occurred on reddit in the thread "[Discussion] about data bias vs inductive bias in machine learning sparked by the PULSE paper/demo".

— Yann LeCun (@ylecun) June 26, 2020The PULSE model and this exchange were later covered in VentureBeat with the article "A deep learning pioneer’s teachable moment on AI bias".

Regardless of which stance you agree with, it makes sense to at least understand the criticisms directed at Dr. LeCun.

— Yann LeCun (@ylecun) June 21, 2020Which again led to questions regarding the validity of the initial claim:Yes.

CitationFor attribution in academic contexts or books, please cite thi…

4 месяца назад @ thegradient.pub
A Speech-To-Text Practitioner’s Criticisms of Industry and Academia
A Speech-To-Text Practitioner’s Criticisms of Industry and Academia A Speech-To-Text Practitioner’s Criticisms of Industry and Academia

This is a follow-up article to our article on building speech-to-text (STT) models, Towards an ImageNet Moment for Speech-to-Text.

Сriticisms of the IndustryIn general, the majority of STT papers we have read were written by researchers from the industry (e.g.

Most criticisms of STT papers and solutions can be attributed to either the"academic" part or the "industry" part of the researchers’ background.

The majority of modern STT papers usually just heavily overfit on the LibriSpeech ASR corpus (LibriSpeech) with increasingly more extravagant methods.

CitationFor attribution in academic contexts or books, please cite this work asAlexander Veysov, "A Speech-To-Text Practitioner’s Criticisms …

6 месяцев, 3 недели назад @ thegradient.pub
Towards an ImageNet Moment for Speech-to-Text
Towards an ImageNet Moment for Speech-to-Text Towards an ImageNet Moment for Speech-to-Text

Speech-to-text (STT), also known as automated-speech-recognition (ASR), has a long history and has made amazing progress over the past decade.

IntroductionFollowing the success and the democratization (the so-called "ImageNet moment", i.e.

This piece will describe our pursuit of an ImageNet moment for STT, which has so far not been found, and particularly in the context of Russian language.

(i) is easy to estimate just by looking at the model's performance during the first 20-25% of its epochs.

CitationFor attribution in academic contexts or books, please cite this work asAlexander Veysov, "Toward's an ImageNet Moment for Speech-to-Text", The Gradient, 2020.

7 месяцев назад @ thegradient.pub
Quantifying Independently Reproducible Machine Learning
Quantifying Independently Reproducible Machine Learning Quantifying Independently Reproducible Machine Learning

My investigation in reproducible ML has also relied on personal notes and records hosted on Mendeley and Github.

What Makes a ML Paper Reproducible?

The biggest factors are that we cannot take all of our assumptions about so-called reproducible ML at face value.

At the same time, our process and systems must result in reproducible work that does not lead us astray.

AcknowledgmentsFeature image source: https://xkcd.com/242/CitationFor attribution in academic contexts or books, please cite this work asEdward Raff, "Quantifying Independently Reproducible Machine Learning", The Gradient, 2020.

8 месяцев, 3 недели назад @ thegradient.pub
GPT-2 and the Nature of Intelligence
GPT-2 and the Nature of Intelligence GPT-2 and the Nature of Intelligence

--The AI system GPT-2, in a December 2019 interview with The Economist, "An artificial intelligence predicts the future"Innateness, empiricism, and recent developments in deep learningConsider two classic hypotheses about the development of language and cognition.

Consider GPT-2, an AI system that was recently featured in The New Yorker and interviewed by The Economist.

The popular blog StatStarCodex featured it, too, in a podcast entitled "GPT-2 as a step towards General Intelligence".

Compared to any previous system for generating natural language, GPT-2 has a number of remarkable strengths.

I speak fluent EnglishIf you run your experiments talktotransformer.com, you will quickly learn th…

9 месяцев назад @ thegradient.pub
The Economics of AI Today
The Economics of AI Today The Economics of AI Today

Every day we hear claims that Artificial Intelligence (AI) systems are about to transform the economy, creating mass unemployment and vast monopolies.

In September 2017, a group of distinguished economists gathered in Toronto to set out a research agenda for the Economics of Artificial Intelligence (AI).

Previous editions of the Economics of AI conference included papers about the impact of AI in sectors such as media or health-care.

Lack of diversity in the AI research workforce, and the increasing influence of the private sector in setting AI research (and ethical) agendas as part of the industrialization of AI research suggest that this could be a problem, but the evidence base is lackin…

9 месяцев, 1 неделя назад @ thegradient.pub
DataTau DataTau
последний пост 17 часов назад
Text Mining with R: The Free eBook
Text Mining with R: The Free eBook Text Mining with R: The Free eBook

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17 часов назад @ datatau.net
3 Unignorable Reasons Big Data and AI Projects Fail
3 Unignorable Reasons Big Data and AI Projects Fail 3 Unignorable Reasons Big Data and AI Projects Fail

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1 день назад @ datatau.net
Buy and Hold Trading Strategy
Buy and Hold Trading Strategy Buy and Hold Trading Strategy

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1 день, 13 часов назад @ datatau.net
What Does Entrepreneurship After 50 Entail?
What Does Entrepreneurship After 50 Entail? What Does Entrepreneurship After 50 Entail?

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1 день, 17 часов назад @ datatau.net
Find awesome Data Science remote jobs around the globe with DSremote.work
Find awesome Data Science remote jobs around the globe with DSremote.work Find awesome Data Science remote jobs around the globe with DSremote.work

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4 дня, 13 часов назад @ datatau.net
Forsage Clone Script
Forsage Clone Script Forsage Clone Script

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4 дня, 19 часов назад @ datatau.net
Piero Molino on Ludwig 0.3
Piero Molino on Ludwig 0.3 Piero Molino on Ludwig 0.3

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4 дня, 22 часа назад @ datatau.net
Microsoft and the Open Data Institute join together to launch a Peer Learning Network for Data Collaborations
Microsoft and the Open Data Institute join together to launch a Peer Learning Network for Data Collaborations Microsoft and the Open Data Institute join together to launch a Peer Learning Network for Data Collaborations

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5 дней, 9 часов назад @ datatau.net
5,000 images of clothes for training neural networks
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5 дней, 11 часов назад @ datatau.net
Airbnb For Boat Rental Script
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Property Rental Software
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5 дней, 16 часов назад @ datatau.net
Start A Lodging Business On Hotel Booking
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5 дней, 16 часов назад @ datatau.net
Airbnb like Coworking Space Rental Script
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5 дней, 16 часов назад @ datatau.net
Airbnb Like Parking Booking Script
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Easiest Customer Loyalty Guide: The Way To Exceptional Brand
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5 дней, 17 часов назад @ datatau.net
Synced Review
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AI Halloween Avatars! StyleGAN2 Generator Reveals Your Inner Zombie
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7 часов назад @ medium.com
Google ‘mT5’ Pretrained Text-to-Text Transformer Achieves SOTA Performance on Multilingual…
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1 день, 11 часов назад @ medium.com
A Look at Google’s Efforts to Earn Public Trust Through ML Fairness and Responsible AI
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Google Brain Sets New Semi-Supervised Learning SOTA in Speech Recognition
Google Brain Sets New Semi-Supervised Learning SOTA in Speech Recognition Google Brain Sets New Semi-Supervised Learning SOTA in Speech Recognition

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Preferred Networks’ ChainerRL Joins PyTorch Ecosystem as ‘PFRL’
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5 дней, 8 часов назад @ medium.com
ICLR 2021 Submission | ‘Lambda Networks’ Achieve SOTA Accuracy, Save Massive Memory
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6 дней, 11 часов назад @ medium.com
Facebook AI Model Directly Translates 100 Languages Without Using English Data
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UK Researchers Say AI Needs More Animal Sense
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1 неделя, 1 день назад @ medium.com
Google Open-Sources 3D System That Shows How Places Looked in the Past
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MIT, TU Wien & IST Austria Brain-Based AI Self-Drives With Just a Few Neurons
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1 неделя, 4 дня назад @ medium.com
Facebook & CMU Open Catalyst Project Applies AI to Renewable Energy Storage
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1 неделя, 5 дней назад @ medium.com
MIT Researcher Neil Thompson on Deep Learning’s Insatiable Compute Demands and Possible Solutions
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1 неделя, 5 дней назад @ medium.com
NeurIPS 2020 Workshop | Indie GAN Interpolation Method Turns Selfies Into Cartoon Characters
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1 неделя, 6 дней назад @ medium.com
Apple ‘Hi, Speed’ Event: 5G, A14 Bionic Chip, and LiDAR for New iPhones
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2 недели назад @ medium.com
ICLR 2021 Submission: Deeper VAEs Excel on Natural Image Benchmarks
ICLR 2021 Submission: Deeper VAEs Excel on Natural Image Benchmarks ICLR 2021 Submission: Deeper VAEs Excel on Natural Image Benchmarks

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🔬 Science
Papers With Code Papers With Code
последний пост 1 час назад
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).

However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance...

In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent.

We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving.

Experiments show that the agent can learn high-quality PDRs from scratch with elementary raw features, and demonstrates strong performance against the best existing PDRs.

1 час назад @ paperswithcode.com
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks

Feedforward networks (FFN) are ubiquitous structures in neural systems and have been studied to understand mechanisms of reliable signal and information transmission.

Here we show that layer-to-layer heterogeneity arising from lamina-specific cellular properties facilitates signal and information transmission in FFNs.

Specifically, we found that signal transformations, made by each layer of neurons on an input-driven spike signal, demodulate signal distortions introduced by preceding layers.

This mechanism boosts information transfer carried by a propagating spike signal and thereby supports reliable spike signal and information transmission in a deep FFN.

Our study suggests that distinct c…

1 час назад @ paperswithcode.com
EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces
EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces

An increasing need to analyse event-centric cross-lingual information calls for innovative user interaction models that assist users in crossing the language barrier.

However, datasets that reflect user interaction traces in cross-lingual settings required to train and evaluate the user interaction models are mostly missing...

In this paper, we present the EventKG+Click dataset that aims to facilitate the creation and evaluation of such interaction models.

EventKG+Click builds upon the event-centric EventKG knowledge graph and language-specific information on user interactions with events, entities, and their relations derived from the Wikipedia clickstream.

(read more)

1 час назад @ paperswithcode.com
Graph Geometry Interaction Learning
Graph Geometry Interaction Learning Graph Geometry Interaction Learning

While numerous approaches have been developed to embed graphs into either Euclidean or hyperbolic spaces, they do not fully utilize the information available in graphs, or lack the flexibility to model intrinsic complex graph geometry.

To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph... GIL captures a more informative internal structural features with low dimensions while maintaining conformal invariance of each space.

Furthermore, our method endows each node the freedom to determine the importance of each g…

1 час назад @ paperswithcode.com
Transformer-based End-to-End Speech Recognition with Local Dense Synthesizer Attention
Transformer-based End-to-End Speech Recognition with Local Dense Synthesizer Attention Transformer-based End-to-End Speech Recognition with Local Dense Synthesizer Attention

Recently, several studies reported that dot-product selfattention (SA) may not be indispensable to the state-of-theart Transformer models.

Motivated by the fact that dense synthesizer attention (DSA), which dispenses with dot products and pairwise interactions, achieved competitive results in many language processing tasks, in this paper, we first propose a DSA-based speech recognition, as an alternative to SA... To reduce the computational complexity and improve the performance, we further propose local DSA (LDSA) to restrict the attention scope of DSA to a local range around the current central frame for speech recognition.

Finally, we combine LDSA with SA to extract the local and global …

1 час назад @ paperswithcode.com
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor

Keypoint detector and descriptor are two main components of point cloud registration.

Previous learning-based keypoint detectors rely on saliency estimation for each point or farthest point sample (FPS) for candidate points selection, which are inefficient and not applicable in large scale scenes...

This paper proposes Random Sample-based Keypoint Detector and Descriptor Network (RSKDD-Net) for large scale point cloud registration.

The key idea is using random sampling to efficiently select candidate points and using a learning-based method to jointly generate keypoints and descriptors.

Extensive experiments on two large scale outdoor LiDAR datasets show that the proposed RSKDD-Net achieves…

1 час назад @ paperswithcode.com
Social distancing in pedestrian dynamics and its effect on disease spreading
Social distancing in pedestrian dynamics and its effect on disease spreading Social distancing in pedestrian dynamics and its effect on disease spreading

Non-pharmaceutical measures such as social distancing, can play an important role to control an epidemic in the absence of vaccinations.

In this paper, we study the impact of social distancing on epidemics for which it is executable... We use a mathematical model combining human mobility and disease spreading.

For the mobility dynamics, we design an agent based model consisting of pedestrian dynamics with a novel type of force to resemble social distancing in crowded sites.

For the spreading dynamics, we consider the compartmental SIE dynamics plus an indirect transmission with the footprints of the infectious pedestrians being the contagion factor.

We show that the increase in the intensit…

1 час назад @ paperswithcode.com
Graph Information Bottleneck
Graph Information Bottleneck Graph Information Bottleneck

Representation learning of graph-structured data is challenging because both graph structure and node features carry important information.

Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features...

Here we introduce Graph Information Bottleneck (GIB), an information-theoretic principle that optimally balances expressiveness and robustness of the learned representation of graph-structured data.

Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task by maximizing the mutual information between the representation and the target, and simultaneously constraining…

1 час назад @ paperswithcode.com
Self-Supervised Training For Low Dose CT Reconstruction
Self-Supervised Training For Low Dose CT Reconstruction Self-Supervised Training For Low Dose CT Reconstruction

To reduce the dose level without compromising the image quality, low dose CT reconstruction has been offered with the availability of compressed sensing based reconstruction methods...

Deep learning based methods have also been used in low dose CT reconstruction problem in different manners.

In this study, we defined a training scheme to use low dose sinograms as their own training targets.

Using the self-supervised training, the filtering part of the FBP method and the parameters of a denoiser neural network are optimized.

We demonstrate that our method outperforms both conventional and compressed sensing based iterative reconstruction methods qualitatively and quantitatively in the recons…

1 час назад @ paperswithcode.com
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks

Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs).

This problem is challenging as the targets usually move randomly but the coverage range of sensors is limited in angle and distance...

Thus, it is required to coordinate sensors to get ideal target coverage with low power consumption, e.g.

To realize this, we propose a Hierarchical Target-oriented Multi-Agent Coordination (HiT-MAC), which decomposes the target coverage problem into two-level tasks: targets assignment by a coordinator and tracking assigned targets by executors.

Empirical results demonstrate the advantage of HiT-MAC in coverage rate, learn…

1 час назад @ paperswithcode.com
Emergence and Stability of Self-Evolved Cooperative Strategies using Stochastic Machines
Emergence and Stability of Self-Evolved Cooperative Strategies using Stochastic Machines Emergence and Stability of Self-Evolved Cooperative Strategies using Stochastic Machines

To investigate the origin of cooperative behaviors, we developed an evolutionary model of sequential strategies and tested our model with computer simulations.

The sequential strategies represented by stochastic machines were evaluated through games of Iterated Prisoner's Dilemma (IPD) with other agents in the population, allowing co-evolution to occur... We expanded upon past works by proposing a novel mechanism to mutate stochastic Moore machines that enables a richer class of machines to be evolved.

These machines were then subjected to various selection mechanisms and the resulting evolved strategies were analyzed.

We found that cooperation can indeed emerge spontaneously in evolving po…

1 час назад @ paperswithcode.com
An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection
An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection

Polyphonic sound event localization and detection (SELD), which jointly performs sound event detection (SED) and direction-of-arrival (DoA) estimation, has better real-world applicability than separate SED or DoA estimation.

It detects the type and occurrence time of sound events as well as their corresponding DoA angles simultaneously... We study the SELD task from a multi-task learning perspective.

Secondly, a previous finding is that, by using hard parameter-sharing, SELD suffers from a performance loss compared with learning the subtasks separately.

We term the proposed method as Event Independent Network V2 (EINV2), which is an improved version of our previously-proposed method and an …

1 час назад @ paperswithcode.com
The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task
The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task

This paper describes the submission of LMU Munich to the WMT 2020 unsupervised shared task, in two language directions, German<->Upper Sorbian.

Our core unsupervised neural machine translation (UNMT) system follows the strategy of Chronopoulou et al.

We also apply BPE-Dropout to the low resource (Upper Sorbian) data to obtain a more robust system.

We additionally experiment with residual adapters and find them useful in the Upper Sorbian->German direction.

Finally, we ensemble our best-performing systems and reach a BLEU score of 32.4 on German->Upper Sorbian and 35.2 on Upper Sorbian->German.

1 час назад @ paperswithcode.com
Unified Gradient Reweighting for Model Biasing with Applications to Source Separation
Unified Gradient Reweighting for Model Biasing with Applications to Source Separation Unified Gradient Reweighting for Model Biasing with Applications to Source Separation

Recent deep learning approaches have shown great improvement in audio source separation tasks.

In this paper, we propose a simple, unified gradient reweighting scheme, with a lightweight modification to bias the learning process of a model and steer it towards a certain distribution of results.

More specifically, we reweight the gradient updates of each batch, using a user-specified probability distribution.

We apply this method to various source separation tasks, in order to shift the operating point of the models towards different objectives.

We demonstrate different parameterizations of our unified reweighting scheme can be used towards addressing several real-world problems, such as unr…

1 час назад @ paperswithcode.com
Fine-tuning ERNIE for chest abnormal imaging signs extraction
Fine-tuning ERNIE for chest abnormal imaging signs extraction Fine-tuning ERNIE for chest abnormal imaging signs extraction

Chest imaging reports describe the results of chest radiography procedures.

Automatic extraction of abnormal imaging signs from chest imaging reports has a pivotal role in clinical research and a wide range of downstream medical tasks...

However, there are few studies on information extraction from Chinese chest imaging reports.

In this paper, we formulate chest abnormal imaging sign extraction as a sequence tagging and matching problem.

On this basis, we propose a transferred abnormal imaging signs extractor with pretrained ERNIE as the backbone, named EASON (fine-tuning ERNIE with CRF for Abnormal Signs ExtractiON), which can address the problem of data insufficiency.

1 час назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 1 час назад
Scalable Gaussian Process Variational Autoencoders
Scalable Gaussian Process Variational Autoencoders Scalable Gaussian Process Variational Autoencoders

Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors.

Amortized Gaussian process inference through GP-VAEs has led to significant improvements in this regard, but is still inhibited by the intrinsic complexity of exact GP inference... We improve the scalability of these methods through principled sparse inference approaches.

We propose a new scalable GP-VAE model that outperforms existing approaches in terms of runtime and memory footprint, is easy to implement, and allows for joint end-to-end optimization of all components.

(read more)

1 час назад @ paperswithcode.com
Deep reinforced learning enables solving discrete-choice life cycle models to analyze social security reforms
Deep reinforced learning enables solving discrete-choice life cycle models to analyze social security reforms Deep reinforced learning enables solving discrete-choice life cycle models to analyze social security reforms

Discrete-choice life cycle models can be used to, e.g., estimate how social security reforms change employment rate.

Optimal employment choices during the life course of an individual can be solved in the framework of life cycle models...

Mostly, life cycle models have been solved with dynamic programming, which is not feasible when the state space is large, as often is the case in a realistic life cycle model.

Solving such life cycle models requires the use of approximate methods, such as reinforced learning algorithms.

Our results suggest that reinforced learning algorithms are of significant value in analyzing complex life cycle models.

1 час назад @ paperswithcode.com
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians

Hyperparameter optimization of neural networks can be elegantly formulated as a bilevel optimization problem.

While research on bilevel optimization of neural networks has been dominated by implicit differentiation and unrolling, hypernetworks such as Self-Tuning Networks (STNs) have recently gained traction due to their ability to amortize the optimization of the inner objective...

In this paper, we diagnose several subtle pathologies in the training of STNs.

Based on these observations, we propose the $\Delta$-STN, an improved hypernetwork architecture which stabilizes training and optimizes hyperparameters much more efficiently than STNs.

weight decay, dropout, number of cutout holes) wi…

1 час назад @ paperswithcode.com
FastFormers: Highly Efficient Transformer Models for Natural Language Understanding
FastFormers: Highly Efficient Transformer Models for Natural Language Understanding FastFormers: Highly Efficient Transformer Models for Natural Language Understanding

Transformer-based models are the state-of-the-art for Natural Language Understanding (NLU) applications.

Models are getting bigger and better on various tasks...

However, Transformer models remain computationally challenging since they are not efficient at inference-time compared to traditional approaches.

In this paper, we present FastFormers, a set of recipes to achieve efficient inference-time performance for Transformer-based models on various NLU tasks.

We provide effective recipes that can guide practitioners to choose the best settings for various NLU tasks and pretrained models.

1 час назад @ paperswithcode.com
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing

However, any disconnect between training sets and the distribution of real-world objects can introduce bias when BNNs are applied to data...

In this work, we incorporate BNNs with flexible posterior parameterizations into a hierarchical inference framework that allows for the reconstruction of population hyperparameters and removes the bias introduced by the training distribution.

We then apply our approach to test data sets whose lens parameters are drawn from distributions that are drastically different from the training set.

We show that our hierarchical inference framework mitigates the bias introduced by an unrepresentative training set's interim prior.

Our full pipeline, from training…

1 час назад @ paperswithcode.com
ActiveNet: A computer-vision based approach to determine lethargy
ActiveNet: A computer-vision based approach to determine lethargy ActiveNet: A computer-vision based approach to determine lethargy

Include the markdown at the top of your GitHub README.md file to showcase the performance of the model.

