Very ML
State-of-the-art Machine Learning News Feed
/r/MachineLearning /r/MachineLearning
последний пост 2 часа назад
[D] What's the best detector to build a basketball detection model on a mobile phone.
[D] What's the best detector to build a basketball detection model on a mobile phone. [D] What's the best detector to build a basketball detection model on a mobile phone.

I'm building a basketball detection model and wanted that to be deployed on IOS.

What is the best object detection model I can use for my problem?

I heard MobileNet SSD is good but I guess this doesn't have good accuracy on small objects.

Training data sample: https://drive.google.com/file/d/114fS0rbh487nemT7U0-JzrNJvaUOUXNy/view

2 часа назад @ reddit.com
[R] Is Google's Tacotron 2 available to the public?
[R] Is Google's Tacotron 2 available to the public? [R] Is Google's Tacotron 2 available to the public?

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

2 часа назад @ reddit.com
[D] Anyone else running the stack Ubuntu 20.04, cuda 11, cudnn 9, and TF 2.3 and an RTX card? I've had so many issues.
[D] Anyone else running the stack Ubuntu 20.04, cuda 11, cudnn 9, and TF 2.3 and an RTX card? I've had so many issues. [D] Anyone else running the stack Ubuntu 20.04, cuda 11, cudnn 9, and TF 2.3 and an RTX card? I've had so many issues.

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

5 часов назад @ reddit.com
What is Yan LeCun's view on Hinton's comments on CNN?[Discussion]
What is Yan LeCun's view on Hinton's comments on CNN?[Discussion] What is Yan LeCun's view on Hinton's comments on CNN?[Discussion]

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

6 часов назад @ reddit.com
[R] Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
[R] Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves [R] Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

9 часов назад @ reddit.com
[P] A Tool to use Docker Effortlessly for ML Research
[P] A Tool to use Docker Effortlessly for ML Research [P] A Tool to use Docker Effortlessly for ML Research

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

14 часов назад @ reddit.com
[D] Combining Text and Numeric Features in Deep Learning
[D] Combining Text and Numeric Features in Deep Learning [D] Combining Text and Numeric Features in Deep Learning

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

14 часов назад @ reddit.com
[P] PyTorch implementation of EventProp: Backpropagation for Exact Gradients in Spiking Neural Networks
[P] PyTorch implementation of EventProp: Backpropagation for Exact Gradients in Spiking Neural Networks [P] PyTorch implementation of EventProp: Backpropagation for Exact Gradients in Spiking Neural Networks

EventProp has recently showed up on arXiv as a method to compute exact gradients for Spiking Neural Networks, enabling easier training with gradient methods.

​Github: https://github.com/lolemacs/pytorch-eventpropPaper: https://arxiv.org/abs/2009.08378Abstract:We derive the backpropagation algorithm for spiking neural networks composed of leaky integrate-and-fire neurons operating in continuous time.

As errors are backpropagated in an event-based manner (at spike times), EventProp requires storing state variables only at these times, providing favorable memory requirements.

EventProp can be applied to spiking networks with arbitrary connectivity, including recurrent, convolutional and deep f…

15 часов назад @ reddit.com
[P] How do you track the time each step takes when you have a delay constraint ?
[P] How do you track the time each step takes when you have a delay constraint ? [P] How do you track the time each step takes when you have a delay constraint ?

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

15 часов назад @ reddit.com
[P] PyCM 2.9 released : Multi-class confusion matrix library in Python
[P] PyCM 2.9 released : Multi-class confusion matrix library in Python [P] PyCM 2.9 released : Multi-class confusion matrix library in Python

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

16 часов назад @ reddit.com
[D] Israeli MIT Professor Regina Barzilay Wins $1M Prize For AI Work In Cancer Diagnostics, Drug Development
[D] Israeli MIT Professor Regina Barzilay Wins $1M Prize For AI Work In Cancer Diagnostics, Drug Development [D] Israeli MIT Professor Regina Barzilay Wins $1M Prize For AI Work In Cancer Diagnostics, Drug Development

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

16 часов назад @ reddit.com
[D] Publishing in JMLR
[D] Publishing in JMLR [D] Publishing in JMLR

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

17 часов назад @ reddit.com
[P] Perceptron Algorithm Trough Origin
[P] Perceptron Algorithm Trough Origin [P] Perceptron Algorithm Trough Origin

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

17 часов назад @ reddit.com
[D] What is the state of the art in deep learning for image feature representation?
[D] What is the state of the art in deep learning for image feature representation? [D] What is the state of the art in deep learning for image feature representation?

Cookies help us deliver our Services.

By using our Services, you agree to our use of cookies.Learn More

17 часов назад @ reddit.com
[R] Is it possible to train a Flux.jl (Julia) model in the cloud?
[R] Is it possible to train a Flux.jl (Julia) model in the cloud? [R] Is it possible to train a Flux.jl (Julia) model in the cloud?

I'm doing research on neural networks and I was wondering about some things, many cloud services offer GPU/CPU clusters (and Google TPU) external hardware.

I see they have a lot of guides for Python and R but nothing for Julia, would it be possible to train a model in the cloud with their external hardware?

I know of course that I could simply create a VM but I would want (if possible) to use their GPU/CPU clusters or TPU resources.

17 часов назад @ reddit.com
Towards Data Science Towards Data Science
последний пост 8 часов назад
Bias Correction For Paid Search In Media Mix Modeling: Paper Review
Bias Correction For Paid Search In Media Mix Modeling: Paper Review Bias Correction For Paid Search In Media Mix Modeling: Paper Review

Bias Correction For Paid Search In Media Mix Modeling: Linked PaperMedia Mix Modeling attempts to estimate the causal effect of media spend on sales, solely based on observational data.

This paper explores the use of Pearl’s graphical framework to control for selection bias in media mix modeling, specifically in paid search ads.

Above, we can see how economic factors drive consumers demand, which in turn drives search queries, which in turn drive paid search and organic search.

To control for this, the paper suggests using Pearl’s backdoor criterion.

To familiarize yourself with the concept of backdoor criterion, I recommend playing with the following code snippet (try creating various grap…

8 часов назад @ towardsdatascience.com
Can I Grade Loans Better Than LendingClub?
Can I Grade Loans Better Than LendingClub? Can I Grade Loans Better Than LendingClub?

Ground rulesThis is going to be a clean fight — my model won’t use any data LendingClub wouldn’t have access to at the point they calculate a loan’s grade (including the grade itself).

On the other hand, LendingClub may have a slight informational advantage on the later end of the test set, since they would have known the outcomes of some loans on the earlier end of the test set by that time.

I’ll gather all their grade A loans from the test set, count them, and calculate their average fraction_recovered .

LendingClub’s turnLendingClub gave 38,779 loans in the test set an A grade.

My turnFirst, I’ll copy over my run_pipeline function from my previous notebook:Now for the moment of truth:Epo…

9 часов назад @ towardsdatascience.com
My Favorite Python Servers To Deploy Into Production
My Favorite Python Servers To Deploy Into Production My Favorite Python Servers To Deploy Into Production

Gunicorn3Gunicorn3 is the “ classic” and industry-standard Python production server.

Another significant advantage to using Gunicorn is that it is the only WSGI web-server for Python that is compatible with almost everything.

One great thing that GEvent prominently features is SSL support and cooperative sockets.

SSL support is always a great thing to have, as well!

Like Gunicorn, uWSGI has a high-priority focus on CPU-usage.

9 часов назад @ towardsdatascience.com
Machine Learning Algorithms for Football Predictions
Machine Learning Algorithms for Football Predictions Machine Learning Algorithms for Football Predictions

And it’s clear that at least 17 collected variables, can influence the match outcome therefore also on the performance of the prediction.

Since these treatments were done, it was found a way to inform the machine of the “place” of the match.

For every victory, the team summed 3 points, for a draw 1 point and for a loss 0 points.

To solve this we subtracted the home team variables results by the visitant variables results for every match.

Showing in this way if the team home is in any way superior or inferior to the visitant team.

9 часов назад @ towardsdatascience.com
Writing advanced SQL queries in pandas
Writing advanced SQL queries in pandas Writing advanced SQL queries in pandas

For each trip, let’s pull the departure date of the next trip: lead1, the second next trip: lead2, the previous trip: lag1 and the third previous trip: lag3.

Essentially, if we try to assign a scalar value to a new column in pandas, the value is broadcasted across all rows.

If we desire the output to be sorted, the code changes to:df.sort_values(['name', 'dep_date'], inplace=True)df.assign(max_dur=df['duration'].max(),sum_dur=df['duration'].sum(),avg_dur_name=df.groupby('name')['duration'].transform('mean'),cum_sum_dur_name=df.groupby('name')['duration'].transform('cumsum'))This will be identical to the SQL output.

I encourage you to try translating this SQL query to pandas on your own befo…

12 часов назад @ towardsdatascience.com
An Introduction To The Julia And PKG REPLs
An Introduction To The Julia And PKG REPLs An Introduction To The Julia And PKG REPLs

Obtaining A PROPER Julia InstallationWhenever I was first introduced to Julia, I made the fatal mistake of installing the language from my package manager.

While this will give you a stable and perfectly usable version of Julia, version 1.1, with long-term support, you will not get to reap the benefits of all the advancements that have been made since then.

After that, you can follow the installation instructions listed below for your respective platform:Linux (Or other non - Mac Unix-Like)Firstly, download the tarball from the Julia mirror.

Next, you’ll need to add Julia to your PATH just as we did on Linux.

MacOSTo install Julia for Mac, take the regular MacOS installation procedure with …

13 часов назад @ towardsdatascience.com
Quick Tutorial: Using Bayesian optimization to tune your hyperparameters in PyTorch
Quick Tutorial: Using Bayesian optimization to tune your hyperparameters in PyTorch Quick Tutorial: Using Bayesian optimization to tune your hyperparameters in PyTorch

Quick Tutorial: Using Bayesian optimization to tune your hyperparameters in PyTorchA faster way to design your neural networksHyperparameter tuning is like tuning a guitar, in that I can’t do it myself and would much rather use an app.

The search for optimal hyperparameters requires some expertise and patience, and you’ll often find people using exhausting methods like grid search and random search to find the hyperparameters that work best for their problem.

A quick tutorialI’m going to show you how to implement Bayesian optimization to automatically find the optimal hyperparameter set for your neural network in PyTorch using Ax.

I won’t be going into the details of Bayesian optimization, …

14 часов назад @ towardsdatascience.com
Are you a Data Scientist aspirant? Here is my story of becoming one
Are you a Data Scientist aspirant? Here is my story of becoming one Are you a Data Scientist aspirant? Here is my story of becoming one

I desperately wanted to become a Data Scientist and finally became one in December 2016 after 18 months of self-learning.

In this post I talk about what I was doing before becoming a Data Scientist, how did I become one and what is it like being one.

As a Data Scientist:My first 6 months of being a Data Scientist were the most challenging period in my career.

I’m now part of a 30 member global(another lab in London) team with Graduate Data Scientists, Data Engineers and Senior Data Scientists.

Summary:I wouldn’t say I was born to be a Data Scientist, it all started with being dissatisfied with what I was doing.

15 часов назад @ towardsdatascience.com
Dressing Up Your Data Visualization
Dressing Up Your Data Visualization Dressing Up Your Data Visualization

Learning Wolfram: Dressing Up Your Data VisualizationHow to Turn a Basic Plot Into an Awesome Plot(image by the author using photos by Yasong Zhou and NOAA on Unsplash)Data visualizations can be a tricky thing.

The process of dressing up a visualization to maximize its appeal is usually one of trial and error.

This story explores how to make an appealing visualization using a few tips and tricks to make things easy.

I am using the Wolfram Data Repository to select one.

For more details on all the Wolfram Language data visualization functions, check out this guide page in the reference documentation.

15 часов назад @ towardsdatascience.com
The Last Machine & Deep-Learning Compendium You’ll Ever Need.
The Last Machine & Deep-Learning Compendium You’ll Ever Need. The Last Machine & Deep-Learning Compendium You’ll Ever Need.

The Last Machine & Deep-Learning Compendium You’ll Ever NeedA comprehensive resource on practically every topic for data science researchersIn the last 3 years, I have been curating everything related, directly or indirectly, to machine-learning (ML), deep-learning (DL), Statistics, Probability, NLP, NLU, deep-vision, etc.

I started curating a compendium because I wanted to expand the scope of my knowledge.

Personally, I read a few articles before I start my day, and once in a while I write and share the knowledge that I gain on Medium.

The topics include probably the majority of modern machine learning algorithms, feature selection and engineering techniques, deep-learning, NLP, audio, vis…

16 часов назад @ towardsdatascience.com
Continuous Delivery for Machine Learning
Continuous Delivery for Machine Learning Continuous Delivery for Machine Learning

Continuous Delivery for Machine LearningDeploying Machine Learning Systems to Production safely and quickly in a sustainable wayTable of contentsContinuous Delivery for Machine LearningMost of the principles and practices of traditional software development can be applied to Machine Learning(ML), but certain unique ML specific challenges need to be handled differently.

This article will look at how Continuous Delivery that has helped traditional software solve its deployment challenges be applied to Machine Learning.

Continuous DeploymentWe won’t be talking much about Continuous Deployment in this article, but it is good to understand the difference between Continuous Delivery and Continuou…

17 часов назад @ towardsdatascience.com
9 things you did not know about jupyter notebook
9 things you did not know about jupyter notebook 9 things you did not know about jupyter notebook

Jupyter notebook has magic commands.

In order to confirm this, you can run the following code in jupyter notebook.

One of the most useful magic commands is a command that lists all magic commands.

%lsmagicExplaining all magic commands would require the whole new article so let’s limit ourselves to just a few examples.

You can run python file from your jupyter notebook using:%run Or you can time the execution of the cell with the following code.

17 часов назад @ towardsdatascience.com
How to Systematically Fool an Image Recognition Neural Network
How to Systematically Fool an Image Recognition Neural Network How to Systematically Fool an Image Recognition Neural Network

How to Systematically Fool an Image Recognition Neural Networkand why it matters… a lotConvolutional neural networks — CNNs — form the basis for image recognition, which is undoubtedly one of the most important applications of deep learning.

Adversarial examples or inputs (think adversary: enemy) are indistinguishable from regular images to the human eye, but can completely fool a variety of image recognition architectures.

We want to make each individual value of the adversarial vector η as small as possible such that the overall image appears unaltered to a human eye.

However, each change is built — following the sign function — such that the change in weighted sum is maximized.

Created b…

17 часов назад @ towardsdatascience.com
A Concise Guide of 10+ Awesome Python Editors and How To Choose Which Editor Suits You The Best…
A Concise Guide of 10+ Awesome Python Editors and How To Choose Which Editor Suits You The Best… A Concise Guide of 10+ Awesome Python Editors and How To Choose Which Editor Suits You The Best…

Thonny EditorScreenshot By AuthorThe Thonny Integrated Development Environment (IDE), which comes pre-installed on the Linux and Linux based Platforms.

It is a great development environment and easy for beginners to get started with.

AtomScreenshot By AuthorThis IDE is similar to the Sublime text editor with additional requirements for Python.

Some of the commonly used packages in Atom for Python development are autocomplete-python, linter-flake8, python-debugger, etc.

Most experts absolutely love vim due to the high computation ability and lightweight compact development working environment.

17 часов назад @ towardsdatascience.com
Quantum Machine Learning
Quantum Machine Learning Quantum Machine Learning

That said, Quantum Machine Learning is the use of quantum computing for computation of machine learning algorithms.

”Machine learning is a thing-labeler, essentially”— Cassie Kozyrkov, Chief Decision Scientist at Google, source —With machine learning, we aim to put a label onto a yet unlabeled thing.

The Case For Quantum Machine LearningQuantum machine learning is the use of quantum computing for the computation of machine learning algorithms.

Thus, tapping the full potential of quantum computing to solve the machine learning optimization problem requires the evaluation and the representation to integrate with the quantum optimizer.

This post is part of the book: Hands-On Quantum Machine Le…

17 часов назад @ towardsdatascience.com
Distill.pub Distill.pub
последний пост 1 неделя, 5 дней 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 неделя, 5 дней назад @ distill.pub
Thread: Differentiable Self-organizing Systems
Thread: Differentiable Self-organizing Systems

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

3 недели, 6 дней назад @ distill.pub
Self-classifying MNIST Digits
Self-classifying MNIST Digits

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

3 недели, 6 дней назад @ distill.pub
Curve Detectors
Curve Detectors

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

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

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

4 месяца, 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.'

5 месяцев, 3 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.

6 месяцев, 1 неделя 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.

6 месяцев, 2 недели назад @ distill.pub
Thread: Circuits
Thread: Circuits

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

6 месяцев, 2 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.

7 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.

8 месяцев, 2 недели назад @ distill.pub
The Gradient The Gradient
последний пост 1 неделя, 4 дня назад
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 неделя, 4 дня 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.

2 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.

2 месяца 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…

2 месяца, 2 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…

3 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 …

5 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.

5 месяцев, 4 недели 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.

7 месяцев, 2 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…

8 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…

8 месяцев, 1 неделя назад @ thegradient.pub
Is NeurIPS Getting Too Big?
Is NeurIPS Getting Too Big? Is NeurIPS Getting Too Big?

NeurIPS 2019, the latest incarnation of the Neural Information Processing Systems conference, wrapped up just over a week ago.

No, that's a keynote at #NeurIPS2019 pic.twitter.com/nJjONGzJww — Jevgenij Gamper (@brutforcimag) December 11, 2019 NeurIPS poster session- Too crowded.

:(NeurIPS 2019, Vancouver, Canada: Got the visa 3 weeks before.

CitationFor attribution in academic contexts or books, please cite this work asAndrey Kurenkov, "Is NeurIPS Getting Too Big?

BibTeX citation:@article{kurenkov2019neuripst,author = {Kurenkov, Andrey},title = {Is NeurIPS Getting Too Big?

9 месяцев назад @ thegradient.pub
An Epidemic of AI Misinformation
An Epidemic of AI Misinformation An Epidemic of AI Misinformation

Unfortunately, the problem of overhyped AI extends beyond the media itself.

General AI still seems like it might be a couple decades away, sixty years after the first optimistic projections were issued.

Hundreds of deep learning for radiology companies have been spawned in the meantime, but thus far no actual radiologists have been replaced, and the best guess is that deep learning can augment radiologists, but not, in the near-term replace them.

If AI system is allegedly better than humans, then which humans, and how much better?

CitationFor attribution in academic contexts or books, please cite this work asGary Marcus, "An Epidemic of AI Misinformation", The Gradient, 2019.