Badges are live and will be dynamically updated with the latest ranking of this paper.

1 час назад @ paperswithcode.com
Contrastive Graph Neural Network Explanation
Contrastive Graph Neural Network Explanation Contrastive Graph Neural Network Explanation

Graph Neural Networks achieve remarkable results on problems with structured data but come as black-box predictors.

Transferring existing explanation techniques, such as occlusion, fails as even removing a single node or edge can lead to drastic changes in the graph...

The resulting graphs can differ from all training examples, causing model confusion and wrong explanations.

Thus, we argue that explicability must use graphs compliant with the distribution underlying the training data.

We coin this property Distribution Compliant Explanation (DCE) and present a novel Contrastive GNN Explanation (CoGE) technique following this paradigm.

1 час назад @ paperswithcode.com
A Database and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds
A Database and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds A Database and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics.

There is a dearth of thermally-driven MIT materials, however, which makes delineating these compounds from those that are exclusively insulating or metallic challenging...

We then perform supervised classification, constructing three electronic-state classifiers: metal vs non-metal (M), insulator vs non-insulator (I), and MIT vs non-MIT (T).

We identify two important descriptors that separate metals, insulators, and MIT materials in a 2D feat…

1 час назад @ paperswithcode.com
Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19
Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19 Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19

In this paper we compare the models for the detection and segmentation of Ground Glass Opacity and Consolidation in chest CT scans.

These lesion areas are often associated both with common pneumonia and COVID-19... We train a Mask R-CNN model to segment these areas with high accuracy using three approaches: merging masks for these lesions into one, deleting the mask for Consolidation, and using both masks separately.

The best model achieves the mean average precision of 44.68% using MS COCO criterion for instance segmentation across all accuracy thresholds.

The classification model, COVID-CT-Mask-Net, which learns to predict the presence of COVID-19 vs common pneumonia vs control, achieves …

1 час назад @ paperswithcode.com
AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild
AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild

Occlusion is probably the biggest challenge for human pose estimation in the wild.

Typical solutions often rely on intrusive sensors such as IMUs to detect occluded joints... To make the task truly unconstrained, we present AdaFuse, an adaptive multiview fusion method, which can enhance the features in occluded views by leveraging those in visible views.

The core of AdaFuse is to determine the point-point correspondence between two views which we solve effectively by exploring the sparsity of the heatmap representation.

We also learn an adaptive fusion weight for each camera view to reflect its feature quality in order to reduce the chance that good features are undesirably corrupted by ``b…

1 час назад @ paperswithcode.com
Classification of Important Segments in Educational Videos using Multimodal Features
Classification of Important Segments in Educational Videos using Multimodal Features Classification of Important Segments in Educational Videos using Multimodal Features

Many e-learning platforms provide quality content, but sometimes educational videos are long and cover many topics...

In this paper, we address the problem of assigning importance scores to video segments, that is how much information they contain with respect to the overall topic of an educational video.

We present an annotation tool and a new dataset of annotated educational videos collected from popular online learning platforms.

Moreover, we propose a multimodal neural architecture that utilizes state-of-the-art audio, visual and textual features.

Our experiments investigate the impact of visual and temporal information, as well as the combination of multimodal features on importance pr…

1 час назад @ paperswithcode.com
Sheet diagrams for bimonoidal categories
Sheet diagrams for bimonoidal categories Sheet diagrams for bimonoidal categories

Bimonoidal categories (also known as rig categories) are categories with two monoidal structures, one of which distributes over the other.

We formally define sheet diagrams, a graphical calculus for bimonoidal categories that was informally introduced by Staton... Sheet diagrams are string diagrams drawn on a branching surface, which is itself an extruded string diagram.

Our main result is a soundness and completeness theorem of the usual form for graphical calculi: we show that sheet diagrams form the free bimonoidal category on a signature.

(read more)

1 час назад @ paperswithcode.com
LEAD: Least-Action Dynamics for Min-Max Optimization
LEAD: Least-Action Dynamics for Min-Max Optimization LEAD: Least-Action Dynamics for Min-Max Optimization

The development of efficient optimization methods for two-player min-max games is an active area of research with a timely impact on adversarial formulations including generative adversarial networks (GANs)...

Existing methods for this type of problem typically employ intuitive, carefully hand-designed mechanisms for controlling the problematic rotational dynamics commonly encountered during optimization.

In this work, we take a novel approach to address this issue by casting min-max optimization as a physical system.

We propose LEAD (Least-Action Dynamics), a second-order optimizer that uses the principle of least-action from physics to discover an efficient optimizer for min-max games.

We…

1 час назад @ paperswithcode.com
Multimodal Emotion Recognition with Transformer-Based Self Supervised Feature Fusion
Multimodal Emotion Recognition with Transformer-Based Self Supervised Feature Fusion Multimodal Emotion Recognition with Transformer-Based Self Supervised Feature Fusion

Emotion Recognition is a challenging research area given its complex nature, and humans express emotional cues across various modalities such as language, facial expressions, and speech.

Representation and fusion of features are the most crucial tasks in multimodal emotion recognition research... Self Supervised Learning (SSL) has become a prominent and influential research direction in representation learning, where researchers have access to pre-trained SSL models that represent different data modalities.

For the first time in the literature, we represent three input modalities of text, audio (speech), and vision with features extracted from independently pre-trained SSL models in this pa…

1 час назад @ paperswithcode.com
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

Convex relaxations have emerged as a promising approach for verifying desirable properties of neural networks like robustness to adversarial perturbations.

Widely used Linear Programming (LP) relaxations only work well when networks are trained to facilitate verification...

This precludes applications that involve verification-agnostic networks, i.e., networks not specially trained for verification.

On the other hand, semidefinite programming (SDP) relaxations have successfully be applied to verification-agnostic networks, but do not currently scale beyond small networks due to poor time and space asymptotics.

For two verification-agnostic networks on MNIST and CIFAR-10, we significantly im…

1 день, 3 часа назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 1 час назад
An Exercise in Open Data: Triple Axis Data on Si single crystal
An Exercise in Open Data: Triple Axis Data on Si single crystal An Exercise in Open Data: Triple Axis Data on Si single crystal

Efforts are rising in opening up science by making data more transparent and more available, including the data reduction and evaluation procedures and code.

A strong foundation for this is the FAIR principle, building on Findability, Accessibility, Interoperability, and Reuse of digital assets...

Here, we have used data, which has been made available by the Institute Laue-Langevin and can be identified using a DOI, to follow the FAIR principle in extracting, evaluating and publishing triple axis data, recorded at IN3.

(read more)

1 день, 3 часа назад @ paperswithcode.com
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels

Include the markdown at the top of your GitHub README.md file to showcase the performance of the model.

Badges are live and will be dynamically updated with the latest ranking of this paper.

1 день, 3 часа назад @ paperswithcode.com
Adaptive Gradient Quantization for Data-Parallel SGD
Adaptive Gradient Quantization for Data-Parallel SGD Adaptive Gradient Quantization for Data-Parallel SGD

Many communication-efficient variants of SGD use gradient quantization schemes.

Motivated by this observation, we introduce two adaptive quantization schemes, ALQ and AMQ.

In both schemes, processors update their compression schemes in parallel by efficiently computing sufficient statistics of a parametric distribution.

We improve the validation accuracy by almost 2% on CIFAR-10 and 1% on ImageNet in challenging low-cost communication setups.

Our adaptive methods are also significantly more robust to the choice of hyperparameters.

1 день, 3 часа назад @ paperswithcode.com
A Combinatorial Perspective on Transfer Learning
A Combinatorial Perspective on Transfer Learning A Combinatorial Perspective on Transfer Learning

Human intelligence is characterized not only by the capacity to learn complex skills, but the ability to rapidly adapt and acquire new skills within an ever-changing environment.

In this work we study how the learning of modular solutions can allow for effective generalization to both unseen and potentially differently distributed data... Our main postulate is that the combination of task segmentation, modular learning and memory-based ensembling can give rise to generalization on an exponentially growing number of unseen tasks.

We provide a concrete instantiation of this idea using a combination of: (1) the Forget-Me-Not Process, for task segmentation and memory based ensembling; and (2) G…

1 день, 3 часа назад @ paperswithcode.com
Show and Speak: Directly Synthesize Spoken Description of Images
Show and Speak: Directly Synthesize Spoken Description of Images Show and Speak: Directly Synthesize Spoken Description of Images

This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the need for any text or phonemes.

The basic structure of SAS is an encoder-decoder architecture that takes an image as input and predicts the spectrogram of speech that describes this image...

The final speech audio is obtained from the predicted spectrogram via WaveNet.

Extensive experiments on the public benchmark database Flickr8k demonstrate that the proposed SAS is able to synthesize natural spoken descriptions for images, indicating that synthesizing spoken descriptions for images while bypassing text and phon…

1 день, 3 часа назад @ paperswithcode.com
Fusion of Dual Spatial Information for Hyperspectral Image Classification
Fusion of Dual Spatial Information for Hyperspectral Image Classification Fusion of Dual Spatial Information for Hyperspectral Image Classification

The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral imagery has led to significant improvements in terms of classification performance.

The task of spectral-spatial hyperspectral image classification has remained challenging because of high intraclass spectrum variability and low interclass spectral variability...

In this work, a novel hyperspectral image classification framework using the fusion of dual spatial information is proposed, in which the dual spatial information is built by both exploiting pre-processing feature extraction and post-processing spatial optimization.

The SP extraction method is used here for the first time in the remote …

1 день, 3 часа назад @ paperswithcode.com
Primal-Dual Mesh Convolutional Neural Networks
Primal-Dual Mesh Convolutional Neural Networks Primal-Dual Mesh Convolutional Neural Networks

Recent works in geometric deep learning have introduced neural networks that allow performing inference tasks on three-dimensional geometric data by defining convolution, and sometimes pooling, operations on triangle meshes.

Our method takes features for both edges and faces of a 3D mesh as input and dynamically aggregates them using an attention mechanism.

At the same time, we introduce a pooling operation with a precise geometric interpretation, that allows handling variations in the mesh connectivity by clustering mesh faces in a task-driven fashion.

We provide theoretical insights of our approach using tools from the mesh-simplification literature.

In addition, we validate experimentall…

1 день, 3 часа назад @ paperswithcode.com
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion

We consider the problem of reconstructing a rank-one matrix from a revealed subset of its entries when some of the revealed entries are corrupted with perturbations that are unknown and can be arbitrarily large.

It is not known which revealed entries are corrupted... We propose a new algorithm combining alternating minimization with extreme-value filtering and provide sufficient and necessary conditions to recover the original rank-one matrix.

In particular, we show that our proposed algorithm is optimal when the set of revealed entries is given by an Erd\H{o}s-R\'enyi random graph.

In particular, the "adversarial" workers could even make decisions designed to make the algorithm output an i…

1 день, 3 часа назад @ paperswithcode.com
Bitcoin Trading is Irrational! An Analysis of the Disposition Effect in Bitcoin
Bitcoin Trading is Irrational! An Analysis of the Disposition Effect in Bitcoin Bitcoin Trading is Irrational! An Analysis of the Disposition Effect in Bitcoin

This phenomenon, known as the \emph{disposition effect} in the field of behavioural finance, is well-known and its prevalence has been shown in a number of existing markets...

One might suspect this and hypothesise that cryptocurrency sells occur more frequently in positive market conditions and less frequently in negative market conditions.

In this paper, we expand on existing research and empirically investigate the prevalence of the disposition effect in Bitcoin by testing this hypothesis.

Our results show that investors are indeed subject to the disposition effect, tending to sell their winning positions too soon and holding on to their losing position for too long.

In this study, we sh…

1 день, 3 часа назад @ paperswithcode.com
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes

The early detection of anomalous events in time series data is essential in many domains of application.

In this paper we deal with critical health events, which represent a significant cause of mortality in intensive care units of hospitals...

One of the most common approaches to tackle early anomaly detection problems is standard classification methods.

We leverage this idea to break the original problem into two hierarchical layers, which we hypothesize are easier to solve.

The results suggest that the proposed approach leads to a better performance relative to state of the art approaches for critical health episode prediction.

1 день, 18 часов назад @ paperswithcode.com
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games

We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language.

While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with textual games...

In this work, we aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure.

We propose a stacked hierarchical attention mechanism to construct an explicit representation of the reasoning process by exploiting the structure of the knowledge graph…

1 день, 18 часов назад @ paperswithcode.com
Reversible Jump PDMP Samplers for Variable Selection
Reversible Jump PDMP Samplers for Variable Selection Reversible Jump PDMP Samplers for Variable Selection

A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), have recently shown great promise: they are non-reversible, can mix better than standard MCMC algorithms, and can use subsampling ideas to speed up computation in big data scenarios.

However, current PDMP samplers can only sample from posterior densities that are differentiable almost everywhere, which precludes their use for model choice...

Motivated by variable selection problems, we show how to develop reversible jump PDMP samplers that can jointly explore the discrete space of models and the continuous space of parameters.

Our framework is general: it takes an…

1 день, 18 часов назад @ paperswithcode.com
Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition

In recent years, a number of approaches based on 2D CNNs and 3D CNNs have emerged for video action recognition, achieving state-of-the-art results on several large-scale benchmark datasets.

We then conduct an effort towards a large-scale analysis involving over 300 action recognition models.

Our comprehensive analysis reveals that a) a significant leap is made in efficiency for action recognition, but not in accuracy; b) 2D-CNN and 3D-CNN models behave similarly in terms of spatio-temporal representation abilities and transferability.

Our analysis also shows that recent action models seem to be able to learn data-dependent temporality flexibly as needed.

Our codes and models are available o…

1 день, 18 часов назад @ paperswithcode.com
Not all parameters are born equal: Attention is mostly what you need
Not all parameters are born equal: Attention is mostly what you need Not all parameters are born equal: Attention is mostly what you need

Transformers are widely used in state-of-the-art machine translation, but the key to their success is still unknown.

Through this, we show that the attention and FFN are equally important and fulfil the same functionality in a model.

We show that the decision about whether a component is frozen or allowed to train is at least as important for the final model performance as its number of parameters.

At the same time, the number of parameters alone is not indicative of a component's importance.

Finally, while the embedding layer is the least essential for machine translation tasks, it is the most important component for language modelling tasks.

1 день, 18 часов назад @ paperswithcode.com
On a Guided Nonnegative Matrix Factorization
On a Guided Nonnegative Matrix Factorization On a Guided Nonnegative Matrix Factorization

Fully unsupervised topic models have found fantastic success in document clustering and classification.

However, these models often suffer from the tendency to learn less-than-meaningful or even redundant topics when the data is biased towards a set of features... For this reason, we propose an approach based upon the nonnegative matrix factorization (NMF) model, deemed \textit{Guided NMF}, that incorporates user-designed seed word supervision.

Our experimental results demonstrate the promise of this model and illustrate that it is competitive with other methods of this ilk with only very little supervision information.

(read more)

1 день, 18 часов назад @ paperswithcode.com
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последний пост 1 неделя, 6 дней назад
Рубрика «Читаем статьи за вас». Июль — август 2020 года
Рубрика «Читаем статьи за вас». Июль — август 2020 года Рубрика «Читаем статьи за вас». Июль — август 2020 года

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 неделя, 6 дней назад @ habr.com
Data Fest 2020 — полностью в Online уже завтра
Data Fest 2020 — полностью в Online уже завтра Data Fest 2020 — полностью в Online уже завтра

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 →

1 месяц, 1 неделя назад @ habr.com
Рубрика «Читаем статьи за вас». Июнь 2020 года
Рубрика «Читаем статьи за вас». Июнь 2020 года Рубрика «Читаем статьи за вас». Июнь 2020 года

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…

2 месяца, 1 неделя назад @ habr.com
Итоговые проекты курса Deep Learning in Natural Language Processing (by DeepPavlov Lab)
Итоговые проекты курса Deep Learning in Natural Language Processing (by DeepPavlov Lab) Итоговые проекты курса Deep Learning in Natural Language Processing (by DeepPavlov Lab)

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…

2 месяца, 3 недели назад @ habr.com
Нет времени объяснять, сделай автопилот
Нет времени объяснять, сделай автопилот Нет времени объяснять, сделай автопилот

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…

2 месяца, 3 недели назад @ habr.com
Рубрика «Читаем статьи за вас». Май 2020. Часть 2
Рубрика «Читаем статьи за вас». Май 2020. Часть 2 Рубрика «Читаем статьи за вас». Май 2020. Часть 2

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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 →

4 месяца назад @ habr.com
Рубрика «Читаем статьи за вас». Май 2020. Часть 1
Рубрика «Читаем статьи за вас». Май 2020. Часть 1 Рубрика «Читаем статьи за вас». Май 2020. Часть 1

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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…

4 месяца, 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

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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…

4 месяца, 3 недели 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

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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…

5 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…

5 месяцев, 3 недели 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…

6 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…

6 месяцев, 2 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…

6 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…

7 месяцев назад @ habr.com
Рубрика «Читаем статьи за вас». Январь — Февраль 2020
Рубрика «Читаем статьи за вас». Январь — Февраль 2020 Рубрика «Читаем статьи за вас». Январь — Февраль 2020

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!

Представлены обзоры 11 статей по Computer Vision, Natural Language Processing, Reinforcement learning и другим темам. Читать дальше →

7 месяцев, 1 неделя назад @ habr.com
inFERENCe inFERENCe
последний пост None
The Spectator The Spectator
последний пост 2 месяца, 3 недели назад
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…

2 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.

8 месяцев назад @ blog.shakirm.com
Off the Convex Path
последний пост 1 месяц, 1 неделя назад
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…

1 месяц, 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.

3 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…

4 месяца 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…

6 месяцев назад @ offconvex.org
Machine Learning Mastery Machine Learning Mastery
последний пост 9 часов назад
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…

9 часов назад @ machinelearningmastery.com
Why Use Ensemble Learning?
Why Use Ensemble Learning? Why Use Ensemble Learning?

In this tutorial, you will discover the benefits of using ensemble methods for machine learning.

— Page 1, Ensemble Machine Learning, 2012.

Originally developed to reduce the variance—thereby improving the accuracy—of an automated decision-making system …— Page 1, Ensemble Machine Learning, 2012.

Let’s take a closer look at these two properties in order to better understand the benefits of using ensemble learning on a project.

Related TutorialsBooksArticlesSummaryIn this post, you discovered the benefits of using ensemble methods for machine learning.

2 дня, 9 часов назад @ machinelearningmastery.com
A Gentle Introduction to Ensemble Learning
A Gentle Introduction to Ensemble Learning A Gentle Introduction to Ensemble Learning

This is referred to generally as ensemble machine learning, or simply ensemble learning.

In this post, you will discover a gentle introduction to ensemble learning.

Ensemble Machine LearningApplied machine learning often involves fitting and evaluating models on a dataset.

This is called an ensemble machine learning model, or simply an ensemble, and the process of finding a well-performing ensemble model is referred to as “ensemble learning“.

BooksArticlesSummaryIn this post, you discovered a gentle introduction to ensemble learning.

5 дней, 9 часов назад @ machinelearningmastery.com
6 Books on Ensemble Learning
6 Books on Ensemble Learning 6 Books on Ensemble Learning

Sections and chapters on ensemble learning in the most popular and common machine learning textbooks.

Ensemble Learning Book ListThe books dedicated to the topic of ensemble learning that we will cover are as follows:There are also some books from Packt, but I won’t be reviewing them; they are:Did I miss a book on ensemble learning?

Section 16.2: Classification and regression trees (CART)Section 16.4: BoostingSection 16.6: Ensemble learningThe book “The Elements of Statistical Learning” published in 2016 covers the key ensemble learning algorithms as well as the theory for ensemble learning generally.

Chapter 8: Model Inference and AveragingChapter 10: Boosting and Additive TreesChapter 15:…

1 неделя назад @ machinelearningmastery.com
Softmax Activation Function with Python
Softmax Activation Function with Python Softmax Activation Function with Python

The most common use of the softmax function in applied machine learning is in its use as an activation function in a neural network model.

A linear activation function is simply the sum of the weighted input to the node, required as input for any activation function.

The sigmoid activation function can also be used as an activation function for multi-class classification problems where classes are non-mutually exclusive.

Softmax Activation FunctionThe softmax function is used as the activation function in the output layer of neural network models that predict a multinomial probability distribution.

add ( Dense ( 3 , activation = 'softmax' ) )By definition, the softmax activation will output…

1 неделя, 2 дня назад @ machinelearningmastery.com
How to Develop LARS Regression Models in Python
How to Develop LARS Regression Models in Python How to Develop LARS Regression Models in Python

In this tutorial, you will discover how to develop and evaluate LARS Regression models in Python.

Tutorial OverviewThis tutorial is divided into three parts; they are:LARS Regression Example of LARS Regression Tuning LARS HyperparametersLARS RegressionLinear regression refers to a model that assumes a linear relationship between input variables and the target variable.

# define model model = Lars ( )We can evaluate the LARS Regression model on the housing dataset using repeated 10-fold cross-validation and report the average mean absolute error (MAE) on the dataset.

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] # define model model = Lars ( ) # fit model model .

BooksAPIsArticlesSumm…

1 неделя, 5 дней назад @ machinelearningmastery.com
Nearest Shrunken Centroids With Python
Nearest Shrunken Centroids With Python Nearest Shrunken Centroids With Python

Tutorial OverviewThis tutorial is divided into three parts; they are:Nearest Centroids Algorithm Nearest Centroids With Scikit-Learn Tuning Nearest Centroid HyperparametersNearest Centroids AlgorithmNearest Centroids is a classification machine learning algorithm.

in their 2002 paper titled “Diagnosis Of Multiple Cancer Types By Shrunken Centroids Of Gene Expression.”Nearest Centroids With Scikit-LearnThe Nearest Shrunken Centroids is available in the scikit-learn Python machine learning library via the NearestCentroid class.

# create the nearest centroid model model = NearestCentroid ( metric = 'euclidean' , shrink_threshold = 0.5 )We can demonstrate the Nearest Shrunken Centroids with a w…

2 недели назад @ machinelearningmastery.com
How to Develop LASSO Regression Models in Python
How to Develop LASSO Regression Models in Python How to Develop LASSO Regression Models in Python

In this tutorial, you will discover how to develop and evaluate Lasso Regression models in Python.

Example of Lasso RegressionIn this section, we will demonstrate how to use the Lasso Regression algorithm.

# define model model = Lasso ( alpha = 1.0 )We can evaluate the Lasso Regression model on the housing dataset using repeated 10-fold cross-validation and report the average mean absolute error (MAE) on the dataset.

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] # define model model = Lasso ( alpha = 1.0 ) # fit model model .

BooksAPIsArticlesSummaryIn this tutorial, you discovered how to develop and evaluate Lasso Regression models in Python.

2 недели, 2 дня назад @ machinelearningmastery.com
How to Develop Ridge Regression Models in Python
How to Develop Ridge Regression Models in Python How to Develop Ridge Regression Models in Python

In this tutorial, you will discover how to develop and evaluate Ridge Regression models in Python.

Example of Ridge RegressionIn this section, we will demonstrate how to use the Ridge Regression algorithm.

# define model model = Ridge ( alpha = 1.0 )We can evaluate the Ridge Regression model on the housing dataset using repeated 10-fold cross-validation and report the average mean absolute error (MAE) on the dataset.

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] # define model model = Ridge ( alpha = 1.0 ) # fit model model .

BooksAPIsArticlesSummaryIn this tutorial, you discovered how to develop and evaluate Ridge Regression models in Python.

2 недели, 5 дней назад @ machinelearningmastery.com
How to Develop Elastic Net Regression Models in Python
How to Develop Elastic Net Regression Models in Python How to Develop Elastic Net Regression Models in Python

In this tutorial, you will discover how to develop Elastic Net regularized regression in Python.

Tutorial OverviewThis tutorial is divided into three parts; they are:Elastic Net Regression Example of Elastic Net Regression Tuning Elastic Net HyperparametersElastic Net RegressionLinear regression refers to a model that assumes a linear relationship between input variables and the target variable.

Example of Elastic Net RegressionIn this section, we will demonstrate how to use the Elastic Net regression algorithm.

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] # define model model = ElasticNet ( alpha = 1.0 , l1_ratio = 0.5 ) # fit model model .

BooksAPIsArticlesSummaryIn this tutorial,…

3 недели назад @ machinelearningmastery.com
Robust Regression for Machine Learning in Python
Robust Regression for Machine Learning in Python Robust Regression for Machine Learning in Python

Tutorial OverviewThis tutorial is divided into four parts; they are:Regression With Outliers Regression Dataset With Outliers Robust Regression Algorithms Compare Robust Regression AlgorithmsRegression With OutliersRegression predictive modeling involves predicting a numeric variable given some input, often numerical input.

Machine learning algorithms used for regression predictive modeling tasks are also referred to as “regression” or “regression algorithms.” The most common method is linear regression.

Regression Dataset With OutliersWe can define a synthetic regression dataset using the make_regression() function.

std ( ) return X , y # load dataset X , y = get_dataset ( ) # summarize sh…

3 недели, 2 дня назад @ machinelearningmastery.com
Gaussian Processes for Classification With Python
Gaussian Processes for Classification With Python Gaussian Processes for Classification With Python

Tutorial OverviewThis tutorial is divided into three parts; they are:Gaussian Processes for Classification Gaussian Processes With Scikit-Learn Tune Gaussian Processes HyperparametersGaussian Processes for ClassificationGaussian Processes, or GP for short, are a generalization of the Gaussian probability distribution (e.g.

Gaussian probability distribution functions summarize the distribution of random variables, whereas Gaussian processes summarize the properties of the functions, e.g.

Gaussian processes and Gaussian processes for classification is a complex topic.

# create the model model = GaussianProcessClassifier ( )The complete example of evaluating the Gaussian Processes Classifier m…

3 недели, 5 дней назад @ machinelearningmastery.com
Radius Neighbors Classifier Algorithm With Python
Radius Neighbors Classifier Algorithm With Python Radius Neighbors Classifier Algorithm With Python

After completing this tutorial, you will know:The Nearest Radius Neighbors Classifier is a simple extension of the k-nearest neighbors classification algorithm.

Tutorial OverviewThis tutorial is divided into three parts; they are:Radius Neighbors Classifier Radius Neighbors Classifier With Scikit-Learn Tune Radius Neighbors Classifier HyperparametersRadius Neighbors ClassifierRadius Neighbors is a classification machine learning algorithm.

Radius Neighbors Classifier With Scikit-LearnThe Radius Neighbors Classifier is available in the scikit-learn Python machine learning library via the RadiusNeighborsClassifier class.

Tune Radius Neighbors Classifier HyperparametersThe hyperparameters for …

4 недели назад @ machinelearningmastery.com
Linear Discriminant Analysis With Python
Linear Discriminant Analysis With Python Linear Discriminant Analysis With Python

In this tutorial, you will discover the Linear Discriminant Analysis classification machine learning algorithm in Python.

After completing this tutorial, you will know:The Linear Discriminant Analysis is a simple linear machine learning algorithm for classification.

Tutorial OverviewThis tutorial is divided into three parts; they are:Linear Discriminant Analysis Linear Discriminant Analysis With scikit-learn Tune LDA HyperparametersLinear Discriminant AnalysisLinear Discriminant Analysis, or LDA for short, is a classification machine learning algorithm.

Linear Discriminant Analysis With scikit-learnThe Linear Discriminant Analysis is available in the scikit-learn Python machine learning lib…

1 месяц назад @ machinelearningmastery.com
How to Hill Climb the Test Set for Machine Learning
How to Hill Climb the Test Set for Machine Learning How to Hill Climb the Test Set for Machine Learning

In this tutorial, you will discover how to hill climb the test set for machine learning.

We implicitly hill climb the test set when we overuse the test set to evaluate our modeling pipelines.

Tutorial OverviewThis tutorial is divided into five parts; they are:Hill Climb the Test Set Hill Climbing Algorithm How to Implement Hill Climbing Hill Climb Diabetes Classification Dataset Hill Climb Housing Regression DatasetHill Climb the Test SetMachine learning competitions, like those on Kaggle, provide a complete training dataset as well as just the input for the test set.

PapersArticlesSummaryIn this tutorial, you discovered how to hill climb the test set for machine learning.

We implicitly hil…

1 месяц назад @ machinelearningmastery.com
Lil'Log Lil'Log
последний пост 2 месяца, 3 недели назад
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…

2 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…

4 месяца, 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…

6 месяцев, 3 недели назад @ lilianweng.github.io
Curriculum for Reinforcement Learning
Curriculum for Reinforcement Learning Curriculum for Reinforcement Learning

Next, we will look into several categories of curriculum learning, as illustrated in Fig.

This framework of proposing curriculum automatically through another RL agent was formalized as Teacher-Student Curriculum Learning (TSCL; Matiisen, et al.

(Image source: Jabri, et al 2019)Learning a latent skill space can be done in different ways, such as in Hausman, et al.

(Image source: Czarnecki, et al., 2018)Cited as:@article{weng2020curriculum, title = "Curriculum for Reinforcement Learning", author = "Weng, Lilian", journal = "lilianweng.github.io/lil-log", year = "2020", url = "https://lilianweng.github.io/lil-log/2020/01/29/curriculum-for-reinforcement-learning.html" }References[1] Jeffrey L.…

9 месяцев назад @ lilianweng.github.io
Piekniewski's blog
последний пост 4 месяца, 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.

4 месяца, 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…

6 месяцев, 2 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.

8 месяцев назад @ blog.piekniewski.info
Sebastian Ruder Sebastian Ruder
последний пост None
💼 University and corporation labs
DeepMind DeepMind
последний пост 1 неделя, 2 дня назад
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 неделя, 2 дня 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.

2 недели, 2 дня 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…

1 месяц, 3 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.

4 месяца, 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.

5 месяцев, 1 неделя 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.

6 месяцев, 1 неделя 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…

6 месяцев, 3 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…

7 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…

8 месяцев, 3 недели назад @ deepmind.com
Dopamine and temporal difference learning: A fruitful relationship between neuroscience and AI
Dopamine and temporal difference learning: A fruitful relationship between neuroscience and AI Dopamine and temporal difference learning: A fruitful relationship between neuroscience and AI

Meanwhile, in close contact with this study of reward learning in animals, computer scientists have developed algorithms for reinforcement learning in artificial systems.

A chain of prediction: temporal difference learningReinforcement learning is one of the oldest and most powerful ideas linking neuroscience and AI.

An important breakthrough in solving the problem of reward prediction was the temporal difference learning (TD) algorithm.

Around the same time, in the late 80s and early 90s, neuroscientists were struggling to understand the behaviour of dopamine neurons.

Distributional reinforcement learning

9 месяцев, 2 недели назад @ deepmind.com
AlphaFold: Using AI for scientific discovery
AlphaFold: Using AI for scientific discovery AlphaFold: Using AI for scientific discovery

In our study published today in Nature, we demonstrate how artificial intelligence research can drive and accelerate new scientific discoveries.

Our system, AlphaFold – described in peer-reviewed papers now published in Nature and PROTEINS – is the culmination of several years of work, and builds on decades of prior research using large genomic datasets to predict protein structure.

What is the protein folding problem?

What any given protein can do depends on its unique 3D structure.

Why is protein folding important?

9 месяцев, 2 недели назад @ deepmind.com
Google Google
последний пост 12 часов назад
Partnerships for advanced weather and climate prediction
Partnerships for advanced weather and climate prediction Partnerships for advanced weather and climate prediction

Now, when I chat with my colleagues around the world on video conference, they’re making daily decisions based on the weather around them, just like my father did on the farm.

Together, NESDIS and Google will use AI and ML to amplify NOAA’s environmental monitoring, weather forecasting and climate research using Google Cloud infrastructure.

By working directly with NOAA’s forecast scientists, we’ll be able to utilize the vast amount of satellite and other environmental data that NOAA collects to enhance prediction for extreme weather events, such as hurricanes and tornadoes.

AI2ES assembles researchers from the atmospheric and ocean sciences and risk communication to develop trustworthy AI …

12 часов назад @ blog.google
How Eurovision inspired a research intern's project
How Eurovision inspired a research intern's project How Eurovision inspired a research intern's project

For example, Amit Moryossef developed a machine learning model for sign language detection while interning this year with our Language team in Zurich.

Since our 2021 Research Internship applications opened this month, Amit chatted with us to discuss what his experience has been like.

How did you end up pursuing research around sign language processing?

My internship project was about sign language detection for video conferencing applications.

This work goes hand in hand with my PhD research—making the world more accessible to people who use sign language.

12 часов назад @ blog.google
Rethinking Attention with Performers
Rethinking Attention with Performers Rethinking Attention with Performers

Such sparsity-based architectures usually require additional layers to implicitly produce a full attention mechanism.

Towards FAVOR: Fast Attention via Matrix AssociativityThe decomposition described above allows one to store the implicit attention matrix with linear, rather than quadratic, memory complexity.

While the original attention mechanism multiplies the stored attention matrix with the value input to obtain the final result, after decomposing the attention matrix, one can rearrange matrix multiplications to approximate the result of the regular attention mechanism, without explicitly constructing the quadratic-sized attention matrix.

Left: Standard unidirectional attention requires…

4 дня, 10 часов назад @ ai.googleblog.com
How AI uncovers important contract data
How AI uncovers important contract data How AI uncovers important contract data

At which point a scramble ensues to find the contract, read through it, and discover what exactly was agreed to.

These documents are legally validated by all the parties involved, which means that the data they contain is intrinsically accurate.

This was the best result we’d ever achieved over three years of on-and-off experiments — and, incredibly, Google needed a relatively tiny data set to achieve it.

The data set was small and the model failed on both entity name and signer name.

So as a next step we changed up our labeling and expanded the data set.

5 дней, 11 часов назад @ cloud.google.com
Announcing the Recipients of the 2020 Award for Inclusion Research
Announcing the Recipients of the 2020 Award for Inclusion Research Announcing the Recipients of the 2020 Award for Inclusion Research

At Google, it is our ongoing goal to support faculty who are conducting innovative research that will have positive societal impact.

As part of that goal, earlier this year we launched the Award for Inclusion Research program, a global program that supports academic research in computing and technology addressing the needs of underrepresented populations.

The Award for Inclusion Research program allows faculty and Google researchers an opportunity to partner on their research initiatives and build new and constructive long-term relationships.

We received 100+ applications from over 100 universities, globally, and today we are excited to announce the 16 proposals chosen for funding, focused …

6 дней, 8 часов назад @ ai.googleblog.com
Unifiedpost and Google collaborate on Document AI to automate procurement data capture
Unifiedpost and Google collaborate on Document AI to automate procurement data capture Unifiedpost and Google collaborate on Document AI to automate procurement data capture

A key part of our strategy at Google Cloud is the creation of industry-specific solutions that address vertical needs.

Procurement DocAI is one of our newest solutions, and it’s deployed by many customers and partners including Unifiedpost.

Google Cloud’s Procurement DocAI delivered two key benefits to address Unifiedpost’s business needs:Lower TCO of procure-to-pay processing .

Unifiedpost will be able to lower their TCO of procure-to-pay processing costs by up to 60% with Procurement DocAI.

The collaboration between Google Cloud and Unifiedpost is just one of the latest examples of how we’re providing AI-powered functional solutions to solve business problems by leveraging our Deployed AI…

6 дней, 22 часа назад @ cloud.google.com
Lending DocAI fast tracks the home loan process
Lending DocAI fast tracks the home loan process Lending DocAI fast tracks the home loan process

Artificial intelligence (AI) continues to transform industries across the globe, and business decision makers of all kinds are taking notice. One example is the mortgage industry; lending institutions like banks and mortgage brokers process hundreds of pages of borrower paperwork for every loan - a heavily manual process that adds thousands of dollars to the cost of issuing a loan. In this industry, borrowers and lenders have high expectations; they want a mortgage document processing solution catered to improving operational efficiency, while ensuring speed and data accuracy. They also want a document automation process that helps enhance their current security and compliance posture.At Go…

1 неделя, 1 день назад @ cloud.google.com
How to create and deploy a model card in the cloud with Scikit-Learn
How to create and deploy a model card in the cloud with Scikit-Learn How to create and deploy a model card in the cloud with Scikit-Learn

Machine learning models are now being used to accomplish many challenging tasks. With their vast potential, ML models also raise questions about their usage, construction, and limitations. Documenting the answers to these questions helps to bring clarity and shared understanding. To help advance these goals, Google has introduced model cards.Model cards aim to provide a concise, holistic picture of a machine learning model. To start, a model card explains what a model does, its intended audience, and who maintains it. A model card also provides insight into the construction of the model, including its architecture and the training data used. Not only does a model card include raw performanc…

1 неделя, 4 дня назад @ cloud.google.com
Duplex is getting smarter and making life a little easier
Duplex is getting smarter and making life a little easier Duplex is getting smarter and making life a little easier

Today, during our Search On event, we shared an update on how Duplex and Google Assistant are helping people in their everyday lives.

From providing more accurate business information in products like Google Maps, to booking appointments and reservations on your behalf, to waiting on hold for you, we’re continuing to bring Duplex to new places to make life a little easier.

Keeping local businesses information freshThis pandemic has shown us how critical up-to-date local information is, both for people trying to find services nearby and for businesses looking for ways to serve their customers.

We began using Duplex to automatically update business information and add it to Search and Maps at…

1 неделя, 5 дней назад @ blog.google
Recreating Historical Streetscapes Using Deep Learning and Crowdsourcing
Recreating Historical Streetscapes Using Deep Learning and Crowdsourcing Recreating Historical Streetscapes Using Deep Learning and Crowdsourcing

The next app, Editor, allows users to load the georectified historical maps as the background and then trace their geographic features (e.g., building footprints, roads, etc.).

Finally, our map renderer, Kartta, visualizes the spatiotemporal vector tiles allowing the users to navigate space and time on historical maps.

Warper and Editor work together to let users upload a map, anchor it to a base map using control points, and trace geographic features like building footprints and roads.

High-level overview of rǝ’s 3D reconstruction pipeline, which takes annotated images and maps and prepares them for 3D rendering.

Key ResultsStreet level view of 3D-reconstructed Chelsea, ManhattanConclusion…

1 неделя, 5 дней назад @ ai.googleblog.com
Project Euphonia’s new step: 1,000 hours of speech recordings
Project Euphonia’s new step: 1,000 hours of speech recordings Project Euphonia’s new step: 1,000 hours of speech recordings

Muratcan Cicek, a PhD candidate at UC Santa Cruz, worked as a summer intern on Google’s Project Euphonia, which aims to improve computers’ abilities to understand impaired speech.

This work was especially relevant and important for Muratcan, who was born with cerebral palsy and has a severe speech impairment.

Before his internship, Muratcan recorded 2,000 phrases for Project Euphonia.

The prototype allowed Muratcan to share the transcription in a video call so others could better understand him.

Muratcan says, “Euphonia transformed my communication skills in a way that I can leverage in my career as an engineer without feeling insecure about my condition.”

1 неделя, 5 дней назад @ blog.google
Measuring Gendered Correlations in Pre-trained NLP Models
Measuring Gendered Correlations in Pre-trained NLP Models Measuring Gendered Correlations in Pre-trained NLP Models

In “Measuring and Reducing Gendered Correlations in Pre-trained Models” we perform a case study on BERT and its low-memory counterpart ALBERT, looking at correlations related to gender, and formulate a series of best practices for using pre-trained language models.

We will soon release a series of checkpoints, Zari1, which reduce gendered correlations while maintaining state-of-the-art accuracy on standard NLP task metrics.

At least some of this is due to models preferentially using gendered correlations in reasoning.

While configuration choices often seem innocuous, we find they can cause significant changes for gendered correlations, both for better and for worse.

While configuration choi…

1 неделя, 6 дней назад @ ai.googleblog.com
Sharing our data privacy commitments for the AI era
Sharing our data privacy commitments for the AI era Sharing our data privacy commitments for the AI era

More and more companies want to adopt the latest cloud-based artificial intelligence (AI) and machine learning (ML) technologies, but they are subject to an increasing array of data privacy regulations.

This is an important concern for customers, who are interested in using AI and ML systems to drive better business outcomes while complying with new data privacy laws.

At Google Cloud, we are committed to giving you increased control and visibility over your data.

Helping you address global privacy and data protection requirements enables you to apply machine learning to accelerate your business with confidence.

To learn more about our three pillars of sovereignty in Google Cloud, see this b…

1 неделя, 6 дней назад @ cloud.google.com
Announcing the 2020 Google PhD Fellows
Announcing the 2020 Google PhD Fellows Announcing the 2020 Google PhD Fellows

Google created the PhD Fellowship Program in 2009 to recognize and support outstanding graduate students who seek to influence the future of technology by pursuing exceptional research in computer science and related fields.

Now in its twelfth year, these Fellowships have helped support approximately 500 graduate students globally in North America and Europe, Africa, Australia, East Asia, and India.

It is our ongoing goal to continue to support the academic community as a whole, and these Fellows as they make their mark on the world.

We congratulate all of this year’s awardees!

Algorithms, Optimizations and MarketsJan van den Brand, KTH Royal Institute of TechnologyMahsa Derakhshan, Univers…

2 недели, 5 дней назад @ ai.googleblog.com
Fernanda Viégas puts people at the heart of AI
Fernanda Viégas puts people at the heart of AI Fernanda Viégas puts people at the heart of AI

When Fernanda Viégas was in college, it took three years with three different majors before she decided she wanted to study graphic design and art history.

Today Fernanda, who grew up in Rio de Janeiro, Brazil, is a senior researcher at Google.

She and her colleagues make sure people at Google think about fairness and values–and putting Google’s AI Principles into practice–when they work on artificial intelligence.

Her team recently launched a series of “AI Explorables," a collection of interactive articles to better explain machine learning to everyone.

I recently sat down with Fernanda via Google Meet to talk about her role and the importance of putting people first when it comes to AI.

2 недели, 6 дней назад @ blog.google
OpenAI OpenAI
последний пост 1 месяц 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…

1 месяц 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.

1 месяц, 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.

3 месяца, 2 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…

4 месяца, 1 неделя 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.

4 месяца, 2 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.

4 месяца, 2 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…

5 месяцев, 3 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 …

6 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…

6 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…

6 месяцев, 2 недели назад @ openai.com
OpenAI Standardizes on PyTorch
OpenAI Standardizes on PyTorch OpenAI Standardizes on PyTorch

We are standardizing OpenAI’s deep learning framework on PyTorch.

The main reason we've chosen PyTorch is to increase our research productivity at scale on GPUs.

It is very easy to try and execute new research ideas in PyTorch; for example, switching to PyTorch decreased our iteration time on research ideas in generative modeling from weeks to days.

Going forward we'll primarily use PyTorch as our deep learning framework but sometimes use other ones when there's a specific technical reason to do so.