9 месяцев, 4 недели назад @ thegradient.pub
DataTau DataTau
последний пост 33 минуты назад
Good Content Vs Bad Content: What Does Your Website Have?
Good Content Vs Bad Content: What Does Your Website Have? Good Content Vs Bad Content: What Does Your Website Have?

You must be logged to comment.

33 минуты назад @ datatau.net
Facebook Marketing In Brooklyn, New York: Painless Guide
Facebook Marketing In Brooklyn, New York: Painless Guide Facebook Marketing In Brooklyn, New York: Painless Guide

You must be logged to comment.

33 минуты назад @ datatau.net
21 Important Facebook Advertising Questions Answered
21 Important Facebook Advertising Questions Answered 21 Important Facebook Advertising Questions Answered

You must be logged to comment.

33 минуты назад @ datatau.net
#5 Eccentric Social Media Challenges Of All Times
#5 Eccentric Social Media Challenges Of All Times #5 Eccentric Social Media Challenges Of All Times

You must be logged to comment.

33 минуты назад @ datatau.net
Easiest Customer Loyalty Guide: The Way To Exceptional Brand
Easiest Customer Loyalty Guide: The Way To Exceptional Brand Easiest Customer Loyalty Guide: The Way To Exceptional Brand

You must be logged to comment.

33 минуты назад @ datatau.net
Big Data Stream Mining with Online Learning – Support Vector Machines
Big Data Stream Mining with Online Learning – Support Vector Machines Big Data Stream Mining with Online Learning – Support Vector Machines

You must be logged to comment.

9 часов назад @ datatau.net
Big Data Stream Mining with Online Learning – Support Vector Machines
Big Data Stream Mining with Online Learning – Support Vector Machines Big Data Stream Mining with Online Learning – Support Vector Machines

You must be logged to comment.

9 часов назад @ datatau.net
CHANGE YOUR WINDOWS COMPUTER PASSWORD WITHOUT KNOWING OLD PASSWORD
CHANGE YOUR WINDOWS COMPUTER PASSWORD WITHOUT KNOWING OLD PASSWORD CHANGE YOUR WINDOWS COMPUTER PASSWORD WITHOUT KNOWING OLD PASSWORD

You must be logged to comment.

1 день назад @ datatau.net
25 TIPS MAKES YOUR LIFE AWESOME
25 TIPS MAKES YOUR LIFE AWESOME 25 TIPS MAKES YOUR LIFE AWESOME

You must be logged to comment.

1 день назад @ datatau.net
Automating Every Aspect of Your Python Project
Automating Every Aspect of Your Python Project Automating Every Aspect of Your Python Project

You must be logged to comment.

1 день, 2 часа назад @ datatau.net
The Art Of Increasing Sales For E-Commerce Businesses
The Art Of Increasing Sales For E-Commerce Businesses The Art Of Increasing Sales For E-Commerce Businesses

You must be logged to comment.

1 день, 7 часов назад @ datatau.net
How To Use Google Trends To Find Good Keywords? (10 Simple Ways)
How To Use Google Trends To Find Good Keywords? (10 Simple Ways) How To Use Google Trends To Find Good Keywords? (10 Simple Ways)

You must be logged to comment.

1 день, 7 часов назад @ datatau.net
Hiring A Digital Marketing Agency
Hiring A Digital Marketing Agency Hiring A Digital Marketing Agency

You must be logged to comment.

1 день, 7 часов назад @ datatau.net
Healthcare Industry Market Research Company In Brooklyn, New York
Healthcare Industry Market Research Company In Brooklyn, New York Healthcare Industry Market Research Company In Brooklyn, New York

You must be logged to comment.

1 день, 7 часов назад @ datatau.net
Easiest Tutorial For Setting Up Google Ads?
Easiest Tutorial For Setting Up Google Ads? Easiest Tutorial For Setting Up Google Ads?

You must be logged to comment.

1 день, 7 часов назад @ datatau.net
Synced Review
последний пост 14 часов назад
New Google NLP Model Approaches BERT-Level Performance Using Orders of Magnitude Fewer Parameters
New Google NLP Model Approaches BERT-Level Performance Using Orders of Magnitude Fewer Parameters New Google NLP Model Approaches BERT-Level Performance Using Orders of Magnitude Fewer Parameters

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.

14 часов назад @ medium.com
Facebook Reality Labs’ Vision of the Future: ‘Tools that Help People Feel Connected’
Facebook Reality Labs’ Vision of the Future: ‘Tools that Help People Feel Connected’ Facebook Reality Labs’ Vision of the Future: ‘Tools that Help People Feel Connected’

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.

15 часов назад @ medium.com
Tracking Recent Topics & Trends in 3D Photo Generation
Tracking Recent Topics & Trends in 3D Photo Generation Tracking Recent Topics & Trends in 3D Photo Generation

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.

1 день, 12 часов назад @ medium.com
Microsoft Gets Exclusive License for OpenAI’s GPT-3 Language Model
Microsoft Gets Exclusive License for OpenAI’s GPT-3 Language Model Microsoft Gets Exclusive License for OpenAI’s GPT-3 Language Model

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.

1 день, 13 часов назад @ medium.com
Adobe’s DL-Based ‘HDMatt’ Handles Image Details Thinner Than Hair
Adobe’s DL-Based ‘HDMatt’ Handles Image Details Thinner Than Hair Adobe’s DL-Based ‘HDMatt’ Handles Image Details Thinner Than Hair

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.

1 день, 20 часов назад @ medium.com
UC Berkeley Reward-Free RL Beats SOTA Reward-Based RL
UC Berkeley Reward-Free RL Beats SOTA Reward-Based RL UC Berkeley Reward-Free RL Beats SOTA Reward-Based RL

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.

2 дня, 17 часов назад @ medium.com
New Google & Oxford Model Time-Shifts People in Videos
New Google & Oxford Model Time-Shifts People in Videos New Google & Oxford Model Time-Shifts People in Videos

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.

5 дней, 18 часов назад @ medium.com
After 15 Long Years, a NumPy Paper Finally Appears!
After 15 Long Years, a NumPy Paper Finally Appears! After 15 Long Years, a NumPy Paper Finally Appears!

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.

6 дней, 15 часов назад @ medium.com
New DeepMind Approach ‘Bootstraps’ Self-Supervised Learning of Image Representations
New DeepMind Approach ‘Bootstraps’ Self-Supervised Learning of Image Representations New DeepMind Approach ‘Bootstraps’ Self-Supervised Learning of Image Representations

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.

1 неделя назад @ medium.com
Czech Tech Does the Monster Mash: Animating 3D Models in Seconds
Czech Tech Does the Monster Mash: Animating 3D Models in Seconds Czech Tech Does the Monster Mash: Animating 3D Models in Seconds

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.

1 неделя назад @ medium.com
Facebook AI Enables Automatic Closed Captions for Facebook Live
Facebook AI Enables Automatic Closed Captions for Facebook Live Facebook AI Enables Automatic Closed Captions for Facebook Live

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.

1 неделя, 1 день назад @ medium.com
Human Feedback Improves OpenAI Model Summarizations
Human Feedback Improves OpenAI Model Summarizations Human Feedback Improves OpenAI Model Summarizations

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.

1 неделя, 1 день назад @ medium.com
Microsoft Democratizes DeepSpeed With Four New Technologies
Microsoft Democratizes DeepSpeed With Four New Technologies Microsoft Democratizes DeepSpeed With Four New Technologies

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.

1 неделя, 2 дня назад @ medium.com
Ex-Uber AI Chief Scientist Zoubin Ghahramani Joins Google Brain Leadership Team
Ex-Uber AI Chief Scientist Zoubin Ghahramani Joins Google Brain Leadership Team Ex-Uber AI Chief Scientist Zoubin Ghahramani Joins Google Brain Leadership Team

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.

1 неделя, 3 дня назад @ medium.com
A Closer Look at the Generalization Gap in Large Batch Training of Neural Networks
A Closer Look at the Generalization Gap in Large Batch Training of Neural Networks A Closer Look at the Generalization Gap in Large Batch Training of Neural Networks

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.

1 неделя, 3 дня назад @ medium.com
🔬 Science
Papers With Code Papers With Code
последний пост 13 часов назад
Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation
Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation

In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been largely neglected in previous research.

In this paper, we propose a difference-aware knowledge selection method...

It first computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns.

Then, the differential information is fused with or disentangled from the contextual information to facilitate final knowledge selection.

Automatic, human observational, and interactive evaluation shows that our method is able to select knowledge more accurate…

13 часов назад @ paperswithcode.com
"Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code
"Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code "Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code

Neural differential equations may be trained by backpropagating gradients via the adjoint method, which is another differential equation typically solved using an adaptive-step-size numerical differential equation solver.

Here, we demonstrate that the particular structure of the adjoint equations makes the usual choices of norm (such as $L^2$) unnecessarily stringent.

By replacing it with a more appropriate (semi)norm, fewer steps are unnecessarily rejected and the backpropagation is made faster.

Experiments on a wide range of tasks---including time series, generative modeling, and physical control---demonstrate a median improvement of 40% fewer function evaluations.

On some problems we see…

13 часов назад @ paperswithcode.com
COPOD: Copula-Based Outlier Detection
COPOD: Copula-Based Outlier Detection COPOD: Copula-Based Outlier Detection

Outlier detection refers to the identification of rare items that are deviant from the general data distribution.

Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability... As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution.

COPOD first constructs an empirical copula, and then uses it to predict tail probabilities of each given data point to determine its level of "extremeness".

This makes COPOD both parameter-free, highly interpretable, and computationally efficient.

In this work, we make three key contributions, 1) propose a novel, …

13 часов назад @ paperswithcode.com
Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging
Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging

Ezafe is a grammatical particle in some Iranian languages that links two words together.

Regardless of the important information it conveys, it is almost always not indicated in Persian script, resulting in mistakes in reading complex sentences and errors in natural language processing tasks...

In this paper, we experiment with different machine learning methods to achieve state-of-the-art results in the task of ezafe recognition.

Transformer-based methods, BERT and XLMRoBERTa, achieve the best results, the latter achieving 2.68% F1-score more than the previous state-of-the-art.

We, moreover, use ezafe information to improve Persian part-of-speech tagging results and show that such informat…

13 часов назад @ paperswithcode.com
Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams
Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams

Continual learning from streaming data sources becomes more and more popular due to the increasing number of online tools and systems.

This poses a critical problem of providing labels for potentially unbounded streams.

In the real world, we are forced to deal with very strict budget limitations, therefore, we will most likely face the scarcity of annotated instances, which are essential in supervised learning.

In our work, we emphasize this problem and propose a novel instance exploitation technique.

Finally, we conduct a complex in-depth comparative analysis of our methods, using state-of-the-art streaming algorithms relevant to the given problem.

13 часов назад @ paperswithcode.com
Longformer for MS MARCO Document Re-ranking Task
Longformer for MS MARCO Document Re-ranking Task Longformer for MS MARCO Document Re-ranking Task

Two step document ranking, where the initial retrieval is done by a classical information retrieval method, followed by neural re-ranking model, is the new standard.

The best performance is achieved by using transformer-based models as re-rankers, e.g., BERT... We employ Longformer, a BERT-like model for long documents, on the MS MARCO document re-ranking task.

The complete code used for training the model can be found on: https://github.com/isekulic/longformer-marco (read more)

13 часов назад @ paperswithcode.com
Renovating Parsing R-CNN for Accurate Multiple Human Parsing
Renovating Parsing R-CNN for Accurate Multiple Human Parsing Renovating Parsing R-CNN for Accurate Multiple Human Parsing

Multiple human parsing aims to segment various human parts and associate each part with the corresponding instance simultaneously.

Through analysis of multiple human parsing task, we observe that human-centric global perception and accurate instance-level parsing scoring are crucial for obtaining high-quality results.

To reverse this phenomenon, we present Renovating Parsing R-CNN (RP R-CNN), which introduces a global semantic enhanced feature pyramid network and a parsing re-scoring network into the existing high-performance pipeline.

The proposed RP R-CNN adopts global semantic representation to enhance multi-scale features for generating human parsing maps, and regresses a confidence sco…

13 часов назад @ paperswithcode.com
Accent Estimation of Japanese Words from Their Surfaces and Romanizations for Building Large Vocabulary Accent Dictionaries
Accent Estimation of Japanese Words from Their Surfaces and Romanizations for Building Large Vocabulary Accent Dictionaries Accent Estimation of Japanese Words from Their Surfaces and Romanizations for Building Large Vocabulary Accent Dictionaries

In Japanese text-to-speech (TTS), it is necessary to add accent information to the input sentence.

However, there are a limited number of publicly available accent dictionaries, and those dictionaries e.g.

In order to build a large scale accent dictionary that contains those words, the authors developed an accent estimation technique that predicts the accent of a word from its limited information, namely the surface (e.g.

The authors applied this technique to an existing large vocabulary Japanese dictionary NEologd, and obtained a large vocabulary Japanese accent dictionary.

Many cases have been observed in which the use of this dictionary yields more appropriate phonetic information than U…

13 часов назад @ paperswithcode.com
Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild
Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild

This paper addresses the problem of monocular 3D human shape and pose estimation from an RGB image.

Despite great progress in this field in terms of pose prediction accuracy, state-of-the-art methods often predict inaccurate body shapes... We suggest that this is primarily due to the scarcity of in-the-wild training data with diverse and accurate body shape labels.

Thus, we propose STRAPS (Synthetic Training for Real Accurate Pose and Shape), a system that utilises proxy representations, such as silhouettes and 2D joints, as inputs to a shape and pose regression neural network, which is trained with synthetic training data (generated on-the-fly during training using the SMPL statistical bod…

13 часов назад @ paperswithcode.com
Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization
Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization

Crop type classification using satellite observations is an important tool for providing insights about planted area and enabling estimates of crop condition and yield, especially within the growing season when uncertainties around these quantities are highest.

As the climate changes and extreme weather events become more frequent, these methods must be resilient to changes in domain shifts that may occur, for example, due to shifts in planting timelines...

In this work, we present an approach for within-season crop type classification using moderate spatial resolution (30 m) satellite data that addresses domain shift related to planting timelines by normalizing inputs by crop growth stage.…

13 часов назад @ paperswithcode.com
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

This work presents Kornia, an open source computer vision library built upon a set of differentiable routines and modules that aims to solve generic computer vision problems.

The package uses PyTorch as its main backend, not only for efficiency but also to take advantage of the reverse auto-differentiation engine to define and compute the gradient of complex functions...

Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be integrated into neural networks to train models to perform a wide range of operations including image transformations,camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detect…

13 часов назад @ paperswithcode.com
Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration
Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration

Classical image registration methods are computationally expensive but are able to handle these artifacts relatively better.

However, deep learning-based techniques are shown to be computationally efficient for automated brain registration but are sensitive to the intensity variations.

In this study, we investigate the effect of variation in intensity distribution among input image pairs for deep learning-based image registration methods.

We find a performance degradation of these models when brain image pairs with different intensity distribution are presented even with similar structures.

This investigation highlights a possible performance limiting factor in deep learning-based registrat…

13 часов назад @ paperswithcode.com
Towards Fast, Accurate and Stable 3D Dense Face Alignment
Towards Fast, Accurate and Stable 3D Dense Face Alignment Towards Fast, Accurate and Stable 3D Dense Face Alignment

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.

13 часов назад @ paperswithcode.com
DiffWave: A Versatile Diffusion Model for Audio Synthesis
DiffWave: A Versatile Diffusion Model for Audio Synthesis DiffWave: A Versatile Diffusion Model for Audio Synthesis

In this work, we propose DiffWave, a versatile Diffusion probabilistic model for conditional and unconditional Waveform generation.

The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis...

DiffWave produces high-fidelity audios in Different Waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation.

We demonstrate that DiffWave matches a strong WaveNet vocoder in terms of speech quality~(MOS: 4.44 versus 4.43), while synthesizing orders of magnitude faster.

In particular, it significantly outperforms …

13 часов назад @ paperswithcode.com
Improving Graph Property Prediction with Generalized Readout Functions
Improving Graph Property Prediction with Generalized Readout Functions Improving Graph Property Prediction with Generalized Readout Functions

Graph property prediction is drawing increasing attention in the recent years due to the fact that graphs are one of the most general data structures since they can contain an arbitrary number of nodes and connections between them, and it is the backbone for many different tasks like classification and regression on such kind of data (networks, molecules, knowledge bases, ...).

We introduce a novel generalized global pooling layer to mitigate the information loss that typically occurs at the Readout phase in Message-Passing Neural Networks...

This novel layer is parametrized by two values ($\beta$ and $p$) which can optionally be learned, and the transformation it performs can revert to sev…

13 часов назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 13 часов назад
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models

Given access to accurate dynamical models, modern planning approaches are effective in computing feasible and optimal plans for repetitive robotic tasks.

CMAX, while being provably guaranteed to reach the goal, requires strong assumptions on the accuracy of the model used for planning and fails to improve the quality of the solution over repetitions of the same task.

In this paper we propose CMAX++, an approach that leverages real-world experience to improve the quality of resulting plans over successive repetitions of a robotic task.

CMAX++ achieves this by integrating model-free learning using acquired experience with model-based planning using the potentially inaccurate model.

We provide…

13 часов назад @ paperswithcode.com
A Deep Learning Based Analysis-Synthesis Framework For Unison Singing
A Deep Learning Based Analysis-Synthesis Framework For Unison Singing A Deep Learning Based Analysis-Synthesis Framework For Unison Singing

Unison singing is the name given to an ensemble of singers simultaneously singing the same melody and lyrics.

While each individual singer in a unison sings the same principle melody, there are slight timing and pitch deviations between the singers, which, along with the ensemble of timbres, give the listener a perceived sense of "unison"...

In this paper, we present a study of unison singing in the context of choirs; utilising some recently proposed deep-learning based methodologies, we analyse the fundamental frequency (F0) distribution of the individual singers in recordings of unison mixtures.

Based on the analysis, we propose a system for synthesising a unison signal from an a cappella…

13 часов назад @ paperswithcode.com
Robust Outlier Arm Identification
Robust Outlier Arm Identification Robust Outlier Arm Identification

We study the problem of Robust Outlier Arm Identification (ROAI), where the goal is to identify arms whose expected rewards deviate substantially from the majority, by adaptively sampling from their reward distributions.

We compute the outlier threshold using the median and median absolute deviation of the expected rewards...

This is a robust choice for the threshold compared to using the mean and standard deviation, since it can identify outlier arms even in the presence of extreme outlier values.

We propose two $\delta$-PAC algorithms for ROAI, which includes the first UCB-style algorithm for outlier detection, and derive upper bounds on their sample complexity.

Experimental results show …

13 часов назад @ paperswithcode.com
Discriminative Segmentation Tracking Using Dual Memory Banks
Discriminative Segmentation Tracking Using Dual Memory Banks Discriminative Segmentation Tracking Using Dual Memory Banks

Existing template-based trackers usually localize the target in each frame with bounding box, thereby being limited in learning pixel-wise representation and handling complex and non-rigid transformation of the target.

Further, existing segmentation tracking methods are still insufficient in modeling and exploiting dense correspondence of target pixels across frames... To overcome these limitations, this work presents a novel discriminative segmentation tracking architecture equipped with dual memory banks, i.e., appearance memory bank and spatial memory bank.

In particular, the appearance memory bank utilizes spatial and temporal non-local similarity to propagate segmentation mask to the c…

13 часов назад @ paperswithcode.com
Claraprint: a chord and melody based fingerprint for western classical music cover detection
Claraprint: a chord and melody based fingerprint for western classical music cover detection Claraprint: a chord and melody based fingerprint for western classical music cover detection

Cover song detection has been an active field in the Music Information Retrieval (MIR) community during the past decades.

Most of the research community focused in solving it for a wide range of music genres with diverse characteristics... Western classical music, a genre heavily based on the recording of "cover songs", or musical works, represents a large heritage, offering immediate application for an efficient fingerprint algorithm.

We propose an engineering approach for retrieving a cover song from a reference database thanks to a fingerprint designed for classical musical works.

We open a new data set to encourage the scientific community to use it for further researches regarding this…

13 часов назад @ paperswithcode.com
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media

In this paper we suggest a minimally-supervised approach for identifying nuanced frames in news article coverage of politically divisive topics.

We suggest to break the broad policy frames suggested by Boydstun et al., 2014 into fine-grained subframes which can capture differences in political ideology in a better way... We evaluate the suggested subframes and their embedding, learned using minimal supervision, over three topics, namely, immigration, gun-control and abortion.

We demonstrate the ability of the subframes to capture ideological differences and analyze political discourse in news media.

(read more)

13 часов назад @ paperswithcode.com
Integration of Clinical Criteria into the Training of Deep Models: Application to Glucose Prediction for Diabetic People
Integration of Clinical Criteria into the Training of Deep Models: Application to Glucose Prediction for Diabetic People Integration of Clinical Criteria into the Training of Deep Models: Application to Glucose Prediction for Diabetic People

Standard objective functions used during the training of neural-network-based predictive models do not consider clinical criteria, leading to models that are not necessarily clinically acceptable.

In this study, we propose the coherent mean squared glycemic error (gcMSE) loss function.

The results show that using the gcMSE loss function, instead of a standard MSE loss function, improves the clinical acceptability of the models.

Finally, we show that this tradeoff between accuracy and clinical acceptability can be successfully addressed with the proposed algorithm.

For given clinical criteria, the algorithm can find the optimal solution that maximizes the accuracy while at the same meeting t…

13 часов назад @ paperswithcode.com
Global-to-Local Neural Networks for Document-Level Relation Extraction
Global-to-Local Neural Networks for Document-Level Relation Extraction Global-to-Local Neural Networks for Document-Level Relation Extraction

Relation extraction (RE) aims to identify the semantic relations between named entities in text.

Recent years have witnessed it raised to the document level, which requires complex reasoning with entities and mentions throughout an entire document...

In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations.

Entity global representations model the semantic information of all entities in the document, entity local representations aggregate the contextual information of multiple mentions of specific entities, and context relation representations encode the t…

13 часов назад @ paperswithcode.com
LP2PB: Translating Answer Set Programs into Pseudo-Boolean Theories
LP2PB: Translating Answer Set Programs into Pseudo-Boolean Theories LP2PB: Translating Answer Set Programs into Pseudo-Boolean Theories

Answer set programming (ASP) is a well-established knowledge representation formalism.

Most ASP solvers are based on (extensions of) technology from Boolean satisfiability solving...

While these solvers have shown to be very successful in many practical applications, their strength is limited by their underlying proof system, resolution.

In this paper, we present a new tool LP2PB that translates ASP programs into pseudo-Boolean theories, for which solvers based on the (stronger) cutting plane proof system exist.

We evaluate our tool, and the potential of cutting-plane-based solving for ASP on traditional ASP benchmarks as well as benchmarks from pseudo-Boolean solving.

13 часов назад @ paperswithcode.com
Automating Outlier Detection via Meta-Learning
Automating Outlier Detection via Meta-Learning Automating Outlier Detection via Meta-Learning

Given an unsupervised outlier detection (OD) task on a new dataset, how can we automatically select a good outlier detection method and its hyperparameter(s) (collectively called a model)?

In this work, we develop the first principled data-driven approach to model selection for OD, called MetaOD, based on meta-learning.

MetaOD capitalizes on the past performances of a large body of detection models on existing outlier detection benchmark datasets, and carries over this prior experience to automatically select an effective model to be employed on a new dataset.

Through comprehensive experiments, we show the effectiveness of MetaOD in selecting a detection model that significantly outperforms…

13 часов назад @ paperswithcode.com
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks.

Besides, previous works usually ignore the interaction between aspect terms when labeling polarities... We propose a GRadient hArmonized and CascadEd labeling model (GRACE) to solve these problems.

Specifically, a cascaded labeling module is developed to enhance the interchange between aspect terms and improve the attention of sentiment tokens when labeling sentiment polarities.

The polarities sequence is designed to depend on the generated aspect terms labels.

Experimental results demonstrate that the proposed …

13 часов назад @ paperswithcode.com
Conditional Sequential Modulation for Efficient Global Image Retouching
Conditional Sequential Modulation for Efficient Global Image Retouching Conditional Sequential Modulation for Efficient Global Image Retouching

Practically, photo retouching can be accomplished by a series of image processing operations...

In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs).

Based on this analysis, we propose an extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching.

The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector.

To realize retouching operations, we modulate the inte…

13 часов назад @ paperswithcode.com
Tabling Optimization for Contextual Abduction
Tabling Optimization for Contextual Abduction Tabling Optimization for Contextual Abduction

Tabling for contextual abduction in logic programming has been introduced as a means to store previously obtained abductive solutions in one context to be reused in another context.

This paper identifies a number of issues in the existing implementations of tabling in contextual abduction and aims to mitigate the issues... We propose a new program transformation for integrity constraints to deal with their proper application for filtering solutions while also reducing the table memory usage.

We further optimize the table memory usage by selectively picking predicates to table and by pragmatically simplifying the representation of the problem.

The evaluation of our proposed approach, on both…

13 часов назад @ paperswithcode.com
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey

Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach.

In SNE, every point is consider to be the neighbor of all other points with some probability and this probability is tried to be preserved in the embedding space... SNE considers Gaussian distribution for the probability in both the input and embedding spaces.

However, t-SNE uses the Student-t and Gaussian distributions in these spaces, respectively.

In this tutorial and survey paper, we explain SNE, symmetric SNE, t-SNE (or Cauchy-SNE), and t-SNE with general degrees of freedom.

Some simulations to visualize the embeddings are also provided.

13 часов назад @ paperswithcode.com
Variational Disentanglement for Rare Event Modeling
Variational Disentanglement for Rare Event Modeling Variational Disentanglement for Rare Event Modeling

Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems.

However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest is often very low relative to the available sample size...

Though very prevalent in healthcare, such imbalanced classification settings are also common and challenging in many other scenarios.

So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems.

Results on synthetic studies…

1 день, 12 часов назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 13 часов назад
IDA: Improved Data Augmentation Applied to Salient Object Detection
IDA: Improved Data Augmentation Applied to Salient Object Detection IDA: Improved Data Augmentation Applied to Salient Object Detection

In this paper, we present an Improved Data Augmentation (IDA) technique focused on Salient Object Detection (SOD).

Our proposed technique enables more precise control of the object's position and size while preserving background information.

The background choice is based on an inter-image optimization, while object size follows a uniform random distribution within a specified interval, and the object position is intra-image optimal.

We show that our method improves the segmentation quality when used for training state-of-the-art neural networks on several famous datasets of the SOD field.

Combining our method with others surpasses traditional techniques such as horizontal-flip in 0.52% for…

1 день, 12 часов назад @ paperswithcode.com
SCREENet: A Multi-view Deep Convolutional Neural Network for Classification of High-resolution Synthetic Mammographic Screening Scans
SCREENet: A Multi-view Deep Convolutional Neural Network for Classification of High-resolution Synthetic Mammographic Screening Scans SCREENet: A Multi-view Deep Convolutional Neural Network for Classification of High-resolution Synthetic Mammographic Screening Scans

Purpose: To develop and evaluate the accuracy of a multi-view deep learning approach to the analysis of high-resolution synthetic mammograms from digital breast tomosynthesis screening cases, and to assess the effect on accuracy of image resolution and training set size.

Materials and Methods: In a retrospective study, 21,264 screening digital breast tomosynthesis (DBT) exams obtained at our institution were collected along with associated radiology reports...

The 2D synthetic mammographic images from these exams, with varying resolutions and data set sizes, were used to train a multi-view deep convolutional neural network (MV-CNN) to classify screening images into BI-RADS classes (BI-RADS …

1 день, 12 часов назад @ paperswithcode.com
PMVOS: Pixel-Level Matching-Based Video Object Segmentation
PMVOS: Pixel-Level Matching-Based Video Object Segmentation PMVOS: Pixel-Level Matching-Based Video Object Segmentation

Semi-supervised video object segmentation (VOS) aims to segment arbitrary target objects in video when the ground truth segmentation mask of the initial frame is provided.

Due to this limitation of using prior knowledge about the target object, feature matching, which compares template features representing the target object with input features, is an essential step...

Recently, pixel-level matching (PM), which matches every pixel in template features and input features, has been widely used for feature matching because of its high performance.

However, despite its effectiveness, the information used to build the template features is limited to the initial and previous frames.

We address th…

1 день, 12 часов назад @ paperswithcode.com
Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
Face Sketch Synthesis with Style Transfer using Pyramid Column Feature Face Sketch Synthesis with Style Transfer using Pyramid Column Feature

In this paper, we propose a novel framework based on deep neural networks for face sketch synthesis from a photo.

Imitating the process of how artists draw sketches, our framework synthesizes face sketches in a cascaded manner... A content image is first generated that outlines the shape of the face and the key facial features.

Textures and shadings are then added to enrich the details of the sketch.

We utilize a fully convolutional neural network (FCNN) to create the content image, and propose a style transfer approach to introduce textures and shadings based on a newly proposed pyramid column feature.

We demonstrate that our style transfer approach based on the pyramid column feature can …

1 день, 12 часов назад @ paperswithcode.com
Residual Spatial Attention Network for Retinal Vessel Segmentation
Residual Spatial Attention Network for Retinal Vessel Segmentation Residual Spatial Attention Network for Retinal Vessel Segmentation

Reliable segmentation of retinal vessels can be employed as a way of monitoring and diagnosing certain diseases, such as diabetes and hypertension, as they affect the retinal vascular structure.

In this work, we propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation... RSAN employs a modified residual block structure that integrates DropBlock, which can not only be utilized to construct deep networks to extract more complex vascular features, but can also effectively alleviate the overfitting.

Moreover, in order to further improve the representation capability of the network, based on this modified residual block, we introduce the spatial attention (SA) and pr…

1 день, 12 часов назад @ paperswithcode.com
Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change
Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change

Biology is both an important application area and a source of motivation for development of advanced machine learning techniques.

Although much attention has been paid to large and complex data sets resulting from high-throughput sequencing, advances in high-quality video recording technology have begun to generate similarly rich data sets requiring sophisticated techniques from both computer vision and time-series analysis...

Here, we focus on one such example from the study of reproductive regulation in small laboratory colonies of $\sim$50 Harpgenathos ants.

These ants can be artificially induced to begin a $\sim$20 day process of hierarchy reformation.

Specifically, we build upon the De…

1 день, 12 часов назад @ paperswithcode.com
Efficient Certification of Spatial Robustness
Efficient Certification of Spatial Robustness Efficient Certification of Spatial Robustness

Recent work has exposed the vulnerability of computer vision models to spatial transformations.

Due to the widespread usage of such models in safety-critical applications, it is crucial to quantify their robustness against spatial transformations...

However, existing work only provides empirical quantification of spatial robustness via adversarial attacks, which lack provable guarantees.

In this work, we propose novel convex relaxations, which enable us, for the first time, to provide a certificate of robustness against spatial transformations.

Our convex relaxations are model-agnostic and can be leveraged by a wide range of neural network verifiers.

1 день, 12 часов назад @ paperswithcode.com
PP-OCR: A Practical Ultra Lightweight OCR System
PP-OCR: A Practical Ultra Lightweight OCR System PP-OCR: A Practical Ultra Lightweight OCR System

The Optical Character Recognition (OCR) systems have been widely used in various of application scenarios, such as office automation (OA) systems, factory automations, online educations, map productions etc.

However, OCR is still a challenging task due to the various of text appearances and the demand of computational efficiency...

In this paper, we propose a practical ultra lightweight OCR system, i.e., PP-OCR.

The overall model size of the PP-OCR is only 3.5M for recognizing 6622 Chinese characters and 2.8M for recognizing 63 alphanumeric symbols, respectively.

We introduce a bag of strategies to either enhance the model ability or reduce the model size.

1 день, 12 часов назад @ paperswithcode.com
Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward
Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward

We considered a novel practical problem of online learning with episodically revealed rewards, motivated by several real-world applications, where the contexts are nonstationary over different episodes and the reward feedbacks are not always available to the decision making agents.

For this online semi-supervised learning setting, we introduced Background Episodic Reward LinUCB (BerlinUCB), a solution that easily incorporates clustering as a self-supervision module to provide useful side information when rewards are not observed... Our experiments on a variety of datasets, both in stationary and nonstationary environments of six different scenarios, demonstrated clear advantages of the prop…

2 дня, 12 часов назад @ paperswithcode.com
Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection
Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection

Object detectors are usually trained with large amount of labeled data, which is expensive and labor-intensive.

Pre-trained detectors applied to unlabeled dataset always suffer from the difference of dataset distribution, also called domain shift... Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance.

In this paper, we are the first to reveal that the region proposal network (RPN) and region proposal classifier~(RPC) in the endemic two-stage detectors (e.g., Faster RCNN) demonstrate significantly different transferability when facing large domain gap.

Moreover, the samples with low-confidence are used for discrepa…

2 дня, 12 часов назад @ paperswithcode.com
ExGAN: Adversarial Generation of Extreme Samples
ExGAN: Adversarial Generation of Extreme Samples ExGAN: Adversarial Generation of Extreme Samples

Existing approaches based on Generative Adversarial Networks (GANs) excel at generating realistic samples, but seek to generate typical samples, rather than extreme samples.

Hence, in this work, we propose ExGAN, a GAN-based approach to generate realistic and extreme samples.

For practical utility, our framework allows the user to specify both the desired extremeness measure, as well as the desired extremeness probability they wish to sample at.

Experiments on real US Precipitation data show that our method generates realistic samples, based on visual inspection and quantitative measures, in an efficient manner.

Moreover, generating increasingly extreme examples using ExGAN can be done in c…

2 дня, 12 часов назад @ paperswithcode.com
MStream: Fast Streaming Multi-Aspect Group Anomaly Detection
MStream: Fast Streaming Multi-Aspect Group Anomaly Detection MStream: Fast Streaming Multi-Aspect Group Anomaly Detection

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.

2 дня, 12 часов назад @ paperswithcode.com
Intrusion Detection for Cyber-Physical Systems using Generative Adversarial Networks in Fog Environment
Intrusion Detection for Cyber-Physical Systems using Generative Adversarial Networks in Fog Environment Intrusion Detection for Cyber-Physical Systems using Generative Adversarial Networks in Fog Environment

Cyber-attacks on cyber-physical systems (CPSs) can lead to sensing and actuation misbehavior, severe damages to physical objects, and safety risks.

In this context, Generative Adversarial Networks (GANs) are a promising unsupervised approach to detect cyber-attacks by implicitly modeling the system.

In this paper, we propose FID-GAN, a novel fog-based, unsupervised intrusion detection system (IDS) for CPSs using GANs.

In order to achieve higher detection rates, the proposed architecture computes a reconstruction loss based on the reconstruction of data samples mapped to the latent space.

We address this problem by training an Encoder that accelerates the reconstruction loss computation.

2 дня, 12 часов назад @ paperswithcode.com
FarsTail: A Persian Natural Language Inference Dataset
FarsTail: A Persian Natural Language Inference Dataset FarsTail: A Persian Natural Language Inference Dataset

Natural language inference (NLI) is known as one of the central tasks in natural language processing (NLP) which encapsulates many fundamental aspects of language understanding.

With the considerable achievements of data-hungry deep learning methods in NLP tasks, a great amount of effort has been devoted to develop more diverse datasets for different languages...

In this paper, we present a new dataset for the NLI task in the Persian language, also known as Farsi, which is one of the dominant languages in the Middle East.

This dataset, named FarsTail, includes 10,367 samples which are provided in both the Persian language as well as the indexed format to be useful for non-Persian researcher…

2 дня, 12 часов назад @ paperswithcode.com
Progressive Semantic-Aware Style Transformation for Blind Face Restoration
Progressive Semantic-Aware Style Transformation for Blind Face Restoration Progressive Semantic-Aware Style Transformation for Blind Face Restoration

Face restoration is important in face image processing, and has been widely studied in recent years.

However, previous works often fail to generate plausible high quality (HQ) results for real-world low quality (LQ) face images...

In this paper, we propose a new progressive semantic-aware style transformation framework, named PSFR-GAN, for face restoration.

Specifically, instead of using an encoder-decoder framework as previous methods, we formulate the restoration of LQ face images as a multi-scale progressive restoration procedure through semantic-aware style transformation.

Finally, we pretrain a face parsing network which can generate decent parsing maps from real-world LQ face images.