Many of our teams have already made the switch, and we look forward to contributing to the PyTorch community in upcoming months.

9 месяцев назад @ openai.com
Microsoft Microsoft
последний пост 1 день, 14 часов назад
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 день, 14 часов назад @ blogs.microsoft.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.

5 дней, 17 часов назад @ azure.microsoft.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 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 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 неделя, 1 день назад @ microsoft.com
Novel object captioning surpasses human performance on benchmarks
Novel object captioning surpasses human performance on benchmarks Novel object captioning surpasses human performance on benchmarks

Refining vision and language pretraining for novel object captioningNovel object captioning (NOC) aims to generate image captions capable of describing novel objects that are not present in the caption training data.

Recently, researchers have developed the novel object captioning challenge (nocaps) to evaluate NOC.

In this challenge, existing computer vision techniques can be leveraged to recognize novel objects.

The combined skill achieves the compositionality generalization, allowing for zero-shot captioning on novel objects.

Looking forward: High potential for performance improvementsWe have demonstrated the power of learning visual vocabulary for novel object captioning.

1 неделя, 6 дней назад @ microsoft.com
What’s that? Microsoft’s latest breakthrough, now in Azure AI, describes images as well as people do
What’s that? Microsoft’s latest breakthrough, now in Azure AI, describes images as well as people do What’s that? Microsoft’s latest breakthrough, now in Azure AI, describes images as well as people do

The breakthrough in a benchmark challenge is a milestone in Microsoft’s push to make its products and services inclusive and accessible to all users.

The new model is now available to customers via the Azure Cognitive Services Computer Vision offering, which is part of Azure AI, enabling developers to use this capability to improve accessibility in their own services.

Automatic image captioning helps all users access the important content in any image, from a photo returned as a search result to an image included in a presentation.

The app uses image captioning to describe photos, including those from social media apps.

“So, there are several apps that use image captioning as way to fill in…

1 неделя, 6 дней назад @ blogs.microsoft.com
Announcing advanced Azure Machine Learning nanodegree program with Udacity
Announcing advanced Azure Machine Learning nanodegree program with Udacity

Earlier this year we announced a free ‘introduction to Machine Learning’ course with Udacity, empowering 10,000 scholars from all over the world to learn the basics of machine learning. Today, we announce the new Machine Learning Engineer for Microsoft Azure Nanodegree Program on Udacity—students can now sign up and start taking this new Nanodegree.

2 недели назад @ azure.microsoft.com
Shrinking the ‘data desert’: Inside efforts to make AI systems more inclusive of people with disabilities
Shrinking the ‘data desert’: Inside efforts to make AI systems more inclusive of people with disabilities Shrinking the ‘data desert’: Inside efforts to make AI systems more inclusive of people with disabilities

“We are in a data desert,” said Mary Bellard, principal innovation architect lead at Microsoft who also oversees the AI for Accessibility program.

We don’t have enough data to power these ideas.”To begin to shrink that data desert, Microsoft researchers have been working for the past year and a half to investigate and suggest ways to make AI systems more inclusive of people with disabilities.

Moreover, computer vision algorithms typically learn from large image datasets of pictures downloaded from the internet.

Working with Microsoft funding and researchers, Gurari’s team has developed a new public dataset to train, validate and test image captioning algorithms.

Earlier this year, Microsoft…

2 недели, 1 день назад @ blogs.microsoft.com
Microsoft’s Cecily Morrison awarded MBE for services to inclusive design
Microsoft’s Cecily Morrison awarded MBE for services to inclusive design Microsoft’s Cecily Morrison awarded MBE for services to inclusive design

Microsoft’s Cecily Morrison awarded MBE for services to inclusive designCecily Morrison, a principal researcher at Microsoft’s Research Lab in Cambridge, has been awarded an MBE in the Queen’s Birthday Honours List.

Tell us about the MBEThe MBE is for services to inclusive design, which is where we think about how we can design technology for each and every one of us.

What’s exciting for me is the recognition of how important inclusive design is for the world.

When we think about inclusive design and disability more generally, we don’t see someone’s physical body or cognitive abilities as being different.

While I was there, I realised that it was the way technology is built that informs the…

2 недели, 3 дня назад @ news.microsoft.com
Deliver AI-powered application search with Azure Cognitive Search and BA Insight
Deliver AI-powered application search with Azure Cognitive Search and BA Insight

Data is growing exponentially, and over 80 percent of data is unstructured, creating a challenge for organizations to find and surface the right information to their customers. What organizations need is a solution that enables them to uncover latent insights from all their content by quickly identifying relevant information and meaningful patterns.

3 недели, 1 день назад @ azure.microsoft.com
Azure Machine Learning helps customers stay ahead of challenges
Azure Machine Learning helps customers stay ahead of challenges

Organizations today are striving to build agility and resilience to the fast-changing environment we live in. AI and machine learning innovation can help tackle these emerging challenges and enable cost efficiencies.

3 недели, 1 день назад @ azure.microsoft.com
Archai can design your neural network with state-of-the-art neural architecture search (NAS)
Archai can design your neural network with state-of-the-art neural architecture search (NAS) Archai can design your neural network with state-of-the-art neural architecture search (NAS)

The goal of neural architecture search (NAS) is to have computers automatically search for the best-performing neural networks.

SOURCE CODE GitHub: ArchaiWe’ve sought to address many of these concerns with a goal of making state-of-the-art NAS research more widely usable.

Currently, Differentiable Architecture Search (DARTS), Petridish, Differentiable ArchiTecture Approximation (DATA), and eXperts Neural Architecture Search (XNAS) are implemented.

Search-space abstractions: A significant amount of current NAS research focuses on rather small search spaces made popular by a few early efforts.

A significant amount of current NAS research focuses on rather small search spaces made popular by a…

3 недели, 5 дней назад @ microsoft.com
CodeXGLUE: A benchmark dataset and open challenge for code intelligence
CodeXGLUE: A benchmark dataset and open challenge for code intelligence CodeXGLUE: A benchmark dataset and open challenge for code intelligence

Recent years have seen a surge of applying of statistical models, including neural nets, to code intelligence tasks.

To address this, researchers from Microsoft Research Asia (Natural Language Computing Group) working together with Developer Division and Bing introduce CodeXGLUE, a benchmark dataset and open challenge for code intelligence.

CodeXGLUE includes six existing code intelligence datasets — BigCloneBench, POJ-104, Defects4J, Bugs2Fix, CONCODE, and CodeSearchNet — but also newly introduced datasets that are highlighted in the table above.

One is for binary classification between code, and the other is for retrieving semantically similar code given code as the query.

We encourage re…

4 недели назад @ microsoft.com
Measuring dataset similarity using optimal transport
Measuring dataset similarity using optimal transport Measuring dataset similarity using optimal transport

In our recent paper, “Geometric Dataset Distances via Optimal Transport,” we propose the Optimal Transport Dataset Distance, or the OTDD for short, an approach to defining and computing similarities, or distances, between classification datasets.

Optimal transport: Comparing by ‘transporting’Optimal transport traces its roots back to 18th-century France, where the mathematician Gaspard Monge was concerned with finding optimal ways to transport dirt and rubble from one location to another.

Optimal transport was born as a method to find least-cost schemes to transport dirt and rubble from one place to another.

And here, too, optimal transport comes to our rescue—we can use it to compute these…

1 месяц назад @ microsoft.com
Facebook Facebook
последний пост 5 дней, 10 часов 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

5 дней, 10 часов 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…

1 месяц 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…

3 месяца, 1 неделя назад @ engineering.fb.com
MIT AI MIT AI
последний пост 11 часов назад
AI Cures: data-driven clinical solutions for Covid-19
AI Cures: data-driven clinical solutions for Covid-19 AI Cures: data-driven clinical solutions for Covid-19

Data-driven clinical solutionsOn Sept. 29, over 650 people representing 50 countries and 70 organizations logged on from around the globe for the virtual AI Cures Conference: Data-driven Clinical Solutions for Covid-19.

“Those from the AI community like myself are always asking ourselves if we are solving the right problems,” says Barzilay.

The event was the first in a pair of conferences that took place as part of the AI Cures initiative.

The next event, AI Cures Drug Discovery Conference, which will focus on cutting-edge AI approaches in this area developed by MIT researchers and their collaborators, will be held virtually on Oct. 30.

AI Cures: Data-driven Clinical Solutions was organized…

11 часов назад @ news.mit.edu
Stressed on the job? An AI teammate may know how to help
Stressed on the job? An AI teammate may know how to help Stressed on the job? An AI teammate may know how to help

Pietrucha is among a team of laboratory researchers that aims to develop AI systems that can sense when a person's cognitive fatigue is interfering with their performance.

The system would then suggest interventions, or even take action in dire scenarios, to help the individual recover or to prevent harm.

This vision has its roots in decades-long research at the laboratory in using technology to "read" a person's cognitive or emotional state.

On the other end of the spectrum, the machine might take actions necessary to ensure the survival of the human team member when the human is incapable of doing so.

An AI teammate may know just how to lift their partner up.

1 день, 10 часов назад @ news.mit.edu
“What to Expect When You’re Expecting Robots”
“What to Expect When You’re Expecting Robots” “What to Expect When You’re Expecting Robots”

Together, they have written a new book, “What to Expect When You’re Expecting Robots: The Future of Human-Robot Collaboration,” published this month by Basic Books.

What we can expect, they write, is that robots of the future will no longer work for us, but with us.

As such, Shah and Major say that robots and humans will have to establish a mutual understanding.

“We were working in parallel universes, me in industry, and Julie in academia, each trying to galvanize understanding for the need to accommodate machines and robots,” Major recalls.

“There could also be transponders for people that broadcast to robots,” Shah says.

5 дней, 23 часа назад @ news.mit.edu
Bringing construction projects to the digital world
Bringing construction projects to the digital world Bringing construction projects to the digital world

Now the startup OpenSpace is bringing some of the benefits of digital work to the real world with a solution that uses 360-degree cameras and computer vision to create comprehensive, time-stamped digital replicas of construction sites.

All customers need to do is walk their job site with a small 360-degree camera on their hard hat.

People have long used photographs to document construction projects, and many times contracts for large construction projects require photos of progress to be taken.

Once a few tours of the job site have been uploaded to OpenSpace’s platform, it can map pictures onto site plans within 15 minutes.

But even in states that have resumed construction, Kalanithi says c…

6 дней, 23 часа назад @ news.mit.edu
Translating lost languages using machine learning
Translating lost languages using machine learning Translating lost languages using machine learning

Lost languages are more than a mere academic curiosity; without them, we miss an entire body of knowledge about the people who spoke them.

The team’s ultimate goal is for the system to be able to decipher lost languages that have eluded linguists for decades, using just a few thousand words.

The resulting model can segment words in an ancient language and map them to counterparts in a related language.

The proposed algorithm can assess the proximity between two languages; in fact, when tested on known languages, it can even accurately identify language families.

While Basque and Latin were closer to Iberian than other languages, they were still too different to be considered related.

6 дней, 23 часа назад @ news.mit.edu
Neural pathway crucial to successful rapid object recognition in primates
Neural pathway crucial to successful rapid object recognition in primates Neural pathway crucial to successful rapid object recognition in primates

MIT researchers have identified a brain pathway critical in enabling primates to effortlessly identify objects in their field of vision.

The findings enrich existing models of the neural circuitry involved in visual perception and help to further unravel the computational code for solving object recognition in the primate brain.

Monkey versus machineIn 2019, Kar, DiCarlo, and colleagues identified that primates must use some recurrent circuits during rapid object recognition.

“These results provide evidence that this recurrently connected network is critical for rapid object recognition, the behavior we're studying.

“This study demonstrates the importance of prefrontal cortical circuits in …

1 неделя назад @ news.mit.edu
Eight Lincoln Laboratory technologies named 2020 R&D 100 Award winners
Eight Lincoln Laboratory technologies named 2020 R&D 100 Award winners Eight Lincoln Laboratory technologies named 2020 R&D 100 Award winners

Eight technologies developed by MIT Lincoln Laboratory researchers, either wholly or in collaboration with researchers from other organizations, were among the winners of the 2020 R&D 100 Awards.

The software technologies are solutions to difficulties inherent in analyzing large volumes of data and to problems in maintaining cybersecurity.

Video data from each camera combined on the fly in chronological order can be exported easily.

RIOThe Reconnaissance of Influence Operations (RIO) software system automates the detection of disinformation narratives, networks, and influential actors.

Since 2010, Lincoln Laboratory has had 66 technologies recognized with R&D 100 Awards.

1 неделя назад @ news.mit.edu
A global collaboration to move artificial intelligence principles to practice
A global collaboration to move artificial intelligence principles to practice A global collaboration to move artificial intelligence principles to practice

Today, artificial intelligence — and the computing systems that underlie it — are more than just matters of technology; they are matters of state and society, of governance and the public interest.

Building on those broader principles, the AI Policy Forum, a global effort convened by the MIT Stephen A. Schwarzman College of Computing, will provide an overarching policy framework and tools for governments and companies to implement in concrete ways.

“Our goal is to help policymakers in making practical decisions about AI policy,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing.

We need for this to be a global collaboration and engage scientists, technologists, polic…

1 неделя, 1 день назад @ news.mit.edu
The real promise of synthetic data
The real promise of synthetic data The real promise of synthetic data

Enter synthetic data: artificial information developers and engineers can use as a stand-in for real data.

They call it the Synthetic Data Vault.

When data scientists were asked to solve problems using this synthetic data, their solutions were as effective as those made with real data 70 percent of the time.

GANs are more often used in artificial image generation, but they work well for synthetic data, too: CTGAN outperformed classic synthetic data creation techniques in 85 percent of the cases tested in Xu's study.

Or companies might also want to use synthetic data to plan for scenarios they haven't yet experienced, like a huge bump in user traffic.

1 неделя, 4 дня назад @ news.mit.edu
Machine learning uncovers potential new TB drugs
Machine learning uncovers potential new TB drugs Machine learning uncovers potential new TB drugs

Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs.

MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability.

“This technique is part of a known subfield of machine learning, but people have not brought it to biology,” Berger says.

In recent years, biologists have begun using machine learning to scour huge databases of potential drug compounds to find molecules that interact with particular targets.

To overcome that, the researchers used a technique called Gaussian process to assign uncertainty values to the data th…

1 неделя, 5 дней назад @ news.mit.edu
MIT Proto Ventures program readies new startups for launch
MIT Proto Ventures program readies new startups for launch MIT Proto Ventures program readies new startups for launch

Powered by the MIT Innovation Initiative (MITii) and launched in October 2019, the MIT Proto Ventures program takes an entirely new approach to venture formation from within MIT.

Under the leadership of MITii Venture Builder Luis Ruben Soenksen PhD '19, the program announced last week that two new MIT startups will launch as part of Proto Ventures.

“The Proto Ventures program has nurtured everything I know is relevant in order to develop high-impact scientific ventures in today’s world,” says Soenksen.

MIT Innovation Initiative actively seeks new Venture Builders to develop additional channels within the MIT Proto Ventures program.

“The role of MIT Venture Builder is one of the most excitin…

2 недели назад @ news.mit.edu
SMART researchers receive Intra-CREATE grant for personalized medicine and cell therapy
SMART researchers receive Intra-CREATE grant for personalized medicine and cell therapy SMART researchers receive Intra-CREATE grant for personalized medicine and cell therapy

SMART CAMP was formed in 2019 to focus on ways to produce living cells as medicine delivered to humans to treat a range of illnesses and medical conditions, including tissue degenerative diseases, cancer, and autoimmune disorders.

“We look forward to leveraging the ideas fostered in SMART CAMP to build data analytics and optical imaging capabilities for this pressing medical challenge of glaucoma prediction,” says Barbastathis.

Chew says, “Our earlier SMART and NTU scientific collaborations on progenitor cells in the central nervous system are now being extended to cell therapy translation.

Our research will seek to plug current gaps and deliver valuable impact to cell therapy research and …

3 недели, 5 дней назад @ news.mit.edu
Anticipating heart failure with machine learning
Anticipating heart failure with machine learning Anticipating heart failure with machine learning

Every year, roughly one out of eight U.S. deaths is caused at least in part by heart failure.

The system determined the right level more than half of the time, and correctly diagnosed level 3 cases 90 percent of the time.

Liao’s hope is that these consensus labels can serve as a universal standard to benchmark future machine learning development.

“Our model can turn both images and text into compact numerical abstractions from which an interpretation can be derived,” says Chauhan.

“These correlations will be valuable for improving search through a large database of X-ray images and reports, to make retrospective analysis even more effective,” Chauhan says.

3 недели, 5 дней назад @ news.mit.edu
Milo Phillips-Brown receives inaugural MAC3 Society and Ethics in Computing Research Award
Milo Phillips-Brown receives inaugural MAC3 Society and Ethics in Computing Research Award Milo Phillips-Brown receives inaugural MAC3 Society and Ethics in Computing Research Award

Milo Phillips-Brown, a postdoc in MIT Philosophy, was recently named the inaugural recipient of the MAC3 Society and Ethics in Computing Research Award, which provides support to promising PhD candidates or postdocs conducting interdisciplinary research on the societal and ethical dimensions of computing.

“I’m thrilled to be the inaugural recipient of the MAC3 Society and Ethics in Computing Research Award.

Phillips-Brown PhD ’19 received his doctorate in philosophy from MIT and his bachelor’s in philosophy from Reed College.

He is a research fellow in digital ethics and governance at the Jain Family Institute and a member of the Society for Philosophy and Disability.

The MAC3 Society and E…

3 недели, 6 дней назад @ news.mit.edu
Provably exact artificial intelligence for nuclear and particle physics
Provably exact artificial intelligence for nuclear and particle physics Provably exact artificial intelligence for nuclear and particle physics

The Standard Model of particle physics describes all the known elementary particles and three of the four fundamental forces governing the universe; everything except gravity.

This month’s paper is one in a series aimed at enabling studies in theoretical physics that are currently computationally intractable.

“Our aim is to develop new algorithms for a key component of numerical calculations in theoretical physics,” says Kanwar.

“These calculations inform us about the inner workings of the Standard Model of particle physics, our most fundamental theory of matter.

This paves the way for significantly accelerated research into the fundamental forces of nature using physics-informed machine le…

1 месяц назад @ news.mit.edu
Berkeley AI
последний пост 2 недели назад
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…

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 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…

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…

3 недели, 1 день 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 …

1 месяц, 2 недели назад @ bair.berkeley.edu
AI Will Change the World.Who Will Change AI?We Will.
AI Will Change the World.Who Will Change AI?We Will. AI Will Change the World.Who Will Change AI?We Will.

Who Will Change AI?

Midway Through the ProgramEarly on Day 3 of the 4 day AI4ALL program, I began to really understand the significance of AI.

Through the eye-opening lecture presentations and discussions, I realized that AI really is everywhere!

AI really can be for everyone, whether you’re a developer or a user — it’s not limited to people with mad coding skills.

Final ThoughtsIn less than a week, the AI4ALL program has shaped my view of AI and my learning process.

2 месяца, 1 неделя назад @ bair.berkeley.edu
Estimating the fatality rate is difficult but doable with better data
Estimating the fatality rate is difficult but doable with better data Estimating the fatality rate is difficult but doable with better data

Estimating the fatality rate is difficult but doable with better dataThe case fatality rate quantifies how dangerous COVID-19 is, and how risk of death varies with strata like geography, age, and race.

Current estimates of the COVID-19 case fatality rate (CFR) are biased for dozens of reasons, from under-testing of asymptomatic cases to government misreporting.

The mathematical form of the naive estimator $E_{\rm naive}$ allows us to see easily what we need to do to make it unbiased.

If we collect data properly, even the naive estimator $E_{\rm naive}$ has good performance.

I’d like to re-emphasize a point here: collecting data as above will make the naive estimator $E_{\rm naive}$ unbias…

2 месяца, 3 недели назад @ bair.berkeley.edu
Exploring Exploration: Comparing Children with RL Agents in Unified Environments
Exploring Exploration: Comparing Children with RL Agents in Unified Environments Exploring Exploration: Comparing Children with RL Agents in Unified Environments

Exploring Exploration: Comparing Children with RL Agents in Unified EnvironmentsDespite recent advances in artificial intelligence (AI) research, human children are still by far the best learners we know of, learning impressive skills like language and high-level reasoning from very little data.

The main thing that we know about the child exploration is that children form hypotheses about how the world works, and they engage in exploration to test those hypotheses.

How do AI agents explore?

We do this using DeepMind Lab, an existing platform for training and evaluating RL agents.

Conclusion and future workIn conclusion, this work only begins to touch on a number of deep questions regarding …

3 месяца назад @ bair.berkeley.edu
Can RL From Pixels be as Efficient as RL From State?
Can RL From Pixels be as Efficient as RL From State? Can RL From Pixels be as Efficient as RL From State?

Can RL From Pixels be as Efficient as RL From State?

To date, it has been commonly assumed that RL operating on coordinate state is significantly more data-efficient than pixel-based RL.

In principle, if the environment is fully observable, we should also be able to learn representations that capture the state.

Contrastive Learning in RL SettingCURL was inspired by recent advances in contrastive representation learning in computer vision (CPC, CPCv2, MoCo, SimCLR).

Contrastive Learning vs Data AugmentationIf data augmentation with RL performs so well, do we need unsupervised representation learning?

3 месяца, 1 неделя назад @ bair.berkeley.edu
Decentralized Reinforcement Learning:Global Decision-Making viaLocal Economic Transactions
Decentralized Reinforcement Learning:Global Decision-Making viaLocal Economic Transactions Decentralized Reinforcement Learning:Global Decision-Making viaLocal Economic Transactions

One might naturally wonder what it might take for learning systems to scale in complexity in the same way as programmed systems have.

In other words, the society of primitive agents form a super-agent that solves the MDP as a consequence of the primitive agents' optimal auction strategies.

Societal decision-making frames standard reinforcement learning from the perspective of self-organizing primitive agents.

As we discuss next, the primitive agents need not be restricted to literal actions.

In some sense these complex learning systems are grown rather than built because every component at every abstraction layer is learning.

3 месяца, 2 недели назад @ bair.berkeley.edu
D4RL: Building Better Benchmarks for Offline Reinforcement Learning
D4RL: Building Better Benchmarks for Offline Reinforcement Learning D4RL: Building Better Benchmarks for Offline Reinforcement Learning

In offline RL, we assume all experience is collected offline, fixed and no additional data can be collected.

In order to develop effective algorithms for offline RL, we need widely available benchmarks that are easy to use and can accurately measure progress on this problem.

Narrow and biased data distributions are a common property in real-world datasets that can create problems for offline RL algorithms.

The Flow project proposes to use autonomous vehicles for reducing traffic congestion, which we believe is a compelling use case for offline RL.

Future DirectionsIn the near future, we would be excited to see offline RL applications move from simulated domains to real-world domains where s…

4 месяца назад @ bair.berkeley.edu
Open Compound Domain Adaptation
Open Compound Domain Adaptation Open Compound Domain Adaptation

Therefore, we start rethinking machine learning and domain adaptation systems, and try to introduce a continuous learning protocol under domain adaptation scenario.

Open Compound Domain Adaptation (OCDA)The goal of domain adaptation is to adapt the model learned on the training data to the test data of a different distribution.

We propose to study Open Compound Domain Adaptation (OCDA), a continuous and more realistic setting for domain adaptation (Figure 2).

The newly proposed Open Compound Domain Adaptation (OCDA) serves as a more comprehensive and more realistic touchstone for evaluating domain adaptation and transfer learning systems.

Figure 3: The differences between single-target doma…

4 месяца, 2 недели назад @ bair.berkeley.edu
OmniTact: A Multi-Directional High-Resolution Touch Sensor
OmniTact: A Multi-Directional High-Resolution Touch Sensor OmniTact: A Multi-Directional High-Resolution Touch Sensor

OmniTact: A Multi-Directional High-Resolution Touch SensorHuman thumb next to our OmniTact sensor, and a US penny for scale.

Recently, the GelSight sensor has caught significant interest for learning-based robotics due to its low cost and rich signal.

Comparison of GelSight-style sensor (left side) to our OmniTact sensor (right side).

The OmniTact SensorOur OmniTact sensor design aims to address these limitations.

We additionally compared performance with another multi-directional tactile sensor, the OptoForce sensor, which only had a success rate of 17%.

5 месяцев, 2 недели назад @ bair.berkeley.edu
Four Novel Approaches to Manipulating Fabric using Model-Free and Model-Based Deep Learning in Simulation
Four Novel Approaches to Manipulating Fabric using Model-Free and Model-Based Deep Learning in Simulation Four Novel Approaches to Manipulating Fabric using Model-Free and Model-Based Deep Learning in Simulation

Four Novel Approaches to Manipulating Fabric using Model-Free and Model-Based Deep Learning in SimulationHumans manipulate 2D deformable structures such as fabric on a daily basis, from putting on clothes to making beds.

Model-Free MethodsModel-Free Learning without DemonstrationsIn this paper we present a model-free deep reinforcement learning approach for smoothing cloth.

An example of real robot cloth smoothing experiments with varying starting states and cloth colors.

Since this policy is easy to define, we code an algorithmic supervisor in simulation and perform imitation learning using Dataset Aggregation (DAgger).

Several episodes of both manipulating rope and cloth using our method,…

5 месяцев, 3 недели назад @ bair.berkeley.edu
AWS Machine Learning AWS Machine Learning
последний пост 8 часов назад
Optimizing costs for machine learning with Amazon SageMaker
Optimizing costs for machine learning with Amazon SageMaker Optimizing costs for machine learning with Amazon SageMaker

Amazon SageMaker notebook instancesAn Amazon SageMaker notebook instance is an ML compute instance running the Jupyter Notebook app.

Consider using Amazon SageMaker Studio notebooks for collaborative workloads and when you don’t need to set up compute instances and file storage beforehand.

For more information, see Managed Spot Training: Save Up to 90% On Your Amazon SageMaker Training Jobs.

Consider using Amazon Elastic Inference, which allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%.

Tagging your resourcesConsider tagging your Amazon SageMaker notebook instances and the h…

8 часов назад @ aws.amazon.com
zomato digitizes menus using Amazon Textract and Amazon SageMaker
zomato digitizes menus using Amazon Textract and Amazon SageMaker zomato digitizes menus using Amazon Textract and Amazon SageMaker

This post summarizes how we used Amazon Textract and Amazon SageMaker to develop a customized menu digitization solution.

We first created an in-house OCR solution by stacking a pre-trained text detection model and a pre-trained text recognition model.

Using Amazon SageMaker to build a menu structure detectorThe next component of this solution was to group the detections from Amazon Textract by menu section.

Using Amazon SageMaker to build rule- and ML-based text classifiersThe final component in the solution was a layer of text classification.

To explore these capabilities of Amazon Textract and Amazon SageMaker in more depth, see Automatically extract text and structured data from documen…

10 часов назад @ aws.amazon.com
Video streaming and deep learning: Using Amazon Kinesis Video Streams with Deep Java Library
Video streaming and deep learning: Using Amazon Kinesis Video Streams with Deep Java Library Video streaming and deep learning: Using Amazon Kinesis Video Streams with Deep Java Library

Amazon Kinesis Video Streams allows you to easily ingest video data from connected devices for processing.

DJL can bridge the ease of Kinesis Video Streams with the power of deep learning for your own video analytics application.

In this tutorial, we walk through running an object detection model against a Kinesis video stream.

For your getMediaworker , you need to pass in the data you pulled from the Kinesis Video Streams console describing your video stream.

You can find more information about the Kinesis Video Streams API in the Amazon Kinesis Video Streams Producer SDK Java GitHub repo.

10 часов назад @ aws.amazon.com
Bringing real-time machine learning-powered insights to rugby using Amazon SageMaker
Bringing real-time machine learning-powered insights to rugby using Amazon SageMaker Bringing real-time machine learning-powered insights to rugby using Amazon SageMaker

The following sections explain the dataset and preprocessing steps, the model training, and model deployment procedures.

After exploring different algorithms, the built-in XGBoost in Amazon SageMaker was used due to its better prediction performance and inference speed.

Amazon SageMaker extracts the metric from Amazon CloudWatch Logs with a regular expression.

If you’d like to compare models for A/B testing, Amazon SageMaker supports hosting representational state transfer (REST) endpoints for multiple models.

For instructions on creating a RESTful API, see Call an Amazon SageMaker model endpoint using Amazon API Gateway and AWS Lambda.

1 день, 8 часов назад @ aws.amazon.com
Building an NLU-powered search application with Amazon SageMaker and the Amazon ES KNN feature
Building an NLU-powered search application with Amazon SageMaker and the Amazon ES KNN feature Building an NLU-powered search application with Amazon SageMaker and the Amazon ES KNN feature

In the post Building a visual search application with Amazon SageMaker and Amazon ES , we shared how to build a visual search application using Amazon SageMaker and the Amazon ES KNN’s Euclidean distance metric.

In this post, you build a very simple search application that demonstrates the potential of using KNN with Amazon ES compared to the traditional Amazon ES ranking method, including a web application for testing the KNN-based search queries in your browser.

The application also compares the search results with Elasticsearch match queries to demonstrate the difference between KNN search and full-text search.

The function passes the search query embedding vector as the search value for…

1 день, 8 часов назад @ aws.amazon.com
Arcanum makes Hungarian heritage accessible with Amazon Rekognition
Arcanum makes Hungarian heritage accessible with Amazon Rekognition Arcanum makes Hungarian heritage accessible with Amazon Rekognition

This post discusses their challenges and why they chose Amazon Rekognition as their solution.

Since Arcanum started using Amazon Rekognition, the number of hits has doubled.

“Amazon Rekognition made Hungarian culture, history, and heritage more accessible to the world,” says Előd Biszak, Arcanum CEO.

You can test Amazon Rekognition features such as facial analysis, face comparison, or celebrity recognition on images specific to your use case on the Amazon Rekognition console.

For more information about Amazon Rekognition, see Amazon Rekognition Documentation.

4 дня, 9 часов назад @ aws.amazon.com
Securing Amazon SageMaker Studio connectivity using a private VPC
Securing Amazon SageMaker Studio connectivity using a private VPC Securing Amazon SageMaker Studio connectivity using a private VPC

With the new ability to launch Amazon SageMaker Studio in your Amazon Virtual Private Cloud (Amazon VPC), you can control the data flow from your Amazon SageMaker Studio notebooks.

In this post, we explore how the Amazon SageMaker Studio VPC connectivity works, implement a sample architecture, and demonstrate some security controls in action.

We need an Amazon SageMaker API endpoint to launch Studio notebooks, training jobs, processing jobs, and deploy endpoints, and an Amazon SageMaker RunTime endpoint for services to call the Amazon SageMaker inference endpoint.

Creating an Amazon SageMaker Studio domain inside a VPCWith the infrastructure in place, you’re ready to create an Amazon SageMa…

5 дней, 5 часов назад @ aws.amazon.com
Using Amazon SageMaker inference pipelines with multi-model endpoints
Using Amazon SageMaker inference pipelines with multi-model endpoints Using Amazon SageMaker inference pipelines with multi-model endpoints

Amazon SageMaker multi-model endpoints (MMEs) are a cost-effective solution to deploy a large number of ML models or per-user models.

For more information about MME, see Save on inference costs by using Amazon SageMaker multi-model endpoints.

Create an Amazon SageMaker model with multi-model support.

Create an Amazon SageMaker inference pipeline with an Sklearn model and multi-model enabled linear learner model.

Creating an Amazon SageMaker model with multi-model supportWhen the training jobs are complete, you’re ready to create an MME.

6 дней, 4 часа назад @ aws.amazon.com
Time series forecasting using unstructured data with Amazon Forecast and the Amazon SageMaker Neural Topic Model
Time series forecasting using unstructured data with Amazon Forecast and the Amazon SageMaker Neural Topic Model Time series forecasting using unstructured data with Amazon Forecast and the Amazon SageMaker Neural Topic Model

In this post, we describe how you can combine Amazon SageMaker with Amazon Forecast to include unstructured text data into your time series use cases.

This related data may include time-varying data (such as price, events, and weather) and categorical data (such as color, genre, or region).

Creating and querying the forecastWhen you’re satisfied with the accuracy metrics from your trained Forecast model, it’s time to generate a forecast.

You also learned how to train a topic model and use the generated topic vectors as related time series for Forecast.

To read more about how you can build an end-to-end operational workflow with Amazon Forecast and AWS StepFunctions, see here.

6 дней, 5 часов назад @ aws.amazon.com
Performing batch fraud predictions using Amazon Fraud Detector, Amazon S3, and AWS Lambda
Performing batch fraud predictions using Amazon Fraud Detector, Amazon S3, and AWS Lambda Performing batch fraud predictions using Amazon Fraud Detector, Amazon S3, and AWS Lambda

Create a Lambda function that reads in a CSV file from Amazon S3, calls the Amazon Fraud Detector get_event_prediction function for each record in the CSV file, and writes a CSV file to Amazon S3.

Creating and publishing a detectorYou can create and publish a detector version using the Amazon Fraud Detector console or via the APIs.

AmazonFraudDetectorFullAccessPolicy – Provides permissions to create resources and generate fraud predictions in Amazon Fraud Detector.

– Provides permissions to create resources and generate fraud predictions in Amazon Fraud Detector.

For more information about Amazon Fraud Detector, including links to additional blog posts, sample notebooks, user guide, and API…

6 дней, 7 часов назад @ aws.amazon.com
Automatically detecting personal protective equipment on persons in images using Amazon Rekognition
Automatically detecting personal protective equipment on persons in images using Amazon Rekognition Automatically detecting personal protective equipment on persons in images using Amazon Rekognition

In this post, we show you how you can use Amazon Rekognition PPE detection to improve safety processes by automatically detecting if persons in images are wearing PPE.

With Amazon Rekognition PPE detection, businesses can augment manual checks with automated PPE detection.

Amazon Rekognition PPE detection also predicts a confidence score for whether the protective equipment is covering the corresponding body part of the person.

We have included a demo web application that implements this reference architecture in the Amazon Rekognition PPE detection GitHub repo.

To test PPE detection with your own images, sign in to the Amazon Rekognition console and upload your images in the Amazon Rekogni…

1 неделя, 4 дня назад @ aws.amazon.com
Detecting playful animal behavior in videos using Amazon Rekognition Custom Labels
Detecting playful animal behavior in videos using Amazon Rekognition Custom Labels Detecting playful animal behavior in videos using Amazon Rekognition Custom Labels

We trained a custom model that detects playful behaviors of cats in a video using Amazon Rekognition Custom Labels.

About Amazon Rekognition Custom LabelsAmazon Rekognition Custom Labels is an automated ML feature that enables you to quickly train your own custom models for detecting business-specific objects and scenes from images—no ML experience required.

Amazon Rekognition Custom Labels builds off the existing capabilities of Amazon Rekognition, which is already trained on tens of millions of images across many categories.

You can then use your custom model via the Amazon Rekognition Custom Labels API and integrate it into your applications.

When the inferred result file is uploaded to …

1 неделя, 5 дней назад @ aws.amazon.com
Processing auto insurance claims at scale using Amazon Rekognition Custom Labels and Amazon SageMaker Ground Truth
Processing auto insurance claims at scale using Amazon Rekognition Custom Labels and Amazon SageMaker Ground Truth Processing auto insurance claims at scale using Amazon Rekognition Custom Labels and Amazon SageMaker Ground Truth

Amazon Rekognition Custom Labels provides a UI for viewing and labeling a dataset on the Amazon Rekognition console, suitable for small datasets.

Use the Region Table to choose a Region that supports Amazon Rekognition Custom Labels and Ground Truth.

It’s finally time to train the custom computer vision model using Amazon Rekognition Custom Labels.

For instructions on completing these steps on the console, see Getting Started with Amazon Recognition Custom Labels and Training a custom single class object detection model with Amazon Rekognition Custom Labels.

Amazon Rekognition Custom Labels saves the detailed results for each test image in a JSON file stored in Amazon S3.

1 неделя, 5 дней назад @ aws.amazon.com
Optimizing applications with EagleDream in Amazon CodeGuru Profiler
Optimizing applications with EagleDream in Amazon CodeGuru Profiler Optimizing applications with EagleDream in Amazon CodeGuru Profiler

This post shares our experience using Amazon CodeGuru Profiler to help one of our customers optimize their application under tight deadlines.

We had previously used other application profiling tools and realized how invaluable they can be when diagnosing these types of issues.

CodeGuru workflowAfter we decided on CodeGuru, it was easy to get CodeGuru Profiler installed and start capturing metrics.

CodeGuru Profiler monitors the environment and generates a flame graph and a recommendation report.

To learn more about how to get started with CodeGuru Profiler and CodeGuru Reviewer, check the documentation found here.

1 неделя, 5 дней назад @ aws.amazon.com
Amazon Translate ranked as #1 machine translation provider by Intento
Amazon Translate ranked as #1 machine translation provider by Intento Amazon Translate ranked as #1 machine translation provider by Intento

Customer obsession, one of the key Amazon Leadership principles that guides everything we do at Amazon, has helped Amazon Translate be recognized as an industry leading neural machine translation provider.

This year, Intento ranked Amazon Translate #1 on the list of top-performing machine translation providers in its The State of Machine Translation 2020 report.

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation.

Neural machine translation is a form of machine translation that uses deep learning models to deliver more accurate and more natural sounding translation than traditional statistical and rule-based translat…

1 неделя, 6 дней назад @ aws.amazon.com
NVIDIA
последний пост 10 часов назад
Listening to the Siren Call: Virginia Tech Works with NVIDIA to Test AV Interactions with Emergency Vehicles
Listening to the Siren Call: Virginia Tech Works with NVIDIA to Test AV Interactions with Emergency Vehicles Listening to the Siren Call: Virginia Tech Works with NVIDIA to Test AV Interactions with Emergency Vehicles

The Virginia Tech Transportation Institute has received a federal grant from the U.S. Department of Transportation to study how autonomous vehicles interact with emergency vehicles and public safety providers.

VTTI, the second largest transportation research institute in the country, will use vehicles equipped with the NVIDIA DRIVE Hyperion platform to conduct these evaluations on public roads.

Safety FirstSafely maneuvering around emergency vehicles, including ambulances, fire trucks and police vehicles, is a key component to everyday driving.

AV fleets will need to be able to identify emergency vehicles, recognize whether lights or sirens are running and obey officers directing traffic.

T…

10 часов назад @ blogs.nvidia.com
Government Execs Must Be ‘Brave, Bold and Benevolent’ to Hasten AI Adoption, Experts Say
Government Execs Must Be ‘Brave, Bold and Benevolent’ to Hasten AI Adoption, Experts Say Government Execs Must Be ‘Brave, Bold and Benevolent’ to Hasten AI Adoption, Experts Say

Many encouraged government executives and federal agencies to act with a greater sense of urgency.

This is a really important conversation.”Just Get StartedThese and other speakers cited a common theme: agencies need to get started now.

CNAS recently released a report calling for $25 billion per year in federal AI investment by 2025.

The RAND Corp. released a congressionally mandated assessment of the DoD’s AI posture last year that recommended defense agencies need to create mechanisms for connecting AI researchers, technology developers and operators.

He urged government executives to take an active role in guiding those changes, encouraging them to be “brave, bold and benevolent.”

4 дня, 11 часов назад @ blogs.nvidia.com
Old Clips Become Big Hits: AI-Enhanced Videos Script a Success Story
Old Clips Become Big Hits: AI-Enhanced Videos Script a Success Story Old Clips Become Big Hits: AI-Enhanced Videos Script a Success Story

After his AI-enhanced vintage video went viral, Denis Shiryaev launched a startup to bottle the magic.

He ran them through an expanding workflow of AI models, including DeOldify for adding color and other open-source algorithms for removing visual noise.

He got requests from a media company in the Netherlands to enhance an old film of Amsterdam.

Displays in the Moscow subway played a vintage video he enhanced of the Russian capital.

The RTX card also trains the team’s custom AI models in eight hours, a job that used to take a week.

4 дня, 12 часов назад @ blogs.nvidia.com
What Is Computer Vision?
What Is Computer Vision? What Is Computer Vision?

What Is Computer Vision?

Computer vision is a broad term for the work done with deep neural networks to develop human-like vision capabilities for applications, most often run on NVIDIA GPUs.

Yet computer vision can do much more than just make sports calls.

Developed with convolutional neural networks, computer vision can perform segmentation, classification and detection for a myriad of applications.

With industry changes from computer vision spanning sports, automotive, agriculture, retail, banking, construction, insurance and beyond, much is at stake.

4 дня, 12 часов назад @ blogs.nvidia.com
NVIDIA Xavier Shatters Records, Excels in Back-to-Back Performance Benchmarks
NVIDIA Xavier Shatters Records, Excels in Back-to-Back Performance Benchmarks NVIDIA Xavier Shatters Records, Excels in Back-to-Back Performance Benchmarks

And when it comes to in-vehicle inference, NVIDIA Xavier has been proven the best — and the only — platform capable of real-world AI processing, yet again.

NVIDIA GPUs smashed performance records across AI inference in data center and edge computing systems in the latest round of MLPerf benchmarks, the only consortium-based and peer-reviewed inference performance tests.

NVIDIA Xavier extended its performance leadership demonstrated in the first AI inference tests, held last year, while supporting all new use cases added for energy-efficient, edge compute SoC.

The new NVIDIA A100 GPU, based on the NVIDIA Ampere architecture, also rose above the competition, outperforming CPUs by up to 237x i…

6 дней, 6 часов назад @ blogs.nvidia.com
Winning MLPerf Inference 0.7 with a Full-Stack Approach
Winning MLPerf Inference 0.7 with a Full-Stack Approach Winning MLPerf Inference 0.7 with a Full-Stack Approach

Three trends continue to drive the AI inference market for both training and inference: growing data sets, increasingly complex and diverse networks, and real-time AI services.

MLPerf Inference 0.7 workloads.

MLPerf Inference 0.7 platform categories and scenarios.

Triton delivers nearly equal performance to a highly customized inference serving implementation in MLPerf Inference 0.7 results.

Accelerate inference applications todayGiven the continuing trends driving AI inference, the NVIDIA inference platform and full-stack approach deliver the best performance, highest versatility, and best programmability, as evidenced by the MLPerf Inference 0.7 test performance.

6 дней, 10 часов назад @ developer.nvidia.com
NVIDIA Inference Performance Surges as AI Use Crosses Tipping Point
NVIDIA Inference Performance Surges as AI Use Crosses Tipping Point NVIDIA Inference Performance Surges as AI Use Crosses Tipping Point

The A100, introduced in May, outperformed CPUs by up to 237x in data center inference, according to the MLPerf Inference 0.7 benchmarks.

Adoption of NVIDIA AI Inference Passes Tipping PointAI inference passed a major milestone this year.