2 дня, 12 часов назад @ paperswithcode.com
📓 Cool Blogs
ODS.ai Habr
последний пост 6 дней, 1 час назад
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 →

6 дней, 1 час 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…

1 месяц 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…

1 месяц, 2 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…

1 месяц, 2 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

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

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

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

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

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

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

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

3 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

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

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

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

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

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

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

f-BRS: Rethinki…

3 месяца, 1 неделя назад @ habr.com
Рубрика «Читаем статьи за вас». Апрель 2020. Часть 2
Рубрика «Читаем статьи за вас». Апрель 2020. Часть 2 Рубрика «Читаем статьи за вас». Апрель 2020. Часть 2

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

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

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

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

ResNeSt: Split-Attention Networks (Amazon, 2020)

Weight Standardization (Johns Hopkins University, 2019)

Supervised Contrastive Learning (Google Research, MIT, 2020)

Improved Training Speed, Accurac…

3 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

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

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

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

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

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

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

Designing Network Design Spaces (FAIR, 2020)

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

4 месяца 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…

4 месяца, 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…

5 месяцев, 1 неделя 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…

5 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…

5 месяцев, 2 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…

5 месяцев, 4 недели 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 →

6 месяцев, 1 неделя назад @ habr.com
Настройка функции потерь для нейронной сети на данных сейсморазведки
Настройка функции потерь для нейронной сети на данных сейсморазведки Настройка функции потерь для нейронной сети на данных сейсморазведки

В прошлой статье мы описали эксперимент по определению минимального объема вручную размеченных срезов для обучения нейронной сети на данных сейсморазведки. Сегодня мы продолжаем эту тему, выбирая наиболее подходящую функцию потерь. Рассмотрены 2 базовых класса функций – Binary cross entropy и Intersection over Union – в 6-ти вариантах с подбором параметров, а также комбинации функций разных классов. Дополнительно рассмотрена регуляризация функции потерь. Спойлер: удалось существенно улучшить качество прогноза сети. Читать дальше →

7 месяцев, 1 неделя назад @ habr.com
inFERENCe inFERENCe
последний пост 10 месяцев, 2 недели назад
The Spectator The Spectator
последний пост 1 месяц, 2 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…

1 месяц, 2 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.

6 месяцев, 4 недели назад @ blog.shakirm.com
The Unofficial Google Data Science Blog The Unofficial Google Data Science Blog
последний пост 2 месяца назад
Changing assignment weights with time-based confounders
Changing assignment weights with time-based confounders Changing assignment weights with time-based confounders

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

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

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

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

2 месяца назад @ unofficialgoogledatascience.com
Humans-in-the-loop forecasting: integrating data science and business planning
Humans-in-the-loop forecasting: integrating data science and business planning Humans-in-the-loop forecasting: integrating data science and business planning

Figure 1: A Google data centerAs an example, consider Google’s forecasting and planning for data center capacity.

In particular, the data scientist must take responsibility for stakeholders approving the “best” forecast from all available information sources.

It required investments from our data science team to re-think our statistical forecasting approach to make it easier to compare against customer forecasts.

It also owns Google’s internal time series forecasting platform described in an earlier blog post .

But looking through the blogosphere, some go further and posit that “platformization” of forecasting and “forecasting as a service” can turn anyone into a data scientist at the push …

9 месяцев, 3 недели назад @ unofficialgoogledatascience.com
Off the Convex Path
последний пост 4 дня, 22 часа 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…

4 дня, 22 часа 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.

2 месяца, 2 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…

3 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…

5 месяцев назад @ offconvex.org
Machine Learning Mastery Machine Learning Mastery
последний пост 1 день, 17 часов назад
How to Train to the Test Set in Machine Learning
How to Train to the Test Set in Machine Learning How to Train to the Test Set in Machine Learning

Tutorial OverviewThis tutorial is divided into three parts; they are:Train to the Test Set Train to Test Set for Classification Train to Test Set for RegressionTrain to the Test SetIn applied machine learning, we seek a model that learns the relationship between the input and output variables using the training dataset.

For example, we could discard all rows in the training set that are too different from the test set and only train on those rows in the training set that are maximally similar to rows in the test set.

While the test set data often have the outcome data blinded, it is possible to “train to the test” by only using the training set samples that are most similar to the test set …

1 день, 17 часов назад @ machinelearningmastery.com
Multi-Core Machine Learning in Python With Scikit-Learn
Multi-Core Machine Learning in Python With Scikit-Learn Multi-Core Machine Learning in Python With Scikit-Learn

Tutorial OverviewThis tutorial is divided into five parts; they are:Multi-Core Scikit-Learn Multi-Core Model Training Multi-Core Model Evaluation Multi-Core Hyperparameter Tuning RecommendationsMulti-Core Scikit-LearnMachine learning can be computationally expensive.

The scikit-learn Python machine learning library provides this capability via the n_jobs argument on key machine learning tasks, such as model training, model evaluation, and hyperparameter tuning.

Now that we are familiar with the scikit-learn library’s capability to support multi-core parallel processing for machine learning, let’s work through some examples.

Multi-Core Model TrainingMany machine learning algorithms support m…

3 дня, 17 часов назад @ machinelearningmastery.com
Automated Machine Learning (AutoML) Libraries for Python
Automated Machine Learning (AutoML) Libraries for Python Automated Machine Learning (AutoML) Libraries for Python

It is ideal for domain experts new to machine learning or machine learning practitioners looking to get good results quickly for a predictive modeling task.

Open-source libraries are available for using AutoML methods with popular machine learning libraries in Python, such as the scikit-learn machine learning library.

Tutorial OverviewThis tutorial is divided into four parts; they are:Automated Machine Learning Auto-Sklearn Tree-based Pipeline Optimization Tool (TPOT) Hyperopt-SklearnAutomated Machine LearningAutomated Machine Learning, or AutoML for short, involves the automatic selection of data preparation, machine learning model, and model hyperparameters for a predictive modeling task.…

6 дней, 17 часов назад @ machinelearningmastery.com
Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)
Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization) Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)

This characterization is generally referred to as Combined Algorithm Selection and Hyperparameter Optimization, or “CASH Optimization” for short.

OverviewThis tutorial is divided into three parts; they are:Challenge of Model and Hyperparameter Selection Solutions to Model and Hyperparameter Selection Combined Algorithm Selection and Hyperparameter OptimizationChallenge of Model and Hyperparameter SelectionThere is no definitive mapping of machine learning algorithms to predictive modeling tasks.

in their 2013 paper titled “Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms.” In the paper, they refer to this problem as “Combined Algorithm Selection And…

1 неделя, 1 день назад @ machinelearningmastery.com
Hyperparameter Optimization With Random Search and Grid Search
Hyperparameter Optimization With Random Search and Grid Search Hyperparameter Optimization With Random Search and Grid Search

Tutorial OverviewThis tutorial is divided into five parts; they are:Model Hyperparameter Optimization Hyperparameter Optimization Scikit-Learn API Hyperparameter Optimization for Classification Random Search for Classification Grid Search for Classification Hyperparameter Optimization for Regression Random Search for Regression Grid Search for Regression Common Questions About Hyperparameter OptimizationModel Hyperparameter OptimizationMachine learning models have hyperparameters.

... # define model model = LogisticRegression() # define search space space = dict() ... # define search search = GridSearchCV(model, space) 1 2 3 4 5 6 7 8 .

# define model model = LogisticRegression ( ) # define…

1 неделя, 3 дня назад @ machinelearningmastery.com
HyperOpt for Automated Machine Learning With Scikit-Learn
HyperOpt for Automated Machine Learning With Scikit-Learn HyperOpt for Automated Machine Learning With Scikit-Learn

Tutorial OverviewThis tutorial is divided into four parts; they are:HyperOpt and HyperOpt-Sklearn How to Install and Use HyperOpt-Sklearn HyperOpt-Sklearn for Classification HyperOpt-Sklearn for RegressionHyperOpt and HyperOpt-SklearnHyperOpt is an open-source Python library for Bayesian optimization developed by James Bergstra.

An extension to HyperOpt was created called HyperOpt-Sklearn that allows the HyperOpt procedure to be applied to data preparation and machine learning models provided by the popular Scikit-Learn open-source machine learning library.

# minimally prepare dataset X = X .

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] # minimally prepare dataset X = X .

values dat…

1 неделя, 6 дней назад @ machinelearningmastery.com
TPOT for Automated Machine Learning in Python
TPOT for Automated Machine Learning in Python TPOT for Automated Machine Learning in Python

Tutorial OverviewThis tutorial is divided into four parts; they are:TPOT for Automated Machine Learning Install and Use TPOT TPOT for Classification TPOT for RegressionTPOT for Automated Machine LearningTree-based Pipeline Optimization Tool, or TPOT for short, is a Python library for automated machine learning.

Install and Use TPOTThe first step is to install the TPOT library, which can be achieved using pip, as follows:pip install tpot 1 pip install tpotOnce installed, we can import the library and print the version number to confirm it was installed successfully:# check tpot version import tpot print('tpot: %s' % tpot.__version__) 1 2 3 # check tpot version import tpot print ( 'tpot: %s' …

2 недели, 1 день назад @ machinelearningmastery.com
Auto-Sklearn for Automated Machine Learning in Python
Auto-Sklearn for Automated Machine Learning in Python Auto-Sklearn for Automated Machine Learning in Python

Auto-Sklearn is an open-source Python library for AutoML using machine learning models from the scikit-learn machine learning library.

Install and Using Auto-SklearnThe first step is to install the Auto-Sklearn library, which can be achieved using pip, as follows:sudo pip install autosklearn 1 sudo pip install autosklearnOnce installed, we can import the library and print the version number to confirm it was installed successfully:# print autosklearn version import autosklearn print('autosklearn: %s' % autosklearn.__version__) 1 2 3 # print autosklearn version import autosklearn print ( 'autosklearn: %s' % autosklearn .

autosklearn: 0.6.0 1 autosklearn: 0.6.0Using Auto-Sklearn is straightfo…

2 недели, 3 дня назад @ machinelearningmastery.com
Scikit-Optimize for Hyperparameter Tuning in Machine Learning
Scikit-Optimize for Hyperparameter Tuning in Machine Learning Scikit-Optimize for Hyperparameter Tuning in Machine Learning

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] print ( X .

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] print ( X .

Automatically Tune Algorithm HyperparametersThe Scikit-Learn machine learning library provides tools for tuning model hyperparameters.

... # define evaluation cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) # define the search search = BayesSearchCV(estimator=SVC(), search_spaces=params, n_jobs=-1, cv=cv) 1 2 3 4 5 .

# perform the search search .

2 недели, 6 дней назад @ machinelearningmastery.com
How to Use AutoKeras for Classification and Regression
How to Use AutoKeras for Classification and Regression How to Use AutoKeras for Classification and Regression

In the spirit of Keras, AutoKeras provides an easy-to-use interface for different tasks, such as image classification, structured data classification or regression, and more.

shape ) # define the search search = StructuredDataClassifier ( max_trials = 15 ) # perform the search search .

... # define the search search = StructuredDataRegressor(max_trials=15, loss='mean_absolute_error') # perform the search search.fit(x=X_train, y=y_train, verbose=0) 1 2 3 4 5 .

# define the search search = StructuredDataRegressor ( max_trials = 15 , loss = 'mean_absolute_error' ) # perform the search search .

shape ) # define the search search = StructuredDataRegressor ( max_trials = 15 , loss = 'mean_absolut…

3 недели, 1 день назад @ machinelearningmastery.com
Multi-Label Classification with Deep Learning
Multi-Label Classification with Deep Learning Multi-Label Classification with Deep Learning

Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.”Deep learning neural networks are an example of an algorithm that natively supports multi-label classification problems.

Neural network models for multi-label classification tasks can be easily defined and evaluated using the Keras deep learning library.

In this tutorial, you will discover how to develop deep learning models for multi-label classification.

# define the model model = Sequential() model.add(Dense(20, input_dim=n_inputs, kernel_initializer='he_uniform…

3 недели, 3 дня назад @ machinelearningmastery.com
Deep Learning Models for Multi-Output Regression
Deep Learning Models for Multi-Output Regression Deep Learning Models for Multi-Output Regression

Deep learning neural networks are an example of an algorithm that natively supports multi-output regression problems.

Neural network models for multi-output regression tasks can be easily defined and evaluated using the Keras deep learning library.

In this tutorial, you will discover how to develop deep learning models for multi-output regression.

# define the model model = Sequential ( ) model .

SummaryIn this tutorial, you discovered how to develop deep learning models for multi-output regression.

3 недели, 6 дней назад @ machinelearningmastery.com
Time Series Forecasting With Prophet in Python
Time Series Forecasting With Prophet in Python Time Series Forecasting With Prophet in Python

columns = [ 'ds' , 'y' ] df [ 'ds' ] = to_datetime ( df [ 'ds' ] ) # define the model model = Prophet ( ) # fit the model model .

columns = [ 'ds' , 'y' ] df [ 'ds' ] = to_datetime ( df [ 'ds' ] ) # define the model model = Prophet ( ) # fit the model model .

columns = [ 'ds' ] future [ 'ds' ] = to_datetime ( future [ 'ds' ] )Tying this together, the complete example is listed below.

columns = [ 'ds' , 'y' ] df [ 'ds' ] = to_datetime ( df [ 'ds' ] ) # define the model model = Prophet ( ) # fit the model model .

tail ( ) ) # define the model model = Prophet ( ) # fit the model model .

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

1 месяц, 2 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…

3 месяца, 2 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…

5 месяцев, 2 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.…

7 месяцев, 4 недели назад @ lilianweng.github.io
Piekniewski's blog
последний пост 3 месяца, 2 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.

3 месяца, 2 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…

5 месяцев, 1 неделя 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.

6 месяцев, 3 недели назад @ blog.piekniewski.info
Sebastian Ruder Sebastian Ruder
последний пост 1 год, 7 месяцев назад
💼 University and corporation labs
DeepMind DeepMind
последний пост 3 недели назад
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…

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.

3 месяца 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.

4 месяца, 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.

5 месяцев 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…

5 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…

5 месяцев, 3 недели 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…

7 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?

8 месяцев, 1 неделя 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

8 месяцев, 1 неделя назад @ deepmind.com
Using WaveNet technology to reunite speech-impaired users with their original voices
Using WaveNet technology to reunite speech-impaired users with their original voices Using WaveNet technology to reunite speech-impaired users with their original voices

This post details a recent project we undertook with Google and ALS campaigner Tim Shaw, as part of Google’s Euphonia project.

We demonstrate an early proof of concept of how text-to-speech technologies can synthesise a high-quality, natural sounding voice using minimal recorded speech data.

But message banking lacks flexibility, resulting in a static dataset of phrases.

Now imagine that you were given the chance to preserve your voice by recording as much of it as possible.

And people who aren’t able to record phrases in time are left to choose a generic computer synthesized voice that lacks the same power of connection as their own.

9 месяцев, 1 неделя назад @ deepmind.com
Learning human objectives by evaluating hypothetical behaviours
Learning human objectives by evaluating hypothetical behaviours Learning human objectives by evaluating hypothetical behaviours

TL;DR: We present a method for training reinforcement learning agents from human feedback in the presence of unknown unsafe states.

Training RL agents in the presence of unsafe states is known as the safe exploration problem.

The agent has one source of information: feedback about unsafe states from a human user.

Existing methods for training agents from human feedback ask the user to evaluate data of the agent acting in the environment.

The user provides feedback on this hypothetical behaviour, and the system interactively learns a model of the user's reward function.

9 месяцев, 2 недели назад @ deepmind.com
From unlikely start-up to major scientific organisation: Entering our tenth year at DeepMind
From unlikely start-up to major scientific organisation: Entering our tenth year at DeepMind From unlikely start-up to major scientific organisation: Entering our tenth year at DeepMind

Pioneering research, growing impactA mission this ambitious requires pioneering research on many fronts over many years.

As our research matures, we’ve been finding more opportunities to partner with others for social and commercial impact, often with our colleagues across Alphabet.

Entering our next phaseAs I discussed with Wired in the summer, this year feels like the start of a new phase for DeepMind as an established scientific organisation.

Over the past year, we’ve also been formalising a leadership team with the seasoned experience and skills for our second decade.

Right back to our origins blending neuroscience with machine learning, we’ve found that breakthroughs happen faster when…

9 месяцев, 3 недели назад @ deepmind.com
Google Google
последний пост 2 недели, 5 дней назад
An AI gold mine: What happened at Google Cloud Next ‘20 OnAir
An AI gold mine: What happened at Google Cloud Next ‘20 OnAir An AI gold mine: What happened at Google Cloud Next ‘20 OnAir

AI and machine learning (ML) tools and solutions are fundamentally changing how businesses are run.

This week at Google Cloud Next '20: OnAir we explored how Cloud AI is empowering teams with AI and ML tools and solutions across a range of skills and knowledge.

Customers lead the wayWe heard how organizations using Google Cloud AI are transforming digitally to improve customer and user experiences.

And McDonald's demonstrated how it’s using Google Cloud AI to get better insights into how to serve its loyal customers.

Discover how a Japanese startup uses Google Cloud AI for faster time-to-market, quick object detection, and creating a flexible ML pipeline for active learning.

2 недели, 5 дней назад @ cloud.google.com
The Technology Behind our Recent Improvements in Flood Forecasting
The Technology Behind our Recent Improvements in Flood Forecasting The Technology Behind our Recent Improvements in Flood Forecasting

Forecasting Water LevelsThe first step in a flood forecasting system is to identify whether a river is expected to flood.

The hydrologic model component of the flood forecasting system described in this week’s Keyword post doubled the lead time of flood alerts for areas covering more than 75 million people.

Once a river is predicted to reach flood level, the next step in generating actionable warnings is to convert the river level forecast into a prediction for how the floodplain will be affected.

We assume that if the gauge increases, the water level increases monotonically, i.e., the water level at other points in the river increases as well.

AcknowledgementsThis work is a collaboration b…

2 недели, 6 дней назад @ ai.googleblog.com
KeyPose: Estimating the 3D Pose of Transparent Objects from Stereo
KeyPose: Estimating the 3D Pose of Transparent Objects from Stereo KeyPose: Estimating the 3D Pose of Transparent Objects from Stereo

In “KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects”, presented at CVPR 2020 in collaboration with the Stanford AI Lab, we describe an ML system that estimates the depth of transparent objects by directly predicting 3D keypoints.

On the right, we visualize points from a 3D model of the bottle, placed at the pose determined by the predicted 3D keypoints.

We provide DenseFusion with two versions of depth, one from the transparent objects, and one from opaque objects.

ConclusionThis work shows that it is possible to accurately estimate the 3D pose of transparent objects from RGB images without reliance on depth images.

We hope the availability of an extensive, l…

3 недели назад @ ai.googleblog.com
Partnering with NSF on human-AI collaboration
Partnering with NSF on human-AI collaboration Partnering with NSF on human-AI collaboration

The Institute we are announcing with NSF will support interdisciplinary research on a variety of modes of interaction between people and AI—like speech, written language, visuals and gestures—and how to make these interactions more effective.

Importantly, the research, tools and techniques from the Institute will be developed with human-centered principles in mind: social benefit, inclusive design, safety and robustness, privacy, and high standards of scientific excellence, consistent with the Google AI Principles.

Research projects in the Institute will engage a diverse set of experts, educate the next generation and promote workforce development, and broaden participation from underrepres…

3 недели назад @ blog.google
A big step for flood forecasts in India and Bangladesh
A big step for flood forecasts in India and Bangladesh A big step for flood forecasts in India and Bangladesh

This year, we’ve launched a new forecasting model that will allow us to double the lead time of many of our alerts—providing more notice to governments and giving tens of millions of people an extra day or so to prepare.

We’re providing people with information about flood depth: when and how much flood waters are likely to rise.

And in areas where we can produce depth maps throughout the floodplain, we’re sharing information about depth in the user’s village or area.

We’ve also overhauled the way our alerts look and function to make sure they’re useful and accessible for everyone.

We now provide the information in different formats, so that people can both read their alerts and see them pre…

3 недели, 1 день назад @ blog.google
Conversational AI drives better customer experiences
Conversational AI drives better customer experiences Conversational AI drives better customer experiences

Conversational AI is opening up a new world of possibilities in areas like customer experience, user engagement, and access to content.

In Cloud AI, we’ve taken Google’s groundbreaking machine learning models in speech and natural language processing and applied them to the contact center space, radically improving the customer experience while also driving down operational costs.

CCAI is also driving cost savings without cutting corners on customer service.

Contact Center AI (CCAI) lets you have both.

CCAI does this by expediting customer requests using virtual agents, assisting live agents, and providing insights on customer interactions to drive improvements back into the system.

3 недели, 2 дня назад @ cloud.google.com
Empowering teams to unlock the value of AI
Empowering teams to unlock the value of AI Empowering teams to unlock the value of AI

Cloud AI Building Blocks provide access to commonly used models (for vision, translation, speech etc) via APIs.

Improved experience for your customersOur expertise and leadership in AI is one of the reasons many organizations choose Google Cloud.

We are steadily transfering advancements from Google AI research into cloud solutions that help you create better experiences for your customers.

Lending Document AI is a new, specialized solution powered by Document AI for the mortgage industry, that processes borrowers’ income and asset documents to speed-up loan applications—a notoriously slow and complex process.

For more info, join us at Google Cloud Next OnAir for Cloud AI week, when Principa…

3 недели, 2 дня назад @ cloud.google.com
Key requirements for an MLOps foundation
Key requirements for an MLOps foundation Key requirements for an MLOps foundation

Machine learning (ML) systems have a special capacity for creating technical debt if not managed well.

Put another way—creating an ML model is the easy part—operationalizing and managing the lifecycle of ML models, data and experiments is where it gets complicated.

Unifying ML system development and operationsStarting with AI Platform Pipelines: we announced a hosted offering for building and managing ML pipelines on AI Platform earlier this year.

Like DevOps, MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops).

Unlike DevOps, ML systems present unique challenges to core DevOps principles like Continuous Integration…

3 недели, 2 дня назад @ cloud.google.com
Taking care of business with Responsible AI
Taking care of business with Responsible AI Taking care of business with Responsible AI

In a survey of global business executives, over 90% reported encountering ethical issues in connection with adoption of an AI system.

At Google, we believe that rigorous evaluations of how to build AI responsibly are not only the right thing to do, they are a critical component of creating successful AI.

We began developing our AI Principles mid-2017, and published them a year later in June of 2018.

They are a living constitution we use to guide our approach to building advanced technologies, conducting research, and drafting our policies.

How Google Cloud puts our AI Principles into practiceOur governance processes are designed to implement our AI Principles in a systematic, repeatable way.

3 недели, 2 дня назад @ cloud.google.com
mixi accelerates AI adoption with help from Google Cloud Advanced Solutions Lab
mixi accelerates AI adoption with help from Google Cloud Advanced Solutions Lab mixi accelerates AI adoption with help from Google Cloud Advanced Solutions Lab

Artificial intelligence (AI) and machine learning (ML) have been helping businesses become more efficient for years now, and their applications are growing seemingly by the day.

Given this fast evolution, it’s understandable that many organizations aren’t quite sure how to best use AI and ML to help their business.

A big part of that was attending an immersive AI and ML training at Google Cloud’s Advanced Solutions Lab (ASL), which aims to help customers make better use of AI and ML technologies through virtual or onsite education.

“I don’t personally work in the AI department, but my work touches on several projects, so I still see the technical side,” Kojo explains.

“It should work as a c…

3 недели, 2 дня назад @ cloud.google.com
TabNet on AI Platform: High-performance, Explainable Tabular Learning
TabNet on AI Platform: High-performance, Explainable Tabular Learning TabNet on AI Platform: High-performance, Explainable Tabular Learning

Deep learning for tabular dataAlthough tabular data is the most common data type in real-world AI, deep learning for tabular data remains under-explored.

Using TabNet on AI PlatformGoogle's TabNet is now available as a built-in algorithm on Cloud AI Platform Training.

The TabNet built-in algorithm makes it easy for you to build and train models with the TabNet architecture.

You can start with the built-in algorithm by selecting "AI Platform -> Jobs -> +New Training Job -> Builtin algorithm Training" in the cloud console.

Then, to use TabNet, simply select it from the built-in algorithm dropdown after uploading your training data:

3 недели, 2 дня назад @ cloud.google.com
A developer’s take: Get the most out of Cloud AI Week at Next On Air
A developer’s take: Get the most out of Cloud AI Week at Next On Air A developer’s take: Get the most out of Cloud AI Week at Next On Air

Welcome to week 8 of Google Cloud Next ‘20: OnAir, where the focus is Cloud AI!

This week our goal is to show you how to generate value with AI on our AI Platform.

At the end of the week, Karl Weinmeister is hosting a developer- and operator-focused live recap and Q&A session.

In this demo, NASA’s Frontier Development Lab partnered with Google Cloud to use AI to accelerate exoplanet research.

If you're new to Cloud AI, check out the AI Platform overview and Qwik Start.

3 недели, 2 дня назад @ cloud.google.com
Beginners guide to painless machine learning
Beginners guide to painless machine learning Beginners guide to painless machine learning

Happily, over the past five years, developing with machine learning has gotten much easier thanks to user-friendly tooling.

Nowadays I find myself spending very little time building and tuning machine learning models and much more time on traditional app development.

In this post, I’ll walk you through some of my favorite, painless Google Cloud AI tools and share my tips for building AI-powered apps fast.

Use Pre-trained ModelsOne of the slowest and most unpleasant parts of machine learning projects is collecting labeled training data--labeled examples that a machine learning algorithm can “learn” from.

It works on images (AutoML Vision), video (AutoML Video), language (AutoML Natural Langu…

3 недели, 2 дня назад @ cloud.google.com
Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screenings
Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screenings Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screenings

In “Detecting Deficient Coverage in Colonoscopies”, we introduce the Colonoscopy Coverage Deficiency via Depth algorithm, or C2D2, a machine learning-based approach to improving colonoscopy coverage.

In addition to the detection of deficient coverage, depth and pose estimation are useful for a variety of other interesting tasks.

Performance on Synthetic VideosWhen using synthetic videos, the availability of ground truth coverage enables the direct measurement of C2D2’s performance.

Indeed, the learning pipeline is designed to perform equally well on synthetic and real colonoscopy videos.

Coverage on real colonoscopy sequences.

3 недели, 5 дней назад @ ai.googleblog.com
Scaling Up Fundamental Quantum Chemistry Simulations on Quantum Hardware
Scaling Up Fundamental Quantum Chemistry Simulations on Quantum Hardware Scaling Up Fundamental Quantum Chemistry Simulations on Quantum Hardware

Unfortunately, the exact solution of quantum chemical equations for all but the smallest systems remains out of reach for modern classical computers, due to the exponential scaling in the number and statistics of quantum variables.

In “Hartree-Fock on a Superconducting Qubit Quantum Computer”, appearing today in Science, the Google AI Quantum team explores this complex question by performing the largest chemical simulation performed on a quantum computer to date.

In our experiment, we used a noise-robust variational quantum eigensolver (VQE) to directly simulate a chemical mechanism via a quantum algorithm.

Importantly, we validate that algorithms being developed for currently available qua…

3 недели, 6 дней назад @ ai.googleblog.com
OpenAI OpenAI
последний пост 1 день, 21 час 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 день, 21 час назад @ openai.com
Learning to Summarize with Human Feedback
Learning to Summarize with Human Feedback Learning to Summarize with Human Feedback

We've applied reinforcement learning from human feedback to train language models that are better at summarization.

Our approach follows directly from our previous work on learning from human feedback.

In particular, our 1.3 billion parameter (1.3B) model trained with human feedback outperforms our 12B model trained only with supervised learning.

Note that our human feedback models generate summaries that are significantly shorter than summaries from models trained on CNN/DM.

This suggests that our human feedback models have learned something more general about how to summarize text, and are not specific to Reddit posts.

2 недели, 5 дней 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.

2 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…

3 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.

3 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.

3 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…

4 месяца, 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 …

4 месяца, 3 недели 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…

5 месяцев, 1 неделя 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…

5 месяцев, 1 неделя 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.

7 месяцев, 3 недели назад @ openai.com
OpenAI Five
OpenAI Five OpenAI Five

You play against [OpenAI Five] and you realize it has a playstyle that is different.

It’s doing things that you’ve never done and you’ve never seen.

One key learning that we took is how it was allocating resources.

It’s just allocating resources as efficiently as possible.

[…] If OpenAI does that dynamic switch at 100%, we maybe went from 5% to 10%?

9 месяцев, 2 недели назад @ openai.com
Deep Double Descent
Deep Double Descent Deep Double Descent

Many classes of modern deep learning models, including CNNs, ResNets, and transformers, exhibit the previously-observed double descent phenomenon when not using early stopping or regularization.

The model-wise double descent phenomenon can lead to a regime where training on more data hurts.

The double descent phenomena is most prominent in settings with added label noise; without it, the peak is smaller and easy to miss.

For a given number of optimization steps (fixed y-coordinate), test and train error exhibit model-size double descent.

We leave fully understanding the mechanisms behind double descent in deep neural networks as an important open question.

9 месяцев, 3 недели назад @ openai.com
Procgen Benchmark
Procgen Benchmark Procgen Benchmark

We’re releasing Procgen Benchmark, 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.

To fulfill this need, we have created Procgen Benchmark.

CoinRun now serves as the inaugural environment in Procgen Benchmark, contributing its diversity to a greater whole.

With Procgen Benchmark, we strive for all of the following: experimental convenience, high diversity within environments, and high diversity across environments.

We've now expanded on those results, conducting our most thorough study of RL generalization to date using all 16 environments in Procgen Benchmark.

9 месяцев, 3 недели назад @ openai.com
Microsoft Microsoft
последний пост 2 недели, 6 дней назад
Expressive Pixels: A new visual communication platform to support creativity, accessibility, and innovation
Expressive Pixels: A new visual communication platform to support creativity, accessibility, and innovation Expressive Pixels: A new visual communication platform to support creativity, accessibility, and innovation

It’s no coincidence, then, that a new platform being released by Microsoft Research, called Expressive Pixels, stems from this belief.

To learn more about the Expressive Pixels journey, read on or explore one of its many facets by clicking on a topic above.

This is what I call full stack!” Gavin Jancke on the process of creating the Expressive Pixels platformThe story of the Expressive Pixels doesn’t end there.

See Figure 1 below for a visual layout of the design, and explore the Expressive Pixels documentation for more details.

Learn more about our contributors at the Expressive Pixels team page.

2 недели, 6 дней назад @ microsoft.com
Platform for Situated Intelligence: An open-source framework for multimodal, integrative AI
Platform for Situated Intelligence: An open-source framework for multimodal, integrative AI Platform for Situated Intelligence: An open-source framework for multimodal, integrative AI

GITHUB Platform for Situated IntelligenceTo address these challenges and create a solid foundation for development, experimentation, and research in this space, we’ve built Platform for Situated Intelligence, an open-source framework for multimodal, integrative AI systems.

Platform for Situated Intelligence addresses these challenges by making time a primary construct in the underlying streaming infrastructure.

We’ve introduced above only a few of the core concepts and affordances in the Platform for Situated Intelligence streaming infrastructure.

Platform for Situated Intelligence was built to accelerate this progress and foster more innovation and research in this space.

We hope you give …

3 недели назад @ microsoft.com
Domain-specific language model pretraining for biomedical natural language processing
Domain-specific language model pretraining for biomedical natural language processing Domain-specific language model pretraining for biomedical natural language processing

By pretraining solely on biomedical text from scratch, our PubMedBERT model outperforms all prior language models and obtains new state-of-the-art results in a wide range of biomedical applications.

A new paradigm for building neural language models in biomedicine and specialized domainsPretrained neural language models are the underpinning of state-of-the-art NLP methods.

Biomedical Term Category BERT SciBERT PubMedBERT (Ours) diabetes disease X X X leukemia disease X X X lithium drug X X X insulin drug X X X DNA gene X X X promoter gene X X X hypertension disease X X nephropathy disease X X lymphoma disease X X lidocaine drug X X oropharyngeal organ X cardiomyocyte cell X chloramphenicol …

3 недели, 2 дня назад @ microsoft.com
Microsoft HoloLens 2: Improved Research Mode to facilitate computer vision research
Microsoft HoloLens 2: Improved Research Mode to facilitate computer vision research Microsoft HoloLens 2: Improved Research Mode to facilitate computer vision research

In Research Mode, HoloLens 2 is also a potent computer vision research device.

(Note: Research Mode is available today to Windows Insiders and soon in an upcoming release of Windows 10 for HoloLens .)

Compared to the previous edition, Research Mode for HoloLens 2 has the following main advantages:In addition to sensors exposed in HoloLens 1 Research Mode, we now also provide IMU sensor access (these include an accelerometer, gyroscope, and magnetometer).

Specifically, articulated hand-tracking and eye-tracking which can be accessed through APIs while using research mode, allowing for a richer set of experiments.

Research Mode for HoloLens 2 also provides researchers with access to the accel…

3 недели, 6 дней назад @ microsoft.com
Facebook Facebook
последний пост 2 месяца назад
MIT AI MIT AI
последний пост 19 часов назад
MIT undergraduates pursue research opportunities through the pandemic
MIT undergraduates pursue research opportunities through the pandemic MIT undergraduates pursue research opportunities through the pandemic

Working from home this summer, students participating in MIT’s Undergraduate Research Opportunities Program (UROP) did their best to overcome these challenges.

The lab of Una-May O’Reilly, a principal research scientist at MIT, is focused on finding and fixing the weaknesses in code-processing models that can cause them to misbehave.

MIT Professor Nancy Kanwisher and her lab are investigating how this special ear for speech and music arises in the infant brain.

Somaia Saba, a second-year student at MIT, was introduced to Kanwisher’s research last year in an intro to neuroscience class and immediately wanted to learn more.

Before the pandemic shut down campus, Kanwisher’s lab collected funct…

19 часов назад @ news.mit.edu
Regina Barzilay wins $1M Association for the Advancement of Artificial Intelligence Squirrel AI award
Regina Barzilay wins $1M Association for the Advancement of Artificial Intelligence Squirrel AI award Regina Barzilay wins $1M Association for the Advancement of Artificial Intelligence Squirrel AI award

As these disciplines undoubtedly continue to impact society, newer fields like artificial intelligence (AI) and robotics have also begun to profoundly reshape the world.

In recognition of this, the world’s largest AI society — the Association for the Advancement of Artificial Intelligence (AAAI) — announced today the winner of their new Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, a $1 million award given to honor individuals whose work in the field has had a transformative impact on society.

Since surviving breast cancer in 2014, she has increasingly focused her efforts on health care.

She created algorithms for early breast cancer diagnosis and risk assessmen…

1 день назад @ news.mit.edu
Six strategic areas identified for shared faculty hiring in computing
Six strategic areas identified for shared faculty hiring in computing Six strategic areas identified for shared faculty hiring in computing

Associated schools: School of Humanities, Arts and the Social Sciences and MIT Sloan School of Management.

Associated schools: School of Science; School of Humanities, Arts, and Social Sciences; and School of Architecture and Planning.

Associated schools: School of Engineering; School of Science; and School of Architecture and Planning.

Quantum Computing.

Achieving these advances poses challenges that span multiple scientific and engineering fields, and from quantum hardware to quantum computing algorithms.

3 недели, 2 дня назад @ news.mit.edu
Toward a machine learning model that can reason about everyday actions
Toward a machine learning model that can reason about everyday actions Toward a machine learning model that can reason about everyday actions

Here, in another approach, researchers capitalize on the relationships embedded in the meanings of words to give their model visual reasoning power.

Given a set of videos, the model creates a numerical representation for each video that aligns with the word representations of the actions shown in the video.

To see how the model would do compared to humans, the researchers asked human subjects to perform the same set of visual reasoning tasks online.

“Conceptually it fits, but I had to think about it.”Limitations of the model include a tendency to overemphasize some features.

A deep learning model that can be trained to “think” more abstractly may be capable of learning with fewer data, say …

3 недели, 2 дня назад @ news.mit.edu
National Science Foundation announces MIT-led Institute for Artificial Intelligence and Fundamental Interactions
National Science Foundation announces MIT-led Institute for Artificial Intelligence and Fundamental Interactions National Science Foundation announces MIT-led Institute for Artificial Intelligence and Fundamental Interactions

The U.S. National Science Foundation (NSF) announced today an investment of more than $100 million to establish five artificial intelligence (AI) institutes, each receiving roughly $20 million over five years.

IAIFI researchers are developing AI for such first-principles theory studies, which naturally require AI approaches that rigorously encode physics knowledge.

IAIFI researchers are working to enhance the scientific potential of various facilities, including the Large Hadron Collider (LHC) and the Laser Interferometer Gravity Wave Observatory (LIGO).

“We will tackle two of the greatest mysteries of science: how our universe works and how intelligence works,” says MIT professor of physic…

4 недели назад @ news.mit.edu
Real-time data for a better response to disease outbreaks
Real-time data for a better response to disease outbreaks Real-time data for a better response to disease outbreaks

Now Kinsa is working with health officials in five states and three cities to help contain and control the virus.

Singh says Kinsa’s data complement other methods of containing the virus like testing, contact tracing, and the use of face masks.