Total cloud AI Inference compute capacity on NVIDIA GPUs has been growing roughly tenfold every two years.

Why AI Inference Is HardUse cases for AI are clearly expanding, but AI inference is hard for many reasons.

Our frameworks include NVIDIA Merlin for recommendation systems, NVIDIA Jarvis for conversational AI, NVIDIA Maxine for video conferencing, NVIDIA Clara for healthcare, and many others available today.

6 дней, 10 часов назад @ blogs.nvidia.com
Taking It to the MAX: Adobe Photoshop Gets New NVIDIA AI-Powered Neural Filters
Taking It to the MAX: Adobe Photoshop Gets New NVIDIA AI-Powered Neural Filters Taking It to the MAX: Adobe Photoshop Gets New NVIDIA AI-Powered Neural Filters

Now, those benefits are extending to Adobe Photoshop users with the introduction of GPU-accelerated neural filters.

These AI-powered tools, leveraging NVIDIA RTX GPUs with the Adobe creative applications, are being showcased at Adobe MAX, which is bringing together creators from around the world virtually through Oct. 22.

Adobe and NVIDIA are closely collaborating on AI technology to improve creative tools in Creative Cloud and Photoshop.

October Studio Driver Ready For DownloadAlongside these updates to Adobe Photoshop, Adobe Premiere Pro and Adobe Premiere Elements, there are new releases of Adobe After Effects, Adobe Substance Alchemist, Notch and Daz 3D — all supported in the new Octobe…

1 неделя назад @ blogs.nvidia.com
Preferring Compile-time Errors over Runtime Errors with Vulkan-hpp
Preferring Compile-time Errors over Runtime Errors with Vulkan-hpp Preferring Compile-time Errors over Runtime Errors with Vulkan-hpp

When you use Vulkan-hpp, some of those runtime errors are turned into compile-time errors.

Finally, each value of an enum class skips the prefix VK_ENUM_NAME , as that in turn would be redundant with the namespace and the enum class name.

An enum class value is not allowed to begin with a digit, so the ‘e’ prefix prevents that.

You also can’t compare two enum class values from different enum classes.

The helper class vk::Flags provides functionality that allows you to apply bitwise operators on enum class values from the same enum class, but only those values.

1 неделя, 4 дня назад @ developer.nvidia.com
NVIDIA, Zoom CEOs Talk the Future of Work
NVIDIA, Zoom CEOs Talk the Future of Work NVIDIA, Zoom CEOs Talk the Future of Work

Amid a pandemic that’s put much of the world’s work, learning, even family reunions online, two of the leaders who have made today’s virtual world possible met Thursday on, where else — Zoom — to talk about what’s next.

NVIDIA CEO Jensen Huang and Zoom CEO Eric Yuan spoke Thursday at the online video conference company’s Zoomtopia user event in a casual, wide-ranging conversation.

“We built a time machine,” Huang said, touching on NVIDIA’s work in drug discovery as an example.

“In the future we will say we’re ‘going to the office,’” Huang said.

NVIDIA, for example, has created a platform called NVIDIA Omniverse that lets colleagues working in different places and with different tools collab…

1 неделя, 5 дней назад @ blogs.nvidia.com
Europe Launches New Era in HPC with World’s Fastest AI Supercomputer
Europe Launches New Era in HPC with World’s Fastest AI Supercomputer Europe Launches New Era in HPC with World’s Fastest AI Supercomputer

They include one system dubbed Leonardo, unveiled today at Italy’s CINECA research center, using NVIDIA technologies to deliver the world’s most powerful AI system.

Joining Leonardo are a wave of new AI supercomputers planned for the Czech Republic, Luxembourg and Slovenia that will act as national centers of competence, expanding skills and creating jobs.

NVIDIA GPUs, InfiniBand Power Latest SystemsAll four supercomputers announced use NVIDIA Ampere architecture GPUs and NVIDIA Mellanox HDR InfiniBand networks to tap an ecosystem of hundreds of HPC and AI applications.

Atos, an NVIDIA systems partner headquartered in France, will build three of the four systems; Hewlett Packard Enterprise …

1 неделя, 5 дней назад @ blogs.nvidia.com
AI Draws World’s Smallest Wanted Posters to Apprehend COVID
AI Draws World’s Smallest Wanted Posters to Apprehend COVID AI Draws World’s Smallest Wanted Posters to Apprehend COVID

Using AI and a supercomputer simulation, Ken Dill’s team drew the equivalent of wanted posters for a gang of proteins that make up COVID-19.

The center has a history of applying its knowledge to viral proteins, helping others identify drugs to disable them.

AI provides MELD key information to predict a protein’s 3D structure from its sequence of amino acids.

“So, both these worlds — AI inference and physics simulations — are playing big roles in helping drug discovery,” said Dill.

Playing a Waiting GameThe COVID-19 challenge gave Laufer researchers with a passion for chemistry a driving focus.

1 неделя, 5 дней назад @ blogs.nvidia.com
How GPUs Are Helping Paris’ Public Hospital System Combat the Spread of COVID-19
How GPUs Are Helping Paris’ Public Hospital System Combat the Spread of COVID-19 How GPUs Are Helping Paris’ Public Hospital System Combat the Spread of COVID-19

In the battle against COVID-19, Greater Paris University Hospitals – Public Assistance Hospital of Paris (AP-HP is the French acronym) isn’t just on the medical front lines — it’s on the data front lines as well.

With a network of 39 hospitals treating 8.3 million patients each year, AP-HP is a major actor in the fight against COVID-19.

Along with its COVID-19 cases comes an awful lot of data, including now geodata that can potentially help lessen the impact of the pandemic.

The expected volume of COVID-19 data and geodata would probably have tested AP-HP’s data crunching capacity.

RAPIDS accelerates analytics and data science pipelines on NVIDIA GPUs by taking advantage of GPU parallelism …

1 неделя, 5 дней назад @ blogs.nvidia.com
At GTC, Educators and Leaders Focus on Equity in AI, Developer Diversity
At GTC, Educators and Leaders Focus on Equity in AI, Developer Diversity At GTC, Educators and Leaders Focus on Equity in AI, Developer Diversity

Not everyone needs to be a developer, but everyone will need to be an AI decision maker.

It was one of several GTC events advancing the conversation on diversity, equity and ethics in AI.

This year, we strengthened our support for women and underrepresented developers and scientists at GTC by providing conference passes to members of professional organizations supporting women, Black and Latino developers.

A Forbes report last year named GTC as one of the U.S.’s top conferences for women to attend to further their careers in AI.

Dinner with Strangers: Developer Diversity in AIIn a virtual edition of the popular Dinner with Strangers networking events at GTC, experts from NVIDIA and NSBE par…

1 неделя, 6 дней назад @ blogs.nvidia.com
Lilt CEO Spence Green Talks Removing Language Barriers in Business
Lilt CEO Spence Green Talks Removing Language Barriers in Business Lilt CEO Spence Green Talks Removing Language Barriers in Business

That’s where Lilt, an AI-powered enterprise language translation company, comes in.

Lilt CEO Spence Green spoke with AI Podcast host Noah Kravitz about how the company is using a human-in-the-loop process to achieve fast, accurate and affordable translation.

Lilt does so with a predictive typing software, in which professional translators receive AI-based suggestions of how to translate content.

Lilt was founded in 2015, and evolved from a solely software company into a software and services business.

Make the AI Podcast BetterHave a few minutes to spare?

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 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…

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…

3 месяца, 4 недели 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.

4 месяца, 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…

4 месяца, 3 недели 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 …

5 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…

5 месяцев, 2 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.

5 месяцев, 3 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…

7 месяцев, 3 недели 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…

8 месяцев, 2 недели назад @ eng.uber.com
neptune.ai neptune.ai
последний пост 3 часа назад
Building a Facemask Surveillance System with Drone Technology
Building a Facemask Surveillance System with Drone Technology Building a Facemask Surveillance System with Drone Technology

Introduction to Drone technologyA Drone is simply a flying robot.

Airship schemaFor the context of this article, we will use a programmable Multi-rotor drone — Tello RYZE.

The Tello drone is made by Shenzhen Ryze Technology.

Drone architectureThe Tello drone has a simple architecture as it makes use of a software development kit (SDK) to interact with the drone.

def get_frame (self) : frame_read = pkg.get_frame_read() return frame_read.frameThe drone class has a ‘get_frame’ method to capture frames received from the drone camera.

3 часа назад @ neptune.ai
Machine Learning as a Service: What It Is, When to Use It and What Are the Best Tools Out There
Machine Learning as a Service: What It Is, When to Use It and What Are the Best Tools Out There Machine Learning as a Service: What It Is, When to Use It and What Are the Best Tools Out There

Amazon offers a robust collection of machine learning tools through its Amazon Machine Learning services and its Amazon SageMaker IDE.

Microsoft Azure Machine Learning StudioAzure Machine Learning Studio, is a development environment that creates a resourceful playground both for entry-level and experienced data scientists.

It’s a hub of machine learning solutions and data science model templates provided by the Azure community, which is made up of developers, researchers, data scientists, machine learning practitioners, and startups.

Watson Machine Learning Cloud: The Watson Machine Learning Cloud service is a set of REST APIs that you can call from any programming language to develop appl…

15 часов назад @ neptune.ai
This Week in Machine Learning: AI and Google Search, LO-shot Learning, Dangers of AI, New Deep Learning Models
This Week in Machine Learning: AI and Google Search, LO-shot Learning, Dangers of AI, New Deep Learning Models This Week in Machine Learning: AI and Google Search, LO-shot Learning, Dangers of AI, New Deep Learning Models

It’s been two weeks since our weekly roundup.

Weekly Roundup: October 13-26» Neptune.ai blog – as always, make sure to visit our blog to find out interesting and in-depth articles on machine learning from the last week.

Pfizer and IBM researchers claim to have developed a machine learning technique that can predict Alzheimer’s disease years before symptoms develop.

» Old but gold, the reliable Reddit thread on ML for more news on machine learning.

Have you found something of interest in this weekly roundup?

19 часов назад @ neptune.ai
Best Reinforcement Learning Tutorials, Examples, Projects, and Courses
Best Reinforcement Learning Tutorials, Examples, Projects, and Courses Best Reinforcement Learning Tutorials, Examples, Projects, and Courses

In this list, you’ll find:reinforcement learning tutorials,examples of where to apply reinforcement learning,interesting reinforcement learning projects,courses to master reinforcement learning.

Machine Learning for Humans: Reinforcement Learning – This tutorial is part of an ebook titled ‘Machine Learning for Humans’.

It starts with an overview of reinforcement learning with its processes and tasks, explores different approaches to reinforcement learning, and ends with a fundamental introduction of deep reinforcement learning.

Applications of Reinforcement Learning in Real World – Explore how reinforcement learning frameworks are undervalued when it comes to devising decision-making models…

1 день, 13 часов назад @ neptune.ai
Understanding Gradient Clipping (and How It Can Fix Exploding Gradients Problem)
Understanding Gradient Clipping (and How It Can Fix Exploding Gradients Problem) Understanding Gradient Clipping (and How It Can Fix Exploding Gradients Problem)

Luckily, you can solve it before it occurs (with gradient clipping) – let’s first look at the problem in-depth.

See the article:How to fix exploding gradients: gradient clippingThere are a couple of techniques that focus on Exploding Gradient problems.

Effect of gradient clipping in a recurrent network with two parameters w and b. Gradient clipping can make gradient descent perform more reasonably in the vicinity of extremely steep cliffs.

Gradient clipping in deep learning frameworksNow we know why Exploding Gradients occur and how Gradient Clipping can resolve it.

You’ve successfully understood the Gradient Clipping Methods, what problem it solves, and the Exploding Gradient Problem.

4 дня, 20 часов назад @ neptune.ai
Understanding GAN Loss Functions
Understanding GAN Loss Functions Understanding GAN Loss Functions

Standard GAN loss function (min-max GAN loss)The standard GAN loss function, also known as the min-max loss, was first described in a 2014 paper by Ian Goodfellow et al., titled “Generative Adversarial Networks“.

The Standard GAN loss function can further be categorized into two parts: Discriminator loss and Generator loss.

Alternate GAN loss functionsSeveral different variations to the original GAN loss have been proposed since its inception.

The following modified loss function plays the same min-max game as in the Standard GAN Loss function.

SummaryIn this blog, we discussed:The original Generative Adversarial Networks loss functions along with the modified ones.

5 дней, 20 часов назад @ neptune.ai
Brier Score: Understanding Model Calibration
Brier Score: Understanding Model Calibration Brier Score: Understanding Model Calibration

Well, this shows perfectly how your plans can be destroyed with a not well-calibrated model (also known as an ill-calibrated model, or a model with a very high Brier score).

But others, like the Brier score in the weather forecasting model above, are often neglected.

The mathematical formulation of the Brier Score depends on the type of predicted variable.

In case the event doesn’t occur in reality, the Brier Score will be:As you may have noticed, the Brier score is a distance in the probability domain.

I hope that, after reading this article you have a good understanding of what Brier score and model calibration are and you’ll be able to use that in your ML projects.

6 дней, 19 часов назад @ neptune.ai
Data Augmentation in Python: Everything You Need to Know
Data Augmentation in Python: Everything You Need to Know Data Augmentation in Python: Everything You Need to Know

That is why throughout this article we will mostly talk about performing Data Augmentation with various DL frameworks.

Data Augmentation techniquesWe can apply various changes to the initial data.

Data Augmentation in Deep LearningAs mentioned above in Deep Learning, Data Augmentation is a common practice.

Data Augmentation in TensorFlow and KerasTo augment images when using TensorFlow or Keras as our DL framework we can:Write our own augmentation pipelines or layers using tf.image .

Keras preprocessingAs mentioned above, Keras has a variety of preprocessing layers that may be used for Data Augmentation.

1 неделя назад @ neptune.ai
Deep Dive into ML Models in Production Using Tensorflow Extended (TFX) and Kubeflow
Deep Dive into ML Models in Production Using Tensorflow Extended (TFX) and Kubeflow Deep Dive into ML Models in Production Using Tensorflow Extended (TFX) and Kubeflow

This time you’ll see the just created Kubeflow pipeline, and next to it there’s the OPEN PIPELINES DASHBOARD command.

SetupIn the first cell of your notebook, you’ll install TFX, Kubeflow (kfp) and a package called skaffold:# Install tfx and kfp Python packages.

This file defines the TFX pipeline and various components in the pipeline.

!gsutil cp data /advertising.csv gs://{ GOOGLE_CLOUD_PROJECT }-kubeflowpipelines-default/advert-pred/ data / data .csvYou can navigate to the Cloud Storage browser to confirm that the file has been uploaded.

!tfx pipeline create \ - -pipeline-path=kubeflow_dag_runner.py \ - -endpoint={ENDPOINT} \ - -build-target-image={CUSTOM_TFX_IMAGE}Running the command abo…

1 неделя, 1 день назад @ neptune.ai
PyTorch Loss Functions: The Ultimate Guide
PyTorch Loss Functions: The Ultimate Guide PyTorch Loss Functions: The Ultimate Guide

In this article, we’ll talk about popular loss functions in PyTorch, and about building custom loss functions.

How to add PyTorch loss functionsWhich loss functions are available in PyTorch?

How to create a custom loss function in PyTorchWhat are the loss functions?

How to add PyTorch loss functionsPyTorch’s torch.nn module has multiple standard loss functions that you can use in your project.

Hopefully this article will serve as your quick start guide to using PyTorch loss functions in your machine learning tasks.

1 неделя, 4 дня назад @ neptune.ai
Image Processing Techniques That You Can Use in Machine Learning Projects
Image Processing Techniques That You Can Use in Machine Learning Projects Image Processing Techniques That You Can Use in Machine Learning Projects

Image processing is a method to perform operations on an image to extract information from it or enhance it.

Digital image processing has a broad range of applications such as image restoration, medical imaging, remote sensing, image segmentation, etc.

In this article, we will be covering the top 6 image processing techniques for machine learning.

Image Restoration Linear Filtering Independent Component Analysis Pixelation Template Matching Image Generation Technique (GAN)See also: Best Image Processing Tools Used in Machine Learning1.

An example of image restoration using image inpainting with OpenCVImage impainting also known as “Compensation of paint loss ”.

1 неделя, 5 дней назад @ neptune.ai
This Week in Machine Learning: Quantum Chemistry, Synthetic Biology, GPT-3 Bot on Reddit, and Relationships
This Week in Machine Learning: Quantum Chemistry, Synthetic Biology, GPT-3 Bot on Reddit, and Relationships This Week in Machine Learning: Quantum Chemistry, Synthetic Biology, GPT-3 Bot on Reddit, and Relationships

Machine Learning has application in so many different fields, that sometimes it may be hard to keep track of all the new things happening every day.

» Machine learning speeds up quantum chemistry calculations by Emily Velasco, California Institute of Technology on Phys.org | October 7Quantum chemistry has its limits.

Fortunately, nothing harmful 😉 But it’s still interesting (and funny) so go check yourself 🙂» How LinkedIn Uses Machine Learning in its Recruiter Recommendation Systems by Jesus Rodriguez on KDnuggets | October 8LinkedIn uses some very innovative machine learning techniques to optimize candidate recommendations.

The author discusses whether the current state of Machine Learning…

1 неделя, 6 дней назад @ neptune.ai
Computer Vision in Machine Learning Industry – Top 12 Best Resources and How to Use Them to Follow Current Trends
Computer Vision in Machine Learning Industry – Top 12 Best Resources and How to Use Them to Follow Current Trends Computer Vision in Machine Learning Industry – Top 12 Best Resources and How to Use Them to Follow Current Trends

So to help you stay on top of the latest trends, we’ve gathered the best resources.

He started the community to help other students, developers, and researchers become better at computer vision.

Tombone’s Computer Vision Blog has everything about Deep Learning, Computer Vision, and the algorithms that are shaping the future of Artificial Intelligence.

It should be on your list of the best resources on Computer Vision and Machine Learning.

The author (who has a PhD in Computer Vision and a Master’s in Theology and a Master’s in Philosophy!)

1 неделя, 6 дней назад @ neptune.ai
Essential Pil (Pillow) Image Tutorial (for Machine Learning People)
Essential Pil (Pillow) Image Tutorial (for Machine Learning People) Essential Pil (Pillow) Image Tutorial (for Machine Learning People)

Essential PIL image conceptsWe will start by understanding a couple of PIL image concepts that are critical.

Note: We import the Image class from PIL not Pillow because Pillow is a PIL fork.

from PIL import Image im = Image.open( "peacock.png" )With the image loaded, Let’s start talking about those image concepts.

Understanding the Image classAs we have earlier we had to import the Image class from PIL before reading in our image.

Finally, we save and display the image using PIL show image function.

2 недели назад @ neptune.ai
How to Structure and Manage Natural Language Processing (NLP) Projects
How to Structure and Manage Natural Language Processing (NLP) Projects How to Structure and Manage Natural Language Processing (NLP) Projects

Key points covered:Creating a good project directory structureDealing with changing data: Data VersioningKeeping track of ML experimentsProper evaluation and managing metrics and KPIsModel Deployment: how to get it rightLet’s jump in.

│ ├── docs <- A default Sphinx project; see sphinx-doc.org for details │ ├── models <- Trained and serialized models, model predictions, or model summaries │ ├── notebooks <- Jupyter notebooks.

A data scientist should ask questions like : Any biases in the data – Biases in the data can be of all types.

Evaluating an unsupervised NLP modelAs a special case, let’s discuss how you would evaluate an unsupervised NLP model.

If you enjoyed this post, a great next st…

2 недели, 1 день назад @ neptune.ai
▶️ YouTube
Henry AI Labs Henry AI Labs
последний пост 12 часов назад
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…

12 часов 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 неделя назад @ youtube.com
Small Language Models Are Also Few-Shot Learners
Small Language Models Are Also Few-Shot Learners Small Language Models Are Also Few-Shot Learners

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 неделя, 6 дней назад @ youtube.com
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG)

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…

2 недели, 6 дней назад @ youtube.com
Well-Read Students Learn Better
Well-Read Students Learn Better Well-Read Students Learn Better

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2 месяца, 2 недели назад @ youtube.com
Easy Data Augmentation for Text Classification
Easy Data Augmentation for Text Classification Easy Data Augmentation for Text Classification

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2 месяца, 2 недели назад @ youtube.com
Contrastive Learning for Unpaired Image-to-Image Translation
Contrastive Learning for Unpaired Image-to-Image Translation Contrastive Learning for Unpaired Image-to-Image Translation

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2 месяца, 3 недели назад @ youtube.com
Data Augmentation using Pre-trained Transformer Models
Data Augmentation using Pre-trained Transformer Models Data Augmentation using Pre-trained Transformer Models

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2 месяца, 3 недели назад @ youtube.com
Momentum Predictive Representations Explained!
Momentum Predictive Representations Explained! Momentum Predictive Representations Explained!

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3 месяца назад @ youtube.com
Distribution Augmentation for Generative Modeling
Distribution Augmentation for Generative Modeling Distribution Augmentation for Generative Modeling

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3 месяца назад @ youtube.com
Contrastive Clustering with SwAV
Contrastive Clustering with SwAV Contrastive Clustering with SwAV

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3 месяца, 1 неделя назад @ youtube.com
Don't Stop Pretraining!
Don't Stop Pretraining! Don't Stop Pretraining!