Better data for better responsesSingh’s first exposure to MIT came while he was attending the Harvard University Kennedy School of Government as a graduate student.

“The world tries to curb the spread of infectious illness with almost zero real-time information about when and where disease is spreading,” Singh says.

In order to get that data, the company needed a new way of providing value to sick people and families.

1 месяц назад @ news.mit.edu
Rewriting the rules of machine-generated art
Rewriting the rules of machine-generated art Rewriting the rules of machine-generated art

Horses don’t normally wear hats, and deep generative models, or GANs, don’t normally follow rules laid out by human programmers.

“The neural network has different memory banks that function as a set of general rules, relating one set of learned patterns to another,” he says.

It’s not easy to identify all of the neurons corresponding to objects and animals the model renders, the researchers say.

Some rules also appear edit-proof; some changes the researchers tried to make failed to execute.

The tool also brings researchers closer to understanding how GANs learn visual concepts with minimal human guidance.

1 месяц назад @ news.mit.edu
Data systems that learn to be better
Data systems that learn to be better Data systems that learn to be better

Big data has gotten really, really big: By 2025, all the world’s data will add up to an estimated 175 trillion gigabytes.

As a result, for any given workload such systems provide performance that is good, but usually not the best.

In contrast, the goal of instance-optimized systems is to build systems that optimize and partially re-organize themselves for the data they store and the workload they serve.

The goal is to not only relieve developers from the daunting and laborious process of tuning database systems, but to also provide performance and cost benefits that are not possible with traditional systems.

The work was done as part of the Data System and AI Lab (DSAIL@CSAIL), which is spo…

1 месяц, 2 недели назад @ news.mit.edu
Shrinking deep learning’s carbon footprint
Shrinking deep learning’s carbon footprint Shrinking deep learning’s carbon footprint

It’s the latest feat of intelligence achieved by deep learning, a machine learning method patterned after the way neurons in the brain process and store information.

We have to find more efficient ways to scale deep learning or develop other technologies.”Some of the excitement over AI’s recent progress has shifted to alarm.

In work posted on the pre-print server arXiv, Thompson and his colleagues show that the ability of deep learning models to surpass key benchmarks tracks their nearly exponential rise in computing power use.

(Like others seeking to track AI’s carbon footprint, the team had to guess at many models’ energy consumption due to a lack of reporting requirements).

Until a new p…

1 месяц, 2 недели назад @ news.mit.edu
Looking into the black box
Looking into the black box Looking into the black box

Our current era is marked by a superabundance of data — data from inexpensive sensors of all types, text, the internet, and large amounts of genomic data being generated in the life sciences.

But much of this success involves trial and error when it comes to the deep learning networks themselves.

Generalization puzzleThere is a second puzzle about what is sometimes called the unreasonable effectiveness of deep networks.

Though deep learning is actively being applied in the world, this has so far occurred without a comprehensive underlying theory.

“After all, even in its current — still highly imperfect — state, deep learning is impacting, or about to impact, just about every aspect of our s…

1 месяц, 4 недели назад @ news.mit.edu
Commentary: America must invest in its ability to innovate
Commentary: America must invest in its ability to innovate Commentary: America must invest in its ability to innovate

Bush’s report to the president of the United States, “Science: The Endless Frontier,” called on the government to support basic research in university labs.

Named the “Endless Frontier Act,” the bill would support research focused on advancing key technologies like artificial intelligence and quantum computing.

It does not seek to alter or replace the NSF, but to “create new strength in parallel,” they write.

But if leaders take the right steps now, they write, those choices will seem, in retrospect, obvious and wise.

“Now as then, our national prosperity hinges on the next generation of technical triumphs,” Reif and Mcrobbie write.

2 месяца назад @ news.mit.edu
Tackling the misinformation epidemic with “In Event of Moon Disaster”
Tackling the misinformation epidemic with “In Event of Moon Disaster” Tackling the misinformation epidemic with “In Event of Moon Disaster”

The team worked with a voice actor and a company called Respeecher to produce the synthetic speech using deep learning techniques.

Through these sophisticated AI and machine learning technologies, the seven-minute film shows how thoroughly convincing deepfakes can be.

As the technology to produce realistic “deepfakes” becomes more easily available, distinguishing fact from fiction will only get more challenging.

The project is supported by the MIT Open Documentary Lab and the Mozilla Foundation, which awarded “In Event of Moon Disaster” a Creative Media Award last year.

The new website is the project’s global digital launch, making the film and associated materials available for free to all…

2 месяца назад @ news.mit.edu
Faculty receive funding to develop artificial intelligence techniques to combat Covid-19
Faculty receive funding to develop artificial intelligence techniques to combat Covid-19 Faculty receive funding to develop artificial intelligence techniques to combat Covid-19

Artificial intelligence has the power to help put an end to the Covid-19 pandemic.

Now, MIT researchers working on seven groundbreaking projects on Covid-19 will be funded to more rapidly develop and apply novel AI techniques to improve medical response and slow the pandemic spread.

The consortium is dedicated to accelerating advances in research and combining machine learning, artificial intelligence, internet of things, ethics, and public policy — for enhancing societal outcomes.

Gifford was awarded funding for his project that uses machine learning to develop more informed vaccine designs with improved population coverage, and to develop models of Covid-19 disease severity using individu…

2 месяца, 1 неделя назад @ news.mit.edu
Letting robots manipulate cables
Letting robots manipulate cables Letting robots manipulate cables

The team’s new system uses a pair of soft robotic grippers with high-resolution tactile sensors (and no added mechanical constraints) to successfully manipulate freely moving cables.

For humans, it can be challenging to manipulate thin flexible objects like ropes, wires, or cables.

As a cable slides between the fingers, its shape is constantly changing, and the robot’s fingers must be constantly sensing and adjusting the cable’s position and motion.

The team’s second step was to create a perception-and-control framework to allow cable manipulation.

In the future, they plan to study more complex cable manipulation tasks such as cable routing and cable inserting through obstacles, and they wa…

2 месяца, 1 неделя назад @ news.mit.edu
Exploring interactions of light and matter
Exploring interactions of light and matter Exploring interactions of light and matter

His father, trained as a mechanical engineer, spent his career working first in that field, then in electrical engineering, and then civil engineering.

Last year, Hu earned tenure as an associate professor in MIT’s Department of Materials Science and Engineering.

“I got fascinated with light,” he says, recalling how he began working in this field.

This includes work on devices called optical diodes or optical isolators, which allow light to pass through only in one direction, and systems for coupling light signals into and out of photonic chips.

Lately, Hu has been focusing on applying machine-learning methods to improve the performance of optical systems.

2 месяца, 3 недели назад @ news.mit.edu
Berkeley AI
последний пост 2 недели назад
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

AWAC: Accelerating Online Reinforcement Learning with Offline DatasetsOur method learns complex behaviors by training offline from prior datasets (expert demonstrations, data from previous experiments, or random exploration data) and then fine-tuning quickly with online interaction.

Robots trained with reinforcement learning (RL) have the potential to be used across a huge variety of challenging real world problems.

Figure 2: On-policy methods are slow to learn compared to off-policy methods, due to the ability of off-policy methods to “stitch" good trajectories together, illustrated on the left.

We aim to study tasks representative of the difficulties of real-world robot learning, where …

2 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.

1 месяц, 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…

1 месяц, 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 …

2 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?

2 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.

2 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…

3 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…

3 месяца, 1 неделя 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%.

4 месяца, 1 неделя 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,…

4 месяца, 3 недели назад @ bair.berkeley.edu
AWS Machine Learning AWS Machine Learning
последний пост 14 часов назад
Gaining insights into winning football strategies using machine learning
Gaining insights into winning football strategies using machine learning Gaining insights into winning football strategies using machine learning

Compute a new feature score as the product of absolute correlation and feature importance feature_score_i = feat_i * abs(corr_i).

Calculating the new feature scoreIn the previous section, we described the construction of a new feature score.

This new feature score incorporates the feature importance from a non-linear XGBoost model, as well as direct linear correlation.

This trained XGBoost model provides a first look into which features are important to the UIUC football team winning a play.

SummaryUIUC football coaches partnered with the Amazon ML Solutions Lab and created an ML model to gain more insights on their performance and strategies.

14 часов назад @ aws.amazon.com
Detecting and redacting PII using Amazon Comprehend
Detecting and redacting PII using Amazon Comprehend Detecting and redacting PII using Amazon Comprehend

Detecting PII in Amazon ComprehendWhen you analyze text using Amazon Comprehend real-time analysis, Amazon Comprehend automatically identifies PII, as summarized in the following table.

Analyzing text on the Amazon Comprehend consoleTo get started with Amazon Comprehend, all you need is an AWS account.

Each entry shows the PII entity, its type, and the level of confidence Amazon Comprehend has in this analysis.

You can choose redaction mode Replace with PII entity to replace PII entities with PII entity type, or choose to mask PII entity with redaction mode Replace with character and replace the characters in PII entities with a character of your choice (!, #, $, %, &, *, or @).

The pii-s3-…

6 дней, 14 часов назад @ aws.amazon.com
Build alerting and human review for images using Amazon Rekognition and Amazon A2I
Build alerting and human review for images using Amazon Rekognition and Amazon A2I Build alerting and human review for images using Amazon Rekognition and Amazon A2I

Configure permission to invoke the Amazon Rekognition DetectModerationLabels You need to attach the AmazonRekognitionFullAccess policy to the AWS Lambda function that calls the Amazon Rekognition detect_moderation_labels API.

To create a human review workflow, complete the following:In the Augmented AI section on the Amazon SageMaker console, navigate to the Human review workflows Choose Create human review workflow.

You’re redirected to the Human review workflows page, where you can see the name and ARN of the human review workflow you just created.

Completing a human review of your imageTo complete a human review of your image, complete the following steps:Open the URL in the email you re…

6 дней, 16 часов назад @ aws.amazon.com
Serving PyTorch models in production with the Amazon SageMaker native TorchServe integration
Serving PyTorch models in production with the Amazon SageMaker native TorchServe integration Serving PyTorch models in production with the Amazon SageMaker native TorchServe integration

With this release, you can use the native Amazon SageMaker SDK to serve PyTorch models with TorchServe.

It’s important to note that our implementation hides the .mar from the user while still using the Amazon SageMaker PyTorch API everyone is used to.

The TorchServe architecture in Amazon SageMakerYou can use TorchServe natively with Amazon SageMaker through the following steps:Create a model in Amazon SageMaker.

This includes the Amazon S3 path where the model artifacts are stored and the Docker registry path for the Amazon SageMaker TorchServe image.

Whether you’re using Amazon SageMaker, Amazon Elastic Compute Cloud (Amazon EC2), or Amazon Elastic Kubernetes Service (Amazon EKS), you can…

6 дней, 18 часов назад @ aws.amazon.com
Activity detection on a live video stream with Amazon SageMaker
Activity detection on a live video stream with Amazon SageMaker Activity detection on a live video stream with Amazon SageMaker

In this post, you use Amazon SageMaker to automatically detect activities from a custom dataset in a live video stream.

Video segments from the live stream are delivered to Amazon Simple Storage Service (Amazon S3), which invokes the Amazon SageMaker endpoint for inference.

Building, training, and deploying an activity detection model with Amazon SageMakerIn this section, you use Amazon SageMaker to build, train, and deploy an activity detection model in a development environment.

ConclusionIn this post, you used Amazon SageMaker to automatically detect activity in a simulated live video stream.

You also used Amazon SageMaker inference to preprocess and classify a video segment delivered to…

6 дней, 20 часов назад @ aws.amazon.com
Automating the analysis of multi-speaker audio files using Amazon Transcribe and Amazon Athena
Automating the analysis of multi-speaker audio files using Amazon Transcribe and Amazon Athena Automating the analysis of multi-speaker audio files using Amazon Transcribe and Amazon Athena

To provide an accurate analysis of these audio files, the transcriptions need to clearly identify who spoke what and when.

One key feature of the service is called speaker identification, which you can use to label each individual speaker when transcribing multi-speaker audio files.

In this post, we walk through a solution that analyzes audio files involving multiple speakers using Amazon Transcribe and Amazon Athena, a serverless query service for big data.

The solution contains the following steps:You upload the audio file to the Amazon Simple Storage Service (Amazon S3) bucket AudioRawBucket.

ConclusionIn this post, we walked through the solution, reviewed sample implementation of audio …

1 неделя назад @ aws.amazon.com
Learn from the winner of the AWS DeepComposer Chartbusters Spin the Model Challenge
Learn from the winner of the AWS DeepComposer Chartbusters Spin the Model Challenge Learn from the winner of the AWS DeepComposer Chartbusters Spin the Model Challenge

AWS is excited to announce the winner of the second AWS DeepComposer Chartbusters challenge, Lena Taupier.

When Lena Taupier first attended the AWS DeepComposer workshop at re:Invent 2019, she had no idea she would be the winner of the Spin the Model challenge.

Building in AWS DeepComposerTo get started, Lena used the AWS DeepComposer learning capsules to learn more about AR-CNN models.

ConclusionThe AWS DeepComposer Chartbusters challenge Spin the Model helped Lena learn about generative AI through a hands-on and fun experience.

Check out the next AWS DeepComposer Chartbusters challenge, The Sounds of Science, running now until September 23.

1 неделя назад @ aws.amazon.com
Amazon Personalize now available in EU (Frankfurt) Region
Amazon Personalize now available in EU (Frankfurt) Region Amazon Personalize now available in EU (Frankfurt) Region

We’re excited to announce the general availability of Amazon Personalize in the EU (Frankfurt) Region.

For more information, see Amazon Personalize Is Now Generally Available.

To use Amazon Personalize, you need to provide the service user interaction(events) data (such as page views, sign-ups, purchases etc.)

For more information about all the Regions Amazon Personalize is available in, see the AWS Region Table.

Get started with Amazon Personalize by visiting the Amazon Personalize console and Developer Guide.

1 неделя, 1 день назад @ aws.amazon.com
Reducing training time with Apache MXNet and Horovod on Amazon SageMaker
Reducing training time with Apache MXNet and Horovod on Amazon SageMaker Reducing training time with Apache MXNet and Horovod on Amazon SageMaker

In this post, we show how you can reduce training time with MXNet and Horovod on Amazon SageMaker.

Amazon SageMaker and MXNet simplify training with Horovod by managing the complexities to support distributed training at scale.

In this section, we review the key changes required for Horovod to correctly work on Amazon SageMaker with Apache MXNet.

To do this, we use the MXNet estimator class from the Amazon SageMaker Python SDK:#Define the basic configuration of your Horovod-enabled Sagemaker training # cluster.

After the training job is complete, Amazon SageMaker automatically archives all files in this directory and uploads it to an Amazon S3 location that you define in the Python Amazon S…

1 неделя, 1 день назад @ aws.amazon.com
Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks
Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks

The new Amazon SageMaker Studio Image Build convenience package allows data scientists and developers to easily build custom container images from your Studio notebooks via a new CLI.

The Amazon SageMaker Studio Image Build CLI lets you build Amazon SageMaker-compatible Docker images directly from your Amazon SageMaker Studio environments.

AWS CodeBuild – CodeBuild is a fully managed build environment that allows you to build Docker images using a transient build environment.

– CodeBuild is a fully managed build environment that allows you to build Docker images using a transient build environment.

SummaryThis post discussed how you can simplify the build of your Docker images from Amazon S…

1 неделя, 2 дня назад @ aws.amazon.com
How Kabbage improved the PPP lending experience with Amazon Textract
How Kabbage improved the PPP lending experience with Amazon Textract How Kabbage improved the PPP lending experience with Amazon Textract

Prior to the PPP, Kabbage had never issued an SBA loan before.

In this post, we share our experience of how Amazon Textract helped support 80% of Kabbage’s PPP applicants to receive a fully automated lending experience and reduced approval times from multiple days to a median speed of 4 hours.

Specifically, we used StartDocumentAnalysis and GetDocumentAnalysis, which allows us to asynchronously interact with Amazon Textract.

In the end, Amazon Textract was accurate, and it scaled to process a substantial backlog.

Amazon Textract allowed us to add a new arrow to our quiver; we had never extracted values from tax filings prior to the PPP.

1 неделя, 5 дней назад @ aws.amazon.com
Right-sizing resources and avoiding unnecessary costs in Amazon SageMaker
Right-sizing resources and avoiding unnecessary costs in Amazon SageMaker Right-sizing resources and avoiding unnecessary costs in Amazon SageMaker

For more information about the costs involved in your ML journey on Amazon SageMaker, see Lowering total cost of ownership for machine learning and increasing productivity with Amazon SageMaker.

In this section, we discuss general guidelines to help you choose the right resources for your Amazon SageMaker ML lifecycle.

Amazon SageMaker Processing dispatches all things needed for processing the entire dataset, such as code, container, and data, to a compute infrastructure separate from the Amazon SageMaker notebook instance.

Training and tuning environmentThe same compute paradigm and benefits for Amazon SageMaker Processing apply to Amazon SageMaker Training and Tuning.

By understanding how…

1 неделя, 5 дней назад @ aws.amazon.com
Automated monitoring of your machine learning models with Amazon SageMaker Model Monitor and sending predictions to human review workflows using Amazon A2I
Automated monitoring of your machine learning models with Amazon SageMaker Model Monitor and sending predictions to human review workflows using Amazon A2I Automated monitoring of your machine learning models with Amazon SageMaker Model Monitor and sending predictions to human review workflows using Amazon A2I

Amazon SageMaker Model Monitor enables you to continuously monitor ML models in production.

The following JSON code is an example of an inference request and response captured:Starting Amazon SageMaker Model MonitorAmazon SageMaker Model Monitor continuously monitors the quality of ML models in production.

Starting the human review workflowTo configure your human review workflow, you complete the following high-level steps:Create the human task UI.

For more information about using Amazon A2I outputs for model retraining, see Object detection and model retraining with Amazon SageMaker and Amazon Augmented AI.

She works with multiple teams in AWS to create technical documentation and tutorial…

1 неделя, 6 дней назад @ aws.amazon.com
RENGA Inc. automates code reviews with Amazon CodeGuru
RENGA Inc. automates code reviews with Amazon CodeGuru RENGA Inc. automates code reviews with Amazon CodeGuru

Code reviews have been an essential process in our development, because RENGA takes code quality very seriously.

Also, no matter how many times we reviewed code, some bugs remained unnoticed, so we needed a mechanism to conduct code reviews more exhaustively.

We spend about 5% of our development time on code reviews, and reviewers spend an additional 1 hour a day on average for code reviews.