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3 месяца, 1 неделя назад @ youtube.com
CheckList Explained! (ACL 2020 Best Paper)
CheckList Explained! (ACL 2020 Best Paper) CheckList Explained! (ACL 2020 Best Paper)

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3 месяца, 1 неделя назад @ youtube.com
Rethinking Pre-training and Self-Training
Rethinking Pre-training and Self-Training Rethinking Pre-training and Self-Training

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4 месяца, 1 неделя назад @ youtube.com
ImageGPT (Generative Pre-training from Pixels)
ImageGPT (Generative Pre-training from Pixels) ImageGPT (Generative Pre-training from Pixels)

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4 месяца, 1 неделя назад @ youtube.com
Machine Learning and AI Academy Machine Learning and AI Academy
последний пост 3 месяца назад
The Reparameterisation Trick|Variational Inference
The Reparameterisation Trick|Variational Inference The Reparameterisation Trick|Variational Inference

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3 месяца назад @ youtube.com
Wasserstein Robust RL
Wasserstein Robust RL Wasserstein Robust RL

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3 месяца, 1 неделя назад @ youtube.com
Random search with linear policies is as good as TRPO on Mujoco (in 2018)!
Random search with linear policies is as good as TRPO on Mujoco (in 2018)! Random search with linear policies is as good as TRPO on Mujoco (in 2018)!

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3 месяца, 1 неделя назад @ youtube.com
Policy Gradients Reinforcement
Policy Gradients Reinforcement Policy Gradients Reinforcement

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3 месяца, 1 неделя назад @ youtube.com
Variational Inference Lecture I|Probabilistic Modelling|Machine Learning
Variational Inference Lecture I|Probabilistic Modelling|Machine Learning Variational Inference Lecture I|Probabilistic Modelling|Machine Learning

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8 месяцев, 3 недели назад @ youtube.com
Optimisation Algorithms for Machine Learning|ADAM's Story and Proof (Part II)
Optimisation Algorithms for Machine Learning|ADAM's Story and Proof (Part II) Optimisation Algorithms for Machine Learning|ADAM's Story and Proof (Part II)

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9 месяцев, 1 неделя назад @ youtube.com
Optimisation Algorithms for Machine Learning|ADAM's Story and Proof (Part I)
Optimisation Algorithms for Machine Learning|ADAM's Story and Proof (Part I) Optimisation Algorithms for Machine Learning|ADAM's Story and Proof (Part I)

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9 месяцев, 2 недели назад @ youtube.com
Math for Machine Learning (Video Taster)|Machine Learning Basics|Introduction to ML
Math for Machine Learning (Video Taster)|Machine Learning  Basics|Introduction to ML Math for Machine Learning (Video Taster)|Machine Learning Basics|Introduction to ML

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9 месяцев, 3 недели назад @ youtube.com
3blue1brown 3blue1brown
последний пост 1 месяц, 3 недели назад
Hamming codes part 2, the elegance of it all
Hamming codes part 2, the elegance of it all Hamming codes part 2, the elegance of it all

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…

1 месяц, 3 недели назад @ youtube.com
Hamming codes, h■w to ov■rco■e n■ise.
Hamming codes, h■w to ov■rco■e n■ise. Hamming codes, h■w to ov■rco■e n■ise.

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…

1 месяц, 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
Group theory and why I love 808,017,424,794,512,875,886,459,904,961,710,757,005,754,368,000,000,000 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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2 месяца, 1 неделя назад @ youtube.com
The impossible chessboard puzzle
The impossible chessboard puzzle The impossible chessboard puzzle

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3 месяца, 3 недели назад @ youtube.com
Tips to be a better problem solver [Last lecture] | Lockdown math ep. 10
Tips to be a better problem solver [Last lecture] | Lockdown math ep. 10 Tips to be a better problem solver [Last lecture] | Lockdown math ep. 10

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5 месяцев, 1 неделя назад @ youtube.com
Intuition for i to the power i | Lockdown math ep. 9
Intuition for i to the power i | Lockdown math ep. 9 Intuition for i to the power i | Lockdown math ep. 9

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5 месяцев, 2 недели назад @ youtube.com
Does contact tracing necessarily sacrifice privacy? (via Nicky Case)
Does contact tracing necessarily sacrifice privacy? (via Nicky Case) Does contact tracing necessarily sacrifice privacy? (via Nicky Case)

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5 месяцев, 2 недели назад @ youtube.com
The power tower puzzle | Lockdown math ep. 8
The power tower puzzle | Lockdown math ep. 8 The power tower puzzle | Lockdown math ep. 8

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 …

5 месяцев, 2 недели назад @ youtube.com
What makes the natural log "natural"? | Lockdown math ep. 7
What makes the natural log "natural"? | Lockdown math ep. 7 What makes the natural log "natural"? | Lockdown math ep. 7

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5 месяцев, 3 недели назад @ youtube.com
Logarithm Fundamentals | Lockdown math ep. 6
Logarithm Fundamentals | Lockdown math ep. 6 Logarithm Fundamentals | Lockdown math ep. 6

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5 месяцев, 3 недели назад @ youtube.com
Imaginary interest rates | Lockdown math ep. 5
Imaginary interest rates | Lockdown math ep. 5 Imaginary interest rates | Lockdown math ep. 5

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5 месяцев, 4 недели назад @ youtube.com
What is Euler's formula actually saying? | Lockdown math ep. 4
What is Euler's formula actually saying? | Lockdown math ep. 4 What is Euler's formula actually saying? | Lockdown math ep. 4

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6 месяцев назад @ youtube.com
Complex number fundamentals | Lockdown math ep. 3
Complex number fundamentals | Lockdown math ep. 3 Complex number fundamentals | Lockdown math ep. 3

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6 месяцев назад @ youtube.com
Trigonometry fundamentals | Lockdown math ep. 2
Trigonometry fundamentals | Lockdown math ep. 2 Trigonometry fundamentals | Lockdown math ep. 2

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6 месяцев, 1 неделя назад @ youtube.com
The simpler quadratic formula | Lockdown math ep. 1
The simpler quadratic formula | Lockdown math ep. 1 The simpler quadratic formula | Lockdown math ep. 1

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6 месяцев, 1 неделя назад @ youtube.com
Two Minute Papers Two Minute Papers
последний пост 6 часов назад
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…

6 часов 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…

3 дня, 12 часов 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 неделя назад @ youtube.com
This Is What Simulating a 100 Million Particles Looks Like!
This Is What Simulating a 100 Million Particles Looks Like! This Is What Simulating a 100 Million Particles Looks Like!

❤️ 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 неделя, 4 дня 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…

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…

2 недели, 3 дня назад @ youtube.com
This AI Can Deal With Body Shape Variation!
This AI Can Deal With Body Shape Variation! This AI Can Deal With Body Shape Variation!

❤️ 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/lavanyashukla/cnndetection/reports/Detecting-CNN-Generated-Images--Vmlldzo2MTU1Mw 📝 The paper "Learning Body Shape Variation in Physics-based Characters" is available here:

http://mrl.snu.ac.kr/publications/ProjectMorphCon/MorphCon.html 🙏 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 назад @ youtube.com
Beautiful Results From 30 Years Of Light Transport Simulation ☀️
Beautiful Results From 30 Years Of Light Transport Simulation ☀️ Beautiful Results From 30 Years Of Light Transport Simulation ☀️

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Specular Manifold Sampling for Rendering High-Frequency Caustics and Glints" is available here:

http://rgl.epfl.ch/publications/Zeltner2020Specular My rendering course is available here, and is free for everyone: https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/ The PostDoc call is available here - https://www.cg.tuwien.ac.at/news/2020-10-02-Lighting-Simulation-Architectural-Design-%E2%80%93-Post-Doc-Position Mitsuba Renderer: https://www.mitsuba-renderer.org/

Also check out Blender and Cycles! - https://www.blender.org/ Credits: The test scenes use textures from CC0 Textures and c…

3 недели, 3 дня назад @ youtube.com
AI-Based Style Transfer For Video…Now in Real Time!
AI-Based Style Transfer For Video…Now in Real Time! AI-Based Style Transfer For Video…Now in Real Time!

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4 недели назад @ youtube.com
Elon Musk’s Neuralink Puts An AI Into Your Brain! 🧠
Elon Musk’s Neuralink Puts An AI Into Your Brain! 🧠 Elon Musk’s Neuralink Puts An AI Into Your Brain! 🧠

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1 месяц назад @ youtube.com
This AI Creates Real Scenes From Your Photos!
This AI Creates Real Scenes From Your Photos! This AI Creates Real Scenes From Your Photos!

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1 месяц назад @ youtube.com
AI Makes Video Game After Watching Tennis Matches!
AI Makes Video Game After Watching Tennis Matches! AI Makes Video Game After Watching Tennis Matches!

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1 месяц, 1 неделя назад @ youtube.com
Can An AI Generate Original Art? 👨‍🎨
Can An AI Generate Original Art? 👨‍🎨 Can An AI Generate Original Art? 👨‍🎨

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1 месяц, 1 неделя назад @ youtube.com
Simulating a Rocket Launch! 🚀
Simulating a Rocket Launch! 🚀 Simulating a Rocket Launch! 🚀

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1 месяц, 2 недели назад @ youtube.com
This AI Creates Human Faces From Your Sketches!
This AI Creates Human Faces From Your Sketches! This AI Creates Human Faces From Your Sketches!

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1 месяц, 2 недели назад @ youtube.com
DataFest Video DataFest Video
последний пост 10 часов назад
Yuri Maximov: Integration in extremely high dimensions
Yuri Maximov: Integration in extremely high dimensions Yuri Maximov: Integration in extremely high dimensions

Data Fest Online 2020 Math Optimization Track https://ods.ai/tracks/optimization-df2020 In this talk we discuss how to compute an integral (or find an expected value of a function) in a high dimensional space. To this end, we first discuss an importance sampling technique which stands for a Monte-Carlo type approximation to the integral by changing the probability measure. Secondly, we describe various importance sampling methods from the (convex and non-convex) optimization perspective, and explore the importance sampling extensions and limitations. Later on, we consider applications of the importance sampling to Beyesian statistics, stochastic optimization, and optimal control. At the end…

10 часов назад @ youtube.com
Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies: QGOpt tutorial
Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies: QGOpt tutorial Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies: QGOpt tutorial

Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 In the last video, we consider a quantum gate decomposition problem and show how this problem can be resolved using QGOpt. We go through code and discuss all features of QGOpt usage. Register and get access to the tracks: https://ods.ai/events/datafest2020

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10 часов назад @ youtube.com
Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies: QGOpt
Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies: QGOpt Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies: QGOpt

Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 In this video, we present QGOpt, a TensorFlow based library for Riemannian optimization in quantum mechanics. Register and get access to the tracks: https://ods.ai/events/datafest2020

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10 часов назад @ youtube.com
Ilia Luchnikov & Alexander Ryzhov: Application of Riemannian Optimization to Quantum Technologies
Ilia Luchnikov & Alexander Ryzhov: Application of Riemannian Optimization to Quantum Technologies Ilia Luchnikov & Alexander Ryzhov: Application of Riemannian Optimization to Quantum Technologies

Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 Many problems of quantum technologies and quantum mechanics can be formulated as constrained optimization problems. These problems can be solved by using optimization on Riemannian manifolds. We show several examples of Riemannian optimization applications to quantum technologies and quantum mechanics tasks in the given video. Register and get access to the tracks: https://ods.ai/events/datafest2020

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10 часов назад @ youtube.com
Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies.Quantum dynamics
Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies.Quantum dynamics Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies.Quantum dynamics

Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 In the present video, we discuss the dynamics of quantum systems. In the classical case, a system's dynamics is often described by a stochastic matrix that induces a Markov process. In the quantum case, there is an analog of a stochastic matrix called a quantum channel. As usual, we compare quantum and classical cases an emphasize analogies and differences between them. Register and get access to the tracks: https://ods.ai/events/datafest2020

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10 часов назад @ youtube.com
Ilia Luchnikov&Alexander Ryzhov Riemannian Optimization for Quantum Technologies.Quantum observables
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Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 In the second video, we discuss quantum observables. Essentially quantum observables are physical observables that can be measured in an experiment. Due to the features of quantum mechanics, quantum observables are different in comparison with classical ones. We discuss this difference in the video and pay attention to analogies between quantum and classical cases. Register and get access to the tracks: https://ods.ai/events/datafest2020

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10 часов назад @ youtube.com
Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies. Quantum states
Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies. Quantum states Ilia Luchnikov & Alexander Ryzhov: Riemannian Optimization for Quantum Technologies. Quantum states

Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 In the first video, we introduce a concept of density matrix, which is a quantum analog of a classical probability distribution. Density matrix fully describes the state of a quantum system, like in the classical case, a probability distribution describes the state of a classical system. We also discuss the properties of density matrices by making a comparison of density matrices with probability distributions. Register and get access to the tracks: https://ods.ai/events/datafest2020

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10 часов назад @ youtube.com
Maxim Kochurov: Riemannian Optimization part 3
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Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 In the last video, we will bind all the parts together into the full algorithm. Register and get access to the tracks: https://ods.ai/events/datafest2020

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10 часов назад @ youtube.com
Maxim Kochurov: Riemannian Optimization part 2
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Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 In the second video we will go through the concepts of Differential geometry and build intuition about what should be going on in the optimization. Register and get access to the tracks: https://ods.ai/events/datafest2020

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10 часов назад @ youtube.com
Maxim Kochurov: Riemannian Optimization part 1
Maxim Kochurov: Riemannian Optimization part 1 Maxim Kochurov: Riemannian Optimization part 1

Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 In the first view we will compare Euclidean Optimization an Riemannian Optimization at high level identifying blank spots in the understanding. Register and get access to the tracks: https://ods.ai/events/datafest2020

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10 часов назад @ youtube.com
DataFest A/B Testing Track Premiere
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Data Fest Online 2020 https://fest.ai/2020/

A/B Testing Track https://ods.ai/tracks/ab-testing-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

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4 дня, 12 часов назад @ youtube.com
Ildar Safilo: A/B splitters and dealing with small data
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Data Fest Online 2020 https://fest.ai/2020/

A/B Testing Track https://ods.ai/tracks/ab-testing-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

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4 дня, 12 часов назад @ youtube.com
Olga Kuryatnikova: Polynomial Optimization
Olga Kuryatnikova: Polynomial Optimization Olga Kuryatnikova: Polynomial Optimization

Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 Polynomial optimization (PO) is a powerful tool for translating real-life situations into mathematical language. The goal of PO is to find a global maximum or minimum of a problem where we optimize a multivariate polynomial objective over a set described by polynomial constraints. PO algorithms can provide better results than the algorithms searching for local optima, but these results might come at a high computational cost. Register and get access to the tracks: https://ods.ai/events/datafest2020

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5 дней, 11 часов назад @ youtube.com
Denis Timonin about AMP/FP16 and Tensor Cores
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Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

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5 дней, 11 часов назад @ youtube.com
Nikolay Nikitin: Structural Learning for Composite Models Using Evolutionary Optimization
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Data Fest Online 2020 https://fest.ai/2020/

Math Optimization Track https://ods.ai/tracks/optimization-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

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5 дней, 11 часов назад @ youtube.com
Семинары JetBrains Research Семинары JetBrains Research
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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…

2 дня, 9 часов 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…

6 дней, 14 часов назад @ youtube.com
Предсказание липофильности при помощи молекулярных подструктур и multitask обучения
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1 неделя, 1 день назад @ youtube.com
A Game Theoretic Framework for Model Based Reinforcement Learning
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Model-based reinforcement learning (MBRL) набирает всё большую популярность благодаря возможности использовать меньше данных для обучения и использованию off-policy данных. Однако, проектирование стабильных и эффективных MBRL алгоритмов всё ещё остаётся тяжёлой задачей. Авторы статьи предлагают фреймворк, который позволяет рассматривать MBRL алгоритм как игру между: (1) игроком политики, максимизирующим награду при текущей модели; (2) игроком модели, который старается обучиться на данных о среде, собранных игроком политики. Для проектирования алгоритма, авторы предлагают использовать модель Штакельберга между двумя игроками и показывают, что её можно решить с помощью двухуровневой оптимизац…

1 неделя, 2 дня назад @ youtube.com
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1 неделя, 3 дня назад @ youtube.com
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Предсказание трехмерной структуры участка антитела — петли CDR-H3 — является важной нерешенной задачей в области биоинформатики. Различных антител очень много, но большая часть участков антител остается неизменной, меняются лишь небольшие петли, ответственные за прикрипление к антигену. Существующие алгоритмы могут хорошо предсказать общую форму антитела, но сильно ошибаются в этих местах. Всего таких участков 6, и основные проблемы вызывает петля CDR-H3, а остальные петли уже достаточно хорошо моделируются. На семинаре будут представлен обзор существующих методов машинного обучения для предсказания структуры CDR-H3, а также будут затронуты некоторые проблемы, возникающие при решении этой з…

1 неделя, 5 дней назад @ youtube.com
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2 недели, 6 дней назад @ youtube.com
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4 месяца, 3 недели назад @ youtube.com
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5 месяцев назад @ youtube.com
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5 месяцев, 1 неделя назад @ youtube.com
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5 месяцев, 2 недели назад @ youtube.com
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5 месяцев, 3 недели назад @ youtube.com
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5 месяцев, 4 недели назад @ youtube.com
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5 месяцев, 4 недели назад @ youtube.com
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5 месяцев, 4 недели назад @ youtube.com
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5 месяцев, 4 недели назад @ youtube.com
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5 месяцев, 4 недели назад @ youtube.com
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5 месяцев, 4 недели назад @ youtube.com
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Data Fest Online 2020

SysML track Как мы готовим переобучение моделей под Spark и не только. Рецепты и особенности. 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

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SysML track Рассказ о том, как силами DS завозим продакшн решения на GCP на примере созданной системы мониторинга метрик.

Полностью самостоятельно написанное решение с применением ансамбля моделек LSTM и бустинга для контроля нормального поведения метрик в множестве разрезов.

Процесс завезения решений ускорили с таким подходом. Работаем и с локальными серверами и с облаком, есть понимание плюсов и минусов.

Развитие систем с ML гораздо быстрее работает непосредственно, когда компетенции прода в команде DS - какие компетенции нужны, чтобы делать хорошо прод в облачке

Полностью выложу секреты-лайфхаки реализации системы мониторинга аномалий во временных рядах с дообучение…

11 часов назад @ youtube.com
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Евгений Ермаков: Есть 2 стула - Data Vault и Anchor Modeling, на какой сядешь, на какой DWH посадишь Евгений Ермаков: Есть 2 стула - Data Vault и Anchor Modeling, на какой сядешь, на какой DWH посадишь

Data Fest Online 2020

SysML track Общепринятым и проверенным временем подходом к построению DWH является схема «Звезда» или «Снежинка». Такой подход каноничен, фундаментален, вотрефоллен и совсем не отвечает той гибкости, к которой призывает Agile. Для того, чтобы сделать структуру DWH гибкой, существуют современные подходы к проектированию: Data Vault и Anchor modeling – похожие и разные одновременно.

Задавшись вопросом, который вынесен в заголовок доклада, мы пришли к неожиданному ответу: выбирать надо не между подходами, выбирать надо лучшее из двух подходов.

В своем докладе я расскажу:

- DV и AM – в чем разница и где точки соприкосновения;

- наш «гибридный» подход к построению хранилища…

11 часов назад @ youtube.com
Владимир Верстов: DSL для ETL в DMP или чего нам не хватило в Airflow или другом готовом решении?
Владимир Верстов: DSL для ETL в DMP или чего нам не хватило в Airflow или другом готовом решении? Владимир Верстов: DSL для ETL в DMP или чего нам не хватило в Airflow или другом готовом решении?

Data Fest Online 2020

SysML track В open source мире существует множество инструментов для запуска, шедулинга и управления ETL процессами: Airflow, Luigi, Metaflow, ... Все они сосредоточены на описании зависимостей между тасками и графами, но не на том, что непосредственно происходит с данными внутри этих тасков и графов. Мы пошли от обратного и прежде всего выделили основные сущности любого ETL процесса: это таблицы и таски, которые читают данные, их преобразуют и прогружают в таблицы. Мы разработали свой DSL на python для единообразного описания таблиц в YT (in-house аналог Hadoop, Greenplum и ClickHouse) и тасков для MapReduce, Spark, разных SQL-диалектов (3 штуки) и голого python. В до…

11 часов назад @ youtube.com
Data Fest Online 2020 SysML Track Premiere
Data Fest Online 2020 SysML Track Premiere Data Fest Online 2020 SysML Track Premiere

Data Fest Online 2020 SysML Track Посмотреть эфир и список треков и организаторов https://datafest.ru/2020/

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https://t.me/datafest

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11 часов назад @ youtube.com
Степан Малькевич: Умная лента ВКонтакте: от идеи до высоконагруженного продакшена
Степан Малькевич: Умная лента ВКонтакте: от идеи до высоконагруженного продакшена Степан Малькевич: Умная лента ВКонтакте: от идеи до высоконагруженного продакшена

Data Fest Online 2020

SysML track Расскажу про все этапы работы умной ленты ВК - всего 10 этапов. Затронем темы BigData, обучения моделей, работы под высокой нагрузкой, АБТ, поиск таргетов, мониторинги и остальное. Тема лучше всего подходит под SysML, так как я расскажу именно про полный цикл реальной продакшен системы. Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

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11 часов назад @ youtube.com
Антон Пилипенко: Грокаем Hadoop мониторинг
Антон Пилипенко: Грокаем Hadoop мониторинг Антон Пилипенко: Грокаем Hadoop мониторинг

Data Fest Online 2020

SysML track Поговорим о важности мониторинга и профита, который он приносит.