Code reviews can, however, be a bottleneck when we want to quickly release new features and promptly deliver those values to our users.

By adopting CodeGuru Reviewer, we successfully automated code reviews and reduced reviewers’ workloads.

1 неделя, 6 дней назад @ aws.amazon.com
How DevFactory builds better applications with Amazon CodeGuru
How DevFactory builds better applications with Amazon CodeGuru How DevFactory builds better applications with Amazon CodeGuru

Amazon CodeGuru is an automated code review service that helps developers improve their quality of code by recommending actions in code review.

CodeGuru consists of two services:In this post, we talk about how DevFactory uses CodeGuru Reviewer to improve their software as a service (SaaS) applications.

It uses these models to find code issues such as concurrency race conditions, resources leaks, and wasted CPU cycles.

DevFactory’s ideal code analysis solution is:Accurate and focused – The most valuable code analysis tools are both highly accurate and highly targeted.

For DevFactory, CodeGuru flagged syntactically valid code that still produced inaccurate results due to paginated Amazon Dyna…

1 неделя, 6 дней назад @ aws.amazon.com
NVIDIA
последний пост 2 недели, 6 дней назад
Startup’s AI Platform Allows Contact-Free Hospital Interactions
Startup’s AI Platform Allows Contact-Free Hospital Interactions Startup’s AI Platform Allows Contact-Free Hospital Interactions

The platform is optimized on NVIDIA GPUs and its edge deployments use the NVIDIA Jetson TX1 module.

Ouva is a member of NVIDIA Inception, a program that provides AI startups go-to-market support, expertise and technology.

By detecting changes in patient movement and alerting workers of them in real time, the Ouva platform allows nurses to pay attention to the right patient at the right time.

“The platform minimizes the time that nurses may be in the dark about how a patient is doing,” said Demir.

Radboud University Medical Center in the Netherlands recently integrated Ouva’s platform for 10 of its neurology wards.

2 недели, 6 дней назад @ blogs.nvidia.com
DIY with AI: GTC to Host NVIDIA Deep Learning Institute Courses for Anyone, Anywhere
DIY with AI: GTC to Host NVIDIA Deep Learning Institute Courses for Anyone, Anywhere DIY with AI: GTC to Host NVIDIA Deep Learning Institute Courses for Anyone, Anywhere

The NVIDIA Deep Learning Institute is launching three new courses, which can be taken for the first time ever at the GPU Technology Conference next month.

The new instructor-led workshops cover fundamentals of deep learning, recommender systems and Transformer-based applications.

DLI at GTC is offered globally, with several courses available in Korean, Japanese and Simplified Chinese for attendees in their respective time zones.

New DLI workshops launching at GTC include:Fundamentals of Deep Learning — Build the confidence to take on a deep learning project by learning how to train a model, work with common data types and model architectures, use transfer learning between models, and more.

2 недели, 6 дней назад @ blogs.nvidia.com
What Is MLOps?
What Is MLOps? What Is MLOps?

MLOps adds to the team the data scientists, who curate datasets and build AI models that analyze them.

Koumchatzky’s team at NVIDIA developed MagLev, the MLOps software that hosts NVIDIA DRIVE, our platform for creating and testing autonomous vehicles.

Among the team’s other checkpoints, jobs must:Launch containers with an approved mechanismProve the job can run across multiple GPU nodesShow performance data to identify potential bottlenecksShow profiling data to ensure the software has been debuggedThe maturity of MLOps practices used in business today varies widely, according to Edwin Webster, a data scientist who started the MLOps consulting practice a year ago for Neal Analytics and wro…

2 недели, 6 дней назад @ blogs.nvidia.com
Accelerating Single Cell Genomic Analysis using RAPIDS
Accelerating Single Cell Genomic Analysis using RAPIDS Accelerating Single Cell Genomic Analysis using RAPIDS

RAPIDS makes it possible to perform interactive data analysis on large datasets using Python APIs that closely resemble NumPy, Pandas, and scikit-learn.

Consider a typical workflow to perform single cell analysis.

In fact, RAPIDS can accelerate the entire single-cell analysis workflow, making it possible to do interactive exploratory data analysis even on large datasets.

Running the RAPIDS analysis on AWS was also 3x cheaper than the CPU version!

Point-and-click re-clustering of a selected group of cells in real time, by using RAPIDS in an interactive cell browser.

3 недели назад @ developer.nvidia.com
In a Class of Its Own: New Mercedes-Benz S-Class Sports Next-Gen AI Cockpit, Powered by NVIDIA
In a Class of Its Own: New Mercedes-Benz S-Class Sports Next-Gen AI Cockpit, Powered by NVIDIA In a Class of Its Own: New Mercedes-Benz S-Class Sports Next-Gen AI Cockpit, Powered by NVIDIA

The Mercedes-Benz S-Class has always combined the best in engineering with a legendary heritage of craftsmanship.

At a world premiere event, the legendary premium automaker debuted the redesigned flagship S-Class sedan.

“This S-Class is going to be the most intelligent Mercedes ever,” said Mercedes-Benz CEO Ola Källenius during the virtual launch.

Like its predecessor, the next-gen MBUX system runs on the high-performance, energy-efficient compute of NVIDIA GPUs for instantaneous AI processing and sharp graphics.

Leveraging NVIDIA technology, Mercedes-Benz was able to consolidate these components into an AI platform — removing 27 switches and buttons — to simplify the architecture while cre…

3 недели назад @ blogs.nvidia.com
Meet the Researcher: Avantika Lal, Discovering Genes, Proteins, and Biological Processes Altered by COVID-19
Meet the Researcher: Avantika Lal, Discovering Genes, Proteins, and Biological Processes Altered by COVID-19 Meet the Researcher: Avantika Lal, Discovering Genes, Proteins, and Biological Processes Altered by COVID-19

Part of that is due to gaps in our understanding of the basic biology of how the virus affects human cells.

The research of Avantika Lal at NVIDIA has unlocked some key findings about these fundamental mechanisms, discovering genes, proteins, and biological processes in human cells that are specifically altered from SARS-CoV-2.

New data has now come out and it’s great to see that most scientists are making COVID-19 data available before journal publication.

What is the impact of your research on the larger COVID-19 research community?

Our computational analysis discovered genes, proteins, and biological processes in human cells that are specifically altered in SARS-CoV-2 infection.

3 недели, 1 день назад @ developer.nvidia.com
Optimizing and Improving Spark 3.0 Performance with GPUs
Optimizing and Improving Spark 3.0 Performance with GPUs Optimizing and Improving Spark 3.0 Performance with GPUs

Apache Spark 3.0 continues this trend with innovations to improve Spark SQL performance, and NVIDIA GPU acceleration, which I cover in this post.

Innovating and accelerating Spark 3.0 performance with GPUs to meet and exceed the modern requirements of data processing.

Spark 3.0 optimizations for Spark SQLUsing its SQL query execution engine, Apache Spark achieves high performance for batch and streaming data.

Accelerator-aware schedulingAs part of a major Spark initiative to better unify DL and data processing on Spark, GPUs are now a schedulable resource in Apache Spark 3.0.

Horovod now has support for Spark 3.0 with GPU scheduling, and a new KerasEstimator class that uses Spark Estimators…

3 недели, 1 день назад @ developer.nvidia.com
Enhancing Sample Efficiency in Reinforcement Learning with Nonparametric Methods
Enhancing Sample Efficiency in Reinforcement Learning with Nonparametric Methods Enhancing Sample Efficiency in Reinforcement Learning with Nonparametric Methods

In this post, I discuss the nonparametric off-policy policy gradient (NOPG), which has a better bias-variance tradeoff and holds little requirements on how to generate off-policy samples.

Nonparametric off-policy gradient estimationOne important component of reinforcement learning theory is the Bellman equation.

For more information about the implementation code, see Nonparametric Off-Policy Policy Gradient.

You could investigate the applicability of deep learning techniques to allow dimensionality reduction, and the usage of different approximations of the Bellman equation, overcoming the issues of nonparametric techniques.

For more information about the details of this work, see A Nonpara…

3 недели, 1 день назад @ developer.nvidia.com
Streaming Interactive Deep Learning Applications at Peak Performance
Streaming Interactive Deep Learning Applications at Peak Performance Streaming Interactive Deep Learning Applications at Peak Performance

Interactive streaming of webcam input and rendered output using TCP sockets, significantly simplifying the forwarding of remote video devices.

Initially, you connect the virtual display by launching the script StreamClient.py:localuser@localmachine:~/NVSS$ python3 StreamClient.pyConnect the network webcam using the script WebcamClient.py:localuser@localmachine:~/NVSS$ python3 WebcamClient.pyVoilà!

As promised, the time for reading and dispatching frames is negligible compared to processing the data: a truly scalable solution to demonstrate peak performance in interactive applications.

In the following section, you execute an inference pipeline in a GPU-enabled Docker container running on a …

3 недели, 1 день назад @ developer.nvidia.com
Up Your Creative Game: GeForce RTX 30 Series GPUs Amp Up Performance
Up Your Creative Game: GeForce RTX 30 Series GPUs Amp Up Performance Up Your Creative Game: GeForce RTX 30 Series GPUs Amp Up Performance

GeForce RTX 30 Series GPUs, powered by our second-generation RTX architecture, aim to reduce the wait, giving creators more time to focus on what matters: creating amazing content.

Ray Tracing at the Speed of LightThe next generation of dedicated ray tracing cores and improved CUDA performance on GeForce RTX 30 Series GPUs speeds up 3D rendering times by up to 2x across top renderers.

Now with RTX 30 Series and RT Core accelerated apps like Blender Cycles, creators can enjoy up to 5x faster motion blur rendering than prior generation RTX.

These apps, like most of the world’s top creative apps, are supported by NVIDIA Studio Drivers, which provide optimal levels of performance and reliabilit…

3 недели, 1 день назад @ blogs.nvidia.com
‘Giant Step into the Future’: NVIDIA CEO Unveils GeForce RTX 30 Series GPUs
‘Giant Step into the Future’: NVIDIA CEO Unveils GeForce RTX 30 Series GPUs ‘Giant Step into the Future’: NVIDIA CEO Unveils GeForce RTX 30 Series GPUs

And with NVIDIA CEO Jensen Huang Tuesday unveiling the new GeForce RTX 30 Series GPUs, it’s delivering NVIDIA’s “greatest generational leap in company history.”The GeForce RTX 30 Series, NVIDIA’s second-generation RTX GPUs, deliver up to 2x the performance and 1.9x the power efficiency over previous-generation GPUs.

In addition to the trio of new GPUs — the flagship GeForce RTX 3080, the GeForce RTX 3070 and the “ferocious” GeForce RTX 3090 — Huang introduced a slate of new tools for GeForce gamers.

At the SIGGRAPH graphics conference two years ago, NVIDIA unveiled the first NVIDIA RTX GPU.

GeForce RTX 3090: A Big, Ferocious GPUAt the apex of the lineup is the RTX 3090.

When done with your …

3 недели, 1 день назад @ blogs.nvidia.com
Speed Reader: Startup Primer Helps Analysts Make Every Second Count
Speed Reader: Startup Primer Helps Analysts Make Every Second Count Speed Reader: Startup Primer Helps Analysts Make Every Second Count

Expected to read upwards of 200,000 words daily from hundreds, if not thousands, of documents, financial analysts are asked to perform the impossible.

“We train the models to mimic that human behavior,” said Barry Dauber, vice president of commercial sales at Primer.

For prototyping and training of its NLP algorithms such as Named Entity Recognition and Headline Generation (on public, open-source news datasets), Primer uses instances with NVIDIA V100 Tensor Core GPUs.

Model serving and clustering happens on instances with NVIDIA T4 GPUs, which can be dialed up and down based on clustering needs.

There, the models can be trained on a customer’s IP and clustering can be performed on premises.

3 недели, 2 дня назад @ blogs.nvidia.com
Rise and Sunshine: NASA Uses Deep Learning to Map Flows on Sun’s Surface, Predict Solar Flares
Rise and Sunshine: NASA Uses Deep Learning to Map Flows on Sun’s Surface, Predict Solar Flares Rise and Sunshine: NASA Uses Deep Learning to Map Flows on Sun’s Surface, Predict Solar Flares

Looking directly at the sun isn’t recommended — unless you’re doing it with AI, which is what NASA is working on.

So when NASA researchers magnify images of the sun with a telescope, they can see tiny blobs, called granules, moving on the surface.

Studying the movement and flows of the granules helps the researchers better understand what’s happening underneath that outer layer of the sun.

Using data science and GPU computing with NVIDIA Quadro RTX-powered HP Z8 workstations, NASA researchers have developed deep learning techniques to more easily track the flows on the sun’s surface.

RTX Flares Up Deep Learning PerformanceWhen studying how storms and hurricanes form, meteorologists analyze …

3 недели, 2 дня назад @ blogs.nvidia.com
Pixel Perfect: V7 Labs Automates Image Annotation for Deep Learning Models
Pixel Perfect: V7 Labs Automates Image Annotation for Deep Learning Models Pixel Perfect: V7 Labs Automates Image Annotation for Deep Learning Models

Cells under a microscope, grapes on a vine and species in a forest are just a few of the things that AI can identify using the image annotation platform created by startup V7 Labs.

V7 Darwin is trained on several million images and optimized on NVIDIA GPUs.

V7 Labs is a member of NVIDIA Inception, a program that provides AI startups with go-to-market support, expertise and technology assistance.

Saving time and costs around image annotation is especially important in the context of healthcare, he said.

V7 Darwin can be used in a laboratory setting, helping to detect protocol errors and automatically log experiments.

3 недели, 5 дней назад @ blogs.nvidia.com
Deploying a Scalable Object Detection Inference Pipeline
Deploying a Scalable Object Detection Inference Pipeline Deploying a Scalable Object Detection Inference Pipeline

To learn more about architecting an object detection inference pipeline at scale, join the Autonomous Driving at Scale: Architect and Deploy Object Detection Inference Pipelines webinar on Sept. 2, led by NVIDIA and TCS experts.

Object detection inference pipeline componentsGPUs are powering autonomous vehicle software development driven by the scale of data, the scale of computation, and algorithmic innovation.

Object detection inference pipeline overviewThe pre-annotation model lies at the heart of the object detection inference pipeline.

An efficient object detection inference pipeline goes a long way in driving consistent and high-quality labeled output.

This post covered the components…

3 недели, 5 дней назад @ developer.nvidia.com
Apple Machine Learning Journal Apple Machine Learning Journal
последний пост None
Uber Engineering Uber Engineering
последний пост 2 месяца, 3 недели назад
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…

2 месяца, 3 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.

3 месяца, 2 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…

3 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 …

3 месяца, 3 недели 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…

4 месяца, 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.

4 месяца, 2 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…

6 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…

7 месяцев, 1 неделя назад @ eng.uber.com
Uber AI in 2019: Advancing Mobility with Artificial Intelligence
Uber AI in 2019: Advancing Mobility with Artificial Intelligence Uber AI in 2019: Advancing Mobility with Artificial Intelligence

At the forefront of this effort is Uber AI, Uber’s center for advanced artificial intelligence research and platforms.

In this year alone, AI research at Uber has led to significant improvements in demand prediction and more seamless pick-up experiences.

Fostering AI collaboration through open sourceIn 2019, Uber AI was committed to sharing knowledge and best practices with the broader scientific community through open source projects.

Looking towards 2020Next year, Uber AI will continue to innovate, collaborate, and contribute to Uber’s platform services through the application of AI across our business.

For more on Uber AI, be sure to check out related articles on the Uber Engineering Blo…

9 месяцев, 1 неделя назад @ eng.uber.com
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

We in Uber AI Labs investigated the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula to help AI agents rapidly learn.

Increasingly, neural architecture search (NAS) algorithms are being deployed to automate the search for architectures, with great results.

32), new learners are able to learn on synthetic data faster than real data (red line vs. blue line in Figure 1).

In our experiments, the estimates come either from training for 128 SGD steps on GTN-generated data or real data.

Then, for each method, the final best architecture according to the estimate is trained a long time on real data.

9 месяцев, 1 неделя назад @ eng.uber.com
Controlling Text Generation with Plug and Play Language Models
Controlling Text Generation with Plug and Play Language Models Controlling Text Generation with Plug and Play Language Models

This article discusses an alternative approach to controlled text generation, titled the Plug and Play Language Model (PPLM), introduced in a recent paper from Uber AI.

In many ways, language models are like wise but unguided wooly mammoths that lumber wherever they please.

As we will show below, attribute models with only a single layer containing 4,000 parameters perform well at recognizing attributes and guiding generation.

Thus, we use the unmodified language model to ensure the fluency of language is maintained at or near the level of the original language model (in this example, GPT-2-medium).

Multiple attribute modelsWe may combine multiple attribute models in controlled generation, …

9 месяцев, 3 недели назад @ eng.uber.com
Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations
Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations

To this end, we previously developed ML models to better understand queries and for multi-objective optimization in Uber Eats search and recommender system in Uber Eats searches and surfaced food options.

Graph learning in a nutshellTo best understand how we made our Uber Eats recommendations more accurate, it helps to know the basics of how graph learning works.

For example, to represent an eater in our Uber Eats model we don’t only use order history to inform order suggestions, but also information about what food items are connected to past Uber Eats orders and insights about similar users.

For our Uber Eats use case, we opted for a graph neural network (GNN)-based approach to obtain an …

9 месяцев, 3 недели назад @ eng.uber.com
Uber Goes to NeurIPS 2019
Uber Goes to NeurIPS 2019 Uber Goes to NeurIPS 2019

This year, Uber is presenting 11 papers at the NeurIPS 2019 conference in Vancouver, Canada!

Scalable Global Optimization via Local Bayesian OptimizationDavid Eriksson (Uber AI) · Michael Pearce (Uber AI intern / Warwick University) · Jacob Gardner (Uber AI) · Ryan Turner (Uber AI) · Matthias Poloczek (Uber AI)ArXivDecember 10 at 4:25 pm, West Ballroom C, NeurIPS Spotlight TalkDecember 10 at 5:30 pm, East Exhibition Hall B&C, Poster #9Bayesian optimization (BO) has recently emerged as a successful technique for the global optimization of black-box functions.

For additional information about our talks and posters, check out the Uber NeurIPS 2019 site.