Рассмотрим подходы к мониторингу Hadoop кластера, джоб, сбору метрик и научимся их интерпретировать.

Сравним возможные способы визуализации. 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

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11 часов назад @ youtube.com
Ольга Мегорская: Разметка данных на большом масштабе–ключевая экспертиза современного MLпроизводства
Ольга Мегорская: Разметка данных на большом масштабе–ключевая экспертиза современного MLпроизводства Ольга Мегорская: Разметка данных на большом масштабе–ключевая экспертиза современного MLпроизводства

Data Fest Online 2020

Yandex track https://ods.ai/tracks/yandex-data-labeling-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:

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1 день, 11 часов назад @ youtube.com
Алексей Друца: Эффективные методы контроля качества в краудсорсинге
Алексей Друца: Эффективные методы контроля качества в краудсорсинге Алексей Друца: Эффективные методы контроля качества в краудсорсинге

Data Fest Online 2020

Yandex track https://ods.ai/tracks/yandex-data-labeling-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:

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1 день, 11 часов назад @ youtube.com
Иван Карпеев: 10 главных ошибок заказчика, из за которых теряются время и деньги
Иван Карпеев: 10 главных ошибок заказчика, из за которых теряются время и деньги Иван Карпеев: 10 главных ошибок заказчика, из за которых теряются время и деньги

Data Fest Online 2020

Yandex track https://ods.ai/tracks/yandex-data-labeling-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

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1 день, 11 часов назад @ youtube.com
Data Fest Online 2020: Yandex Track Premiere
Data Fest Online 2020: Yandex Track Premiere Data Fest Online 2020: Yandex Track Premiere

Data Fest Online 2020

Yandex track https://ods.ai/tracks/yandex-data-labeling-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 день, 11 часов назад @ youtube.com
Data Fest A/B Testing Track Premiere
Data Fest A/B Testing Track Premiere Data Fest A/B Testing Track Premiere

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

4 дня, 11 часов назад @ youtube.com
Ildar Safilo: A/B splitters and dealing with small data
Ildar Safilo: A/B splitters and dealing with small data Ildar Safilo: A/B splitters and dealing with small data

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

4 дня, 11 часов назад @ youtube.com
Влад Виноградов: Поиск похожих изображений — справочник от А до Я
Влад Виноградов: Поиск похожих изображений — справочник от А до Я Влад Виноградов: Поиск похожих изображений — справочник от А до Я

Data Fest Online 2020

Computer Vision in Industry track https://ods.ai/tracks/cv-in-industry-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

5 дней, 11 часов назад @ youtube.com
Андрей Шадриков: 1000 и 1 боль при конвертации модели
Андрей Шадриков: 1000 и 1 боль при конвертации модели Андрей Шадриков: 1000 и 1 боль при конвертации модели

Data Fest Online 2020

Computer Vision in Industry track https://ods.ai/tracks/cv-in-industry-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

5 дней, 11 часов назад @ youtube.com
🎧 Podcasts
Lex Fridman AI Podcast Lex Fridman AI Podcast
последний пост 2 дня, 4 часа назад
#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…

2 дня, 4 часа 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…

6 дней, 2 часа 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 неделя, 2 дня 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:/…

2 недели, 1 день 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.

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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

3 недели, 2 дня 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…

3 недели, 4 дня назад @ lexfridman.com
#127 – Joe Rogan: Conversations, Ideas, Love, Freedom & The Joe Rogan Experience
#127 – Joe Rogan: Conversations, Ideas, Love, Freedom & The Joe Rogan Experience #127 – Joe Rogan: Conversations, Ideas, Love, Freedom & The Joe Rogan Experience

Joe Rogan is a comedian, UFC commentator, and the host of the Joe Rogan Experience.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

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OUTLINE:0:00 – Introduction5:17 – Fear of mortality6:48 – Chaos of 2020 and beyond10:32 – Are we going to be okay?

19:30 – Violence, competition, and Sober October26:45 – Mike Tyson27:49 – Managing obsession30:13 – Jiu jitsu game35:55 – Best martial art for self defense39:30 – Guns43:59 – Memorable JRE moments49:31 – Ideas breed in brains of humans56:08 – Advice for Lex1:06:09 – Long-form conversation1:12:28 – …

1 месяц назад @ lexfridman.com
#126 – James Gosling: Java, JVM, Emacs, and the Early Days of Computing
#126 – James Gosling: Java, JVM, Emacs, and the Early Days of Computing #126 – James Gosling: Java, JVM, Emacs, and the Early Days of Computing

James Gosling is the founder and lead designer of the Java programming language.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode.

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OUTLINE:0:00 – Introduction4:45 – Irrational numbers8:04 – Math and programming10:36 – Coding style14:41 – First computer23:54 – Lisp27:22 – Write an Emacs implementation in C35:15 – Early days of the Internet45:57 – Elon Musk, Steve Jobs, Jeff Bezos56:13 – Work hard and smart58:48 – Open source1:10:25 – Java1:28:31 – Java virtual machine1:44:05 – Android1:47:04 – Advice

1 месяц назад @ lexfridman.com
#125 – Ryan Hall: Martial Arts and the Philosophy of Violence, Power, and Grace
#125 – Ryan Hall: Martial Arts and the Philosophy of Violence, Power, and Grace #125 – Ryan Hall: Martial Arts and the Philosophy of Violence, Power, and Grace

Ryan Hall is a jiu jitsu black belt, UFC fighter, and a philosopher of the martial arts.

Please check out our sponsors to get a discount and to support this podcast:– PowerDot, use code LEX: https://powerdot.com/lex– Babbel: https://babbel.com and use code LEX– Cash App: download app & use code “LexPodcast”If you would like to get more information about this podcast go to https://lexfridman.com/podcast or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode.

On …

1 месяц, 1 неделя назад @ lexfridman.com
#124 – Stephen Wolfram: Fundamental Theory of Physics, Life, and the Universe
#124 – Stephen Wolfram: Fundamental Theory of Physics, Life, and the Universe #124 – Stephen Wolfram: Fundamental Theory of Physics, Life, and the Universe

Stephen Wolfram is a computer scientist, mathematician, and theoretical physicist.

This is our second conversation on the podcast.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

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3:52:55 – Sabine Hossenfelder and how beauty leads physics astray4:01:07 – Eric Weinstein and Geometric Unity4:06:17 – Travel faster than speed of light4:16:59 – Why does the universe exist at all

1 месяц, 1 неделя назад @ lexfridman.com
#123 – Manolis Kellis: Origin of Life, Humans, Ideas, Suffering, and Happiness
#123 – Manolis Kellis: Origin of Life, Humans, Ideas, Suffering, and Happiness #123 – Manolis Kellis: Origin of Life, Humans, Ideas, Suffering, and Happiness

Manolis Kellis is a professor at MIT and head of the MIT Computational Biology Group.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode.

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OUTLINE:00:00 – Introduction06:20 – Epigenome10:28 – Evolution15:26 – Neanderthals27:15 – Origin of life on Earth43:44 – Life is a fight against physics49:56 – Life as a set of transformations51:35 – Time scales1:00:31 – Transformations of ideas in human civilization1:05:19 – Life is more than a rat race1:13:18 – Life sucks sometimes and that’s okay1:30:16 – Getting older1:3…

1 месяц, 2 недели назад @ lexfridman.com
#122 – David Fravor: UFOs, Aliens, Fighter Jets, and Aerospace Engineering
#122 – David Fravor: UFOs, Aliens, Fighter Jets, and Aerospace Engineering #122 – David Fravor: UFOs, Aliens, Fighter Jets, and Aerospace Engineering

David Fravor is a navy pilot of 18 years and a primary witness in one of the most credible UFO sightings in history, video of which has been released by the Pentagon and reported on by the NY Times.

OUTLINE:00:00 – Introduction07:13 – Top Gun12:06 – Navy pilot career24:14 – AI is the third brain of a jet fighter40:37 – Sully47:34 – Landing a jet fighter on a carrier53:18 – What’s it like to fly a jet fighter?

1:05:22 – Greatest plane ever made1:11:04 – The Tic Tac UFO story1:49:16 – Intelligent extraterrestrial life1:53:30 – Why aren’t UFOs investigated more seriously1:59:52 – Tic Tac UFO details2:07:55 – What do you think the Tic Tac was?

2:16:23 – SpaceX2:30:01 – Response to Mick West Deb…

1 месяц, 2 недели назад @ lexfridman.com
Lex Solo #3 – In Memory of My Grandmother
Lex Solo #3 – In Memory of My Grandmother Lex Solo #3 – In Memory of My Grandmother

My attempt to find the words to honor my grandmother, an amazing woman who is responsible for much of who I am, who taught me how to be a man, taught me about strength, about wisdom, about compassion, about love.

If you would like to get more information about this podcast go to https://lexfridman.com/podcast or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

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OUTLINE:00:00 – In memory of my grandmother02…

1 месяц, 2 недели назад @ lexfridman.com
#121 – Eugenia Kuyda: Friendship with an AI Companion
#121 – Eugenia Kuyda: Friendship with an AI Companion #121 – Eugenia Kuyda: Friendship with an AI Companion

Eugenia Kuyda co-founder of Replika, an AI companion.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

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2:27:45 – Does an AI companion need a body?

2:30:20 – Her2:37:24 – GPT-3 for conversation2:43:48 – We should be nice to AI2:46:52 – Book recommendations2:53:45 – Russian language2:58:41 – Meaning of life

1 месяц, 3 недели назад @ lexfridman.com
Lex Solo #2 – The Future of Neuralink
Lex Solo #2 – The Future of Neuralink Lex Solo #2 – The Future of Neuralink

My thoughts on 8 possible long-term futures of Neuralink after attending the August 2020 progress update.

This is a solo episode #2 of the podcast.

If you would like to get more information about this podcast go to https://lexfridman.com/podcast or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

On some podcast players you should be able to click the timestamp to jump to that time.

1 месяц, 3 недели назад @ lexfridman.com
DeepMind: The Podcast DeepMind: The Podcast
последний пост None
Microsoft Research Podcast Microsoft Research Podcast
последний пост 3 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.

3 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

4 месяца, 1 неделя 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

4 месяца, 3 недели 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.

5 месяцев 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…

5 месяцев, 1 неделя 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

5 месяцев, 2 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…

5 месяцев, 3 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…

6 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…

6 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.

6 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…

6 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

6 месяцев, 4 недели 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

7 месяцев 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

7 месяцев, 1 неделя 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…

7 месяцев, 2 недели назад @ blubrry.com
NLP Highlights NLP Highlights
последний пост 3 недели, 4 дня назад
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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3 недели, 4 дня 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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1 месяц, 3 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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2 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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2 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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3 месяца, 3 недели назад @ soundcloud.com
115 - AllenNLP, interviewing Matt Gardner
115 - AllenNLP, interviewing Matt Gardner 115 - AllenNLP, interviewing Matt Gardner

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4 месяца, 1 неделя 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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5 месяцев 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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5 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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5 месяцев, 2 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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6 месяцев 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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6 месяцев, 3 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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7 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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7 месяцев, 1 неделя назад @ soundcloud.com
107 - Multi-Modal Transformers, with Hao Tan and Mohit Bansal
107 - Multi-Modal Transformers, with Hao Tan and Mohit Bansal 107 - Multi-Modal Transformers, with Hao Tan and Mohit Bansal

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8 месяцев назад @ soundcloud.com
106 - Ethical Considerations In NLP Research, with Emily Bender
106 - Ethical Considerations In NLP Research, with Emily Bender 106 - Ethical Considerations In NLP Research, with Emily Bender

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8 месяцев, 1 неделя назад @ soundcloud.com
Data Skeptic
последний пост 4 дня, 14 часов назад
ACID Compliance
ACID Compliance ACID Compliance

ACID ComplianceLinhda joins us to discuss the topic of ACID Compliance.

4 дня, 14 часов 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 неделя, 4 дня 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.

2 недели, 1 день 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.

3 недели, 1 день 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.

1 месяц 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.

1 месяц, 1 неделя 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)

1 месяц, 2 недели назад @ dataskeptic.com
Voting Mechanisms
Voting Mechanisms Voting Mechanisms

Voting MechanismsSteven Heilman joins us to discuss his paper Designing Stable Elections.

1 месяц, 3 недели 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.

2 месяца 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.

2 месяца, 1 неделя назад @ dataskeptic.com
Listener Survey Review
Listener Survey Review Listener Survey Review

Listener Survey ReviewIn this episode, Kyle and Linhda review the results of our recent survey.

Hear all about the demographic details and how we interpret these results.

2 месяца, 2 недели назад @ dataskeptic.com
Human Computer Interaction and Online Privacy
Human Computer Interaction and Online Privacy Human Computer Interaction and Online Privacy

Human Computer Interaction and Online PrivacyMoses Namara from the HATLab joins us to discuss his research into the interaction between privacy and human computer interaction.

3 месяца назад @ dataskeptic.com
Authorship Attribution of Lennon McCartney Songs
Authorship Attribution of Lennon McCartney Songs Authorship Attribution of Lennon McCartney Songs

Mark Glickman joins us to discuss the paper Data in the Life: Authorship Attribution in Lennon-McCartney Songs.

3 месяца, 1 неделя назад @ dataskeptic.com
GANs Can Be Interpretable
GANs Can Be Interpretable GANs Can Be Interpretable

Erik Härkönen joins us to discuss the paper GANSpace: Discovering Interpretable GAN Controls. During the interview, Kyle makes reference to this amazing interpretable GAN controls video and it’s accompanying codebase found here. Erik mentions the GANspace collab notebook which is a rapid way to try these ideas out for yourself.

3 месяца, 2 недели назад @ dataskeptic.com
Sentiment Preserving Fake Reviews
Sentiment Preserving Fake Reviews Sentiment Preserving Fake Reviews

David Ifeoluwa Adelani joins us to discuss Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection.

3 месяца, 3 недели назад @ dataskeptic.com
Linear Digressions Linear Digressions
последний пост 3 месяца 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!

3 месяца 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…

3 месяца, 1 неделя 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

3 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…

3 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:

4 месяца 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.

4 месяца, 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 …

4 месяца, 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…

4 месяца, 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.

4 месяца, 3 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:

4 месяца, 4 недели 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:

5 месяцев 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. …

5 месяцев, 1 неделя 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.

5 месяцев, 2 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:

5 месяцев, 3 недели 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…

6 месяцев назад @ lineardigressions.com
SuperDataScience SuperDataScience
последний пост 4 дня, 15 часов назад
SDS 412: Stand More - Sit Less
SDS 412: Stand More - Sit Less SDS 412: Stand More - Sit Less

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4 дня, 15 часов 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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6 дней, 4 часа назад @ soundcloud.com
SDS 410: Communicate Your Needs
SDS 410: Communicate Your Needs SDS 410: Communicate Your Needs

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1 неделя, 4 дня 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 неделя, 6 дней назад @ soundcloud.com
SDS 408: Meaning is Everything
SDS 408: Meaning is Everything SDS 408: Meaning is Everything

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2 недели, 4 дня назад @ soundcloud.com
SDS 407: How to Encourage Diversity in Data Science
SDS 407: How to Encourage Diversity in Data Science SDS 407: How to Encourage Diversity in Data Science

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2 недели, 6 дней назад @ soundcloud.com
SDS 406: Abandon Hope
SDS 406: Abandon Hope SDS 406: Abandon Hope

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3 недели, 4 дня назад @ soundcloud.com
SDS 405: The Work of Quants and Data Scientists in the Financial Space
SDS 405: The Work of Quants and Data Scientists in the Financial Space SDS 405: The Work of Quants and Data Scientists in the Financial Space

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3 недели, 6 дней назад @ soundcloud.com
SDS 404: The Narrative Arc in Storytelling
SDS 404: The Narrative Arc in Storytelling SDS 404: The Narrative Arc in Storytelling

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1 месяц назад @ soundcloud.com
SDS 403: Gamifying Your Data Science Work and Education
SDS 403: Gamifying Your Data Science Work and Education SDS 403: Gamifying Your Data Science Work and Education

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1 месяц назад @ soundcloud.com
SDS 402: Face Your Demons
SDS 402: Face Your Demons SDS 402: Face Your Demons

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1 месяц, 1 неделя назад @ soundcloud.com
SDS 401: From Data Science Student to Professional
SDS 401: From Data Science Student to Professional SDS 401: From Data Science Student to Professional

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1 месяц, 1 неделя назад @ soundcloud.com
SDS 400: Think Bigger
SDS 400: Think Bigger SDS 400: Think Bigger

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1 месяц, 2 недели назад @ soundcloud.com
SDS 399: Contributing to the Community of Data Scientists
SDS 399: Contributing to the Community of Data Scientists SDS 399: Contributing to the Community of Data Scientists

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1 месяц, 2 недели назад @ soundcloud.com
SDS 398: Emotional Burnout
SDS 398: Emotional Burnout SDS 398: Emotional Burnout

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1 месяц, 3 недели назад @ soundcloud.com
Data Science at Home Data Science at Home
последний пост 4 дня, 18 часов назад
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

4 дня, 18 часов 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 неделя, 2 дня 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.

2 недели, 1 день 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

1 месяц 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!

1 месяц, 1 неделя 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.

1 месяц, 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.

1 месяц, 4 недели 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

2 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.

2 месяца, 3 недели назад @ datascienceathome.com
GPT-3 cannot code (and never will) (Ep. 114)
GPT-3 cannot code (and never will) (Ep. 114) GPT-3 cannot code (and never will) (Ep. 114)

July 26, 2020 podcastThe hype around GPT-3 is alarming and gives and provides us with the awful picture of people misunderstanding artificial intelligence.

In response to some comments that claim GPT-3 will take developers’ jobs, in this episode I express some personal opinions about the state of AI in generating source code (and in particular GPT-3).

If you have comments about this episode or just want to chat, come join us on the official Discord channel.

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 learni…

3 месяца назад @ datascienceathome.com
Make Stochastic Gradient Descent Fast Again (Ep. 113)
Make Stochastic Gradient Descent Fast Again (Ep. 113) Make Stochastic Gradient Descent Fast Again (Ep. 113)

July 22, 2020 podcastThere is definitely room for improvement in the family of algorithms of stochastic gradient descent.

In this episode I explain a relatively simple method that has shown to improve on the Adam optimizer.

But, watch out!

This approach does not generalize well.

Join our Discord channel and chat with us.

3 месяца, 1 неделя назад @ datascienceathome.com
What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112)
What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112) What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112)

July 20, 2020 podcastIn this episode I speak about data transformation frameworks available for the data scientist who writes Python code.

The usual suspect is clearly Pandas, as the most widely used library and de-facto standard.

However when data volumes increase and distributed algorithms are in place (according to a map-reduce paradigm of computation), Pandas no longer performs as expected.

In this episode I explain the frameworks that are the best equivalent to Pandas in bigdata contexts.

Amethix is a consulting firm focused on data science, machine learning, and artificial intelligence.

3 месяца, 1 неделя назад @ datascienceathome.com
[RB] It’s cold outside. Let’s speak about AI winter (Ep. 111)
[RB] It’s cold outside. Let’s speak about AI winter (Ep. 111) [RB] It’s cold outside. Let’s speak about AI winter (Ep. 111)

July 5, 2020 podcastIn this episode I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019.

As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe.

A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible.

Let’s stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future.

This episode is brought to you by ProtonmailClick on the link in the description or go to protonmail.com/datascience and get 20% off their annual subscription.

3 месяца, 3 недели назад @ datascienceathome.com
Rust and machine learning #4: practical tools (Ep. 110)
Rust and machine learning #4: practical tools (Ep. 110) Rust and machine learning #4: practical tools (Ep. 110)

June 29, 2020 podcastIn this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust.

Not all of them are mature enough for production environments.

I believe that community effort can change this very quickly.

To make a comparison with the Python ecosystem I will cover frameworks for linear algebra (numpy), dataframes (pandas), off-the-shelf machine learning (scikit-learn), deep learning (tensorflow) and reinforcement learning (openAI).

Rust is the language of the future.

4 месяца назад @ datascienceathome.com
Unsupervised Unsupervised
последний пост 2 недели, 6 дней назад
Where Kaggle meets the real world
Where Kaggle meets the real world Where Kaggle meets the real world

Unsupervised is a podcast about Data Science in Israel.

At each episode we interview an industry professional or a researcher from academia and discuss different aspects and problems in data science.

We want to give a peek to what’s going on with data science across the Israeli industry and also to talk about different algorithms, tools, papers, methods and pretty much everything that’s interesting and related to Data Science and Machine Learning.

The podcast is aimed to data science professionals and researchers, as well as for those who work and collaborate with data science teams and beginners in the field.

We want to thank Samsung Next for hosting us.

2 недели, 6 дней назад @ unsupervised-podcast.xyz
Creating your labeled training set, with Jonathan Laserson
Creating your labeled training set, with Jonathan Laserson Creating your labeled training set, with Jonathan Laserson

Unsupervised is a podcast about Data Science in Israel.

At each episode we interview an industry professional or a researcher from academia and discuss different aspects and problems in data science.

We want to give a peek to what’s going on with data science across the Israeli industry and also to talk about different algorithms, tools, papers, methods and pretty much everything that’s interesting and related to Data Science and Machine Learning.

The podcast is aimed to data science professionals and researchers, as well as for those who work and collaborate with data science teams and beginners in the field.

We want to thank Samsung Next for hosting us.

9 месяцев, 1 неделя назад @ unsupervised-podcast.xyz