Interested in the ML research that Uber …

9 месяцев, 3 недели назад @ eng.uber.com
neptune.ai neptune.ai
последний пост 2 дня, 4 часа назад
This Week in Machine Learning: Books, Movies, Universe as a Neural Network, and Robots That Write
This Week in Machine Learning: Books, Movies, Universe as a Neural Network, and Robots That Write This Week in Machine Learning: Books, Movies, Universe as a Neural Network, and Robots That Write

Weekly Roundup: September 8-21» 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.

» Huge List of Free Artificial Intelligence, Machine Learning, Data Science & Python E-Books on InsaneA list of AI, ML, Deep Learning, Python, and Data Science Free Ebooks available on the internet!

📚» Physicist: The Entire Universe Might Be a Neural Network by Victor Tangermann on Futurism | September 9Is it possible that the entire universe on its most fundamental level is a neural network?

» TOP 10 FASCINATING MOVIES ON DATA SCIENCE, MACHINE LEARNING & AI by Adilin Beatrice on Analytics Insight | September 18We all …

2 дня, 4 часа назад @ neptune.ai
Deep dive into TensorBoard: Tutorial With Examples
Deep dive into TensorBoard: Tutorial With Examples Deep dive into TensorBoard: Tutorial With Examples

How to install TensorBoardBefore you can start using TensorBoard you have to install it either via pip or via condapip install tensorboard conda install -c conda-forge tensorboardUsing TensorBoard with Jupyter notebooks and Google ColabWith TensorBoard installed, you can now load it into your Notebook.

% load_ext tensorboard log_folder = 'logs'How to use TensorBoard callbackThe next step is to specify the TensorBoard callback during the model’s fit method.

Using TensorBoard with deep learning frameworksYou are not limited to using TensorBoard with TensorFlow alone.

from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter(log_dir= 'logs' )The next step is to add the items you…

1 неделя назад @ neptune.ai
Best Tools to Manage Machine Learning Projects
Best Tools to Manage Machine Learning Projects Best Tools to Manage Machine Learning Projects

…by using the right tool that will help you manage machine learning projects.

Some are more end-to-end some are focused on a particular stage of the machine learning lifecycle but all of them will help you manage your machine learning projects.

It facilitates the scaling of machine learning models by making run orchestration and deployments of machine learning workflows easier.

It’s a great platform for teams collaborating on machine learning projects who want to simplify workflow and share ideas conveniently.

It has all the integrated tools for the entire machine learning workflow providing all of the components used for machine learning in a single toolset.

1 неделя, 1 день назад @ neptune.ai
This Week in Machine Learning: Diffbot the Web Crawler, AI Winter, 2021 Trends, & Google Maps
This Week in Machine Learning: Diffbot the Web Crawler, AI Winter, 2021 Trends, & Google Maps This Week in Machine Learning: Diffbot the Web Crawler, AI Winter, 2021 Trends, & Google Maps

In our weekly roundup, we present you the most interesting stories from the world of machine learning and data science.

If you’re interested in what has happened in the machine learning realm over the last week, see what we’ve gathered for you.

Weekly Roundup: September 1-7» 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.

🌎» The Top 5 AI and Machine Learning Trends to Watch Out For in 2021 by Devan Bansal on Techopedia | September 2The Top AI and Machine Learning Trends for 2️⃣0️⃣2️⃣1️⃣ include advancements in forecasting, healthcare, reinforcement learning, conversational AI, and predictive maint…

2 недели, 1 день назад @ neptune.ai
Top 12 Machine Learning Podcasts That You Want to Check as a Data Scientist
Top 12 Machine Learning Podcasts That You Want to Check as a Data Scientist Top 12 Machine Learning Podcasts That You Want to Check as a Data Scientist

Paperspace provides GPU-enabled compute resources to data scientists and machine learning engineers.

Dillion, explains how they build an organization which helps in building and scaling of machine learning workflows.

Quantum Machine Learningimage credits: plotplotQuantum machine learning is an intersection of Quantum Physics and Machine Learning.

Host: Katie MaloneGuest: Zach DrakeWell after all these podcasts you might have or certainly become somewhat interested in learning Data Science.

So I am listing out the resources where you can learn machine learning for free.

2 недели, 1 день назад @ neptune.ai
Machine Learning Trends to Watch Out in 2020 and 2021
Machine Learning Trends to Watch Out in 2020 and 2021 Machine Learning Trends to Watch Out in 2020 and 2021

People are experimenting with Machine Learning to get ahead… or simply keep up.

Top 5 trends in machine learning that you should look out for in 2020 and 20211.

Banking: Banks are using machine learning for customer services, investment modeling, risk prediction, risk prevention, and investments.

Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.” – WikipediaRL is very appealing because it feels like the learning we observe every day.

The idea that machines could think and perform tasks, just like we humans do, is not that far 🙂Gopal SinghPython & Machine Learning Instructor | Founder of probog.comYou liked it?

3 недели назад @ neptune.ai
This Week in Machine Learning: Human-Machine Interaction, AI Limitations, and Neuralink
This Week in Machine Learning: Human-Machine Interaction, AI Limitations, and Neuralink This Week in Machine Learning: Human-Machine Interaction, AI Limitations, and Neuralink

Machine learning and artificial intelligence help to discover new things, push the boundaries, and sometimes even save people’s lives.

Here are the best picks from the last week from the world of machine learning.

Machine learning and AI can help humans with disabilities.

» How Machine Learning Is Changing Medicine And Healthcare?

» Old but gold, the reliable Reddit thread on ML for more news on machine learning.

3 недели, 1 день назад @ neptune.ai
Implementing Content-Based Image Retrieval with Siamese Networks in PyTorch
Implementing Content-Based Image Retrieval with Siamese Networks in PyTorch Implementing Content-Based Image Retrieval with Siamese Networks in PyTorch

In this post we:explain the theoretical concepts behind content-based image retrieval,behind content-based image retrieval, show step by step how to build a content-based image retrieval system with PyTorch, addressing a specific application: Finding face images with a set of given face attributes (i.e.

Concepts explained that might be of interest: Ranking Loss, Contrastive Loss, Siamese Nets, Triplet Nets, Triplet Loss, Image Retrieval1.

Those networks are set up in a siamese fashion and trained with a ranking loss (triplet loss in our case).

Triplet Loss in PyTorchPyTorch provides an implementation of the Triplet Loss called Triplet Margin Loss which you can find here.

Use the trained mod…

3 недели, 2 дня назад @ neptune.ai
This Week in Machine Learning: ML Applications, Creative Revolution, and Fighting Pandemic
This Week in Machine Learning: ML Applications, Creative Revolution, and Fighting Pandemic This Week in Machine Learning: ML Applications, Creative Revolution, and Fighting Pandemic

Every week brings tons of news, opinions, and discoveries in the world of machine learning and AI.

But with our Weekly Roundup, you can easily catch up with the most important information and learn what’s happened in the industry.

Weekly Roundup: August 18-24» 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.

» Chatbots Are Machine Learning Their Way To Human Language by Adrian Bridgwater on Forbes | August 20Is machine learning going to reduce the language barrier between people and AI?

» Old but gold, the reliable Reddit thread on ML for more news on machine learning.

1 месяц назад @ neptune.ai
Hyperparameter Tuning in Python: a Complete Guide 2020
Hyperparameter Tuning in Python: a Complete Guide 2020 Hyperparameter Tuning in Python: a Complete Guide 2020

In this article, I will show you some of the best ways to do hyperparameter tuning that are available today (in 2020).

Hyperparameter tuning methodsIn this section, I will introduce all of the hyperparameter tuning methods that are popular today.

Hyperparameter tuning algorithmsThese are the algorithms developed specifically for doing hyperparameter tuning.

When you build a model for hyperparameter tuning, you also define the hyperparameter search space in addition to the model architecture.

Hyperparameter tuning resources and examplesIn this section, I will share some hyperparameter tuning examples implemented for different ML and DL frameworks.

1 месяц назад @ neptune.ai
▶️ YouTube
Henry AI Labs Henry AI Labs
последний пост 1 месяц, 1 неделя назад
Well-Read Students Learn Better
Well-Read Students Learn Better Well-Read Students Learn Better

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц, 1 неделя назад @ youtube.com
Easy Data Augmentation for Text Classification
Easy Data Augmentation for Text Classification Easy Data Augmentation for Text Classification

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц, 1 неделя 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц, 2 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц, 3 недели назад @ youtube.com
Momentum Predictive Representations Explained!
Momentum Predictive Representations Explained! Momentum Predictive Representations Explained!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 месяца назад @ youtube.com
Distribution Augmentation for Generative Modeling
Distribution Augmentation for Generative Modeling Distribution Augmentation for Generative Modeling

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 месяца назад @ youtube.com
Contrastive Clustering with SwAV
Contrastive Clustering with SwAV Contrastive Clustering with SwAV

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 месяца назад @ youtube.com
Don't Stop Pretraining!
Don't Stop Pretraining! Don't Stop Pretraining!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 месяца назад @ youtube.com
CheckList Explained! (ACL 2020 Best Paper)
CheckList Explained! (ACL 2020 Best Paper) CheckList Explained! (ACL 2020 Best Paper)

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 месяца назад @ youtube.com
Rethinking Pre-training and Self-Training
Rethinking Pre-training and Self-Training Rethinking Pre-training and Self-Training

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

3 месяца, 1 неделя назад @ youtube.com
ImageGPT (Generative Pre-training from Pixels)
ImageGPT (Generative Pre-training from Pixels) ImageGPT (Generative Pre-training from Pixels)

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

3 месяца, 1 неделя назад @ youtube.com
Training GANs with Limited Data
Training GANs with Limited Data Training GANs with Limited Data

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

3 месяца, 1 неделя назад @ youtube.com
On the Measure of Intelligence (Introduction)
On the Measure of Intelligence (Introduction) On the Measure of Intelligence (Introduction)

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

3 месяца, 1 неделя назад @ youtube.com
PEGASUS Explained!
PEGASUS Explained! PEGASUS Explained!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

3 месяца, 2 недели назад @ youtube.com
Funnel Transformer Explained!
Funnel Transformer Explained! Funnel Transformer Explained!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

3 месяца, 2 недели назад @ youtube.com
Machine Learning and AI Academy Machine Learning and AI Academy
последний пост 1 месяц, 4 недели назад
The Reparameterisation Trick|Variational Inference
The Reparameterisation Trick|Variational Inference The Reparameterisation Trick|Variational Inference

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц, 4 недели назад @ youtube.com
Wasserstein Robust RL
Wasserstein Robust RL Wasserstein Robust RL

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 месяца 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)!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 месяца назад @ youtube.com
Policy Gradients Reinforcement
Policy Gradients Reinforcement Policy Gradients Reinforcement

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 месяца 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

7 месяцев, 2 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)

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

8 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)

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

8 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

8 месяцев, 3 недели назад @ youtube.com
Introduction to Machine Learning|Short Introduction to Machine Learning|Machine Learning Basics
Introduction to Machine Learning|Short Introduction to Machine Learning|Machine Learning Basics Introduction to Machine Learning|Short Introduction to Machine Learning|Machine Learning Basics

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

8 месяцев, 3 недели назад @ youtube.com
Grow your Career with AI
Grow your Career with AI Grow your Career with AI

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

8 месяцев, 4 недели назад @ youtube.com
Introduction to Deep RL
Introduction to Deep RL Introduction to Deep RL

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

8 месяцев, 4 недели назад @ youtube.com
3blue1brown 3blue1brown
последний пост 2 недели, 5 дней 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…

2 недели, 5 дней 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…

2 недели, 5 дней 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц назад @ youtube.com
The impossible chessboard puzzle
The impossible chessboard puzzle The impossible chessboard puzzle

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 месяца, 2 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

4 месяца 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

4 месяца, 1 неделя 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)

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

4 месяца, 1 неделя 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

4 месяца, 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

4 месяца, 2 недели назад @ youtube.com
Logarithm Fundamentals | Lockdown math ep. 6
Logarithm Fundamentals | Lockdown math ep. 6 Logarithm Fundamentals | Lockdown math ep. 6

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

4 месяца, 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

4 месяца, 3 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

4 месяца, 4 недели назад @ youtube.com
Complex number fundamentals | Lockdown math ep. 3
Complex number fundamentals | Lockdown math ep. 3 Complex number fundamentals | Lockdown math ep. 3

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

5 месяцев назад @ youtube.com
Trigonometry fundamentals | Lockdown math ep. 2
Trigonometry fundamentals | Lockdown math ep. 2 Trigonometry fundamentals | Lockdown math ep. 2

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

5 месяцев 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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

5 месяцев, 1 неделя назад @ youtube.com
Two Minute Papers Two Minute Papers
последний пост 1 день, 17 часов назад
This AI Creates Real Scenes From Your Photos!
This AI Creates Real Scenes From Your Photos! This AI Creates Real Scenes From Your Photos!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 день, 17 часов 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!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

4 дня, 21 час назад @ youtube.com
Can An AI Generate Original Art? 👨‍🎨
Can An AI Generate Original Art? 👨‍🎨 Can An AI Generate Original Art? 👨‍🎨

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 неделя, 1 день назад @ youtube.com
Simulating a Rocket Launch! 🚀
Simulating a Rocket Launch! 🚀 Simulating a Rocket Launch! 🚀

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 неделя, 4 дня 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!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 недели, 1 день назад @ youtube.com
Can We Simulate Coalescing Bubbles? 🌊
Can We Simulate Coalescing Bubbles? 🌊 Can We Simulate Coalescing Bubbles? 🌊

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

2 недели, 4 дня назад @ youtube.com
OpenAI’s Image GPT Completes Your Images With Style!
OpenAI’s Image GPT Completes Your Images With Style! OpenAI’s Image GPT Completes Your Images With Style!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

Bitte verwende die offizielle YouTube App für Android oder iOS oder nutze YouTube Go.

3 недели, 1 день назад @ youtube.com
This AI Creates Images Of Nearly Any Animal! 🦉
This AI Creates Images Of Nearly Any Animal! 🦉 This AI Creates Images Of Nearly Any Animal! 🦉

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

Bitte verwende die offizielle YouTube App für Android oder iOS oder nutze YouTube Go.

3 недели, 3 дня назад @ youtube.com
TecoGAN: Super Resolution Extraordinaire!
TecoGAN: Super Resolution Extraordinaire! TecoGAN: Super Resolution Extraordinaire!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

Bitte verwende die offizielle YouTube App für Android oder iOS oder nutze YouTube Go.

4 недели, 1 день назад @ youtube.com
This AI Removes Shadows From Your Photos! 🌒
This AI Removes Shadows From Your Photos! 🌒 This AI Removes Shadows From Your Photos! 🌒

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц назад @ youtube.com
How Can We Simulate Water Droplets? 🌊
How Can We Simulate Water Droplets? 🌊 How Can We Simulate Water Droplets? 🌊

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц назад @ youtube.com
From Video Games To Reality…With Just One AI!
From Video Games To Reality…With Just One AI! From Video Games To Reality…With Just One AI!

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц, 1 неделя назад @ youtube.com
Can We Simulate Tearing Meat? 🥩
Can We Simulate Tearing Meat? 🥩 Can We Simulate Tearing Meat? 🥩

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц, 1 неделя назад @ youtube.com
OpenAI GPT-3 - Good At Almost Everything! 🤖
OpenAI GPT-3 - Good At Almost Everything! 🤖 OpenAI GPT-3 - Good At Almost Everything! 🤖

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц, 2 недели назад @ youtube.com
Physics in 4 Dimensions…How?
Physics in 4 Dimensions…How? Physics in 4 Dimensions…How?

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 месяц, 2 недели назад @ youtube.com
Primer Primer
последний пост 4 месяца, 1 неделя назад
Epidemic, Endemic, and Eradication Simulations
Epidemic, Endemic, and Eradication Simulations

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 1 неделя назад @ youtube.com
Simulating Foraging Decisions
Simulating Foraging Decisions

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

6 месяцев, 1 неделя назад @ youtube.com
DataFest Video DataFest Video
последний пост 2 месяца назад
Kaggle TReNDS Neuroimaging: predict multiple assessments and age from brain MRI - Nikita CHURKIN
Kaggle TReNDS Neuroimaging: predict multiple assessments and age from brain MRI - Nikita CHURKIN

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

2 месяца назад @ youtube.com
Управление проектами и продуктами в Data Science
Управление проектами и продуктами в Data Science

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 2 недели назад @ youtube.com
Почему вы никогда не найдете Дата Саентиста – Валерий Бабушкин
Почему вы никогда не найдете Дата Саентиста – Валерий Бабушкин

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Карьера в ИТ – Владимир Утратенко
Карьера в ИТ – Владимир Утратенко

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
DS, ML, AI – как профильная англоязычная магистратура может помочь карьере – Денис Столяров
DS, ML, AI – как профильная англоязычная магистратура может помочь карьере – Денис Столяров

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Data Science: чему и где учиться – Юрий Дорн
Data Science: чему и где учиться – Юрий Дорн

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Как скрестить аплифт деревья и RL для рекомендашки – Валерий Бабушкин
Как скрестить аплифт деревья и RL для рекомендашки – Валерий Бабушкин

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Топологический анализ временных рядов для прогнозирования спроса – Евгений Бурнаев
Топологический анализ временных рядов для прогнозирования спроса – Евгений Бурнаев

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Что понимаешь про карьеру в Data Science после десяти лет работы – Виктор Кантор
Что понимаешь про карьеру в Data Science после десяти лет работы – Виктор Кантор

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Как мы строим DevOps в видеоаналитике – Георг Гаал
Как мы строим DevOps в видеоаналитике – Георг Гаал

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Как перестать бояться и начать разрабатывать специализированные структуры данных – Алексей Миловидов
Как перестать бояться и начать разрабатывать специализированные структуры данных – Алексей Миловидов

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Гибкое управление DS проектами – Асхат Уразбаев
Гибкое управление DS проектами – Асхат Уразбаев

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Как тестировать DS-код – Алексей Могильников
Как тестировать DS-код – Алексей Могильников

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Каннибализация в ритейле и ecommerce – Иван Максимов
Каннибализация в ритейле и ecommerce – Иван Максимов

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Семинары JetBrains Research Семинары JetBrains Research
последний пост 18 часов назад
Поиск шаблонов изменений в Python и их автоматизация
Поиск шаблонов изменений в Python и их автоматизация Поиск шаблонов изменений в Python и их автоматизация

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

18 часов назад @ youtube.com
Построение векторного представления программного кода на основе AST
Построение векторного представления программного кода на основе AST Построение векторного представления программного кода на основе AST

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

3 дня, 21 час назад @ youtube.com
Построение векторных представлений для изменений программного кода
Построение векторных представлений для изменений программного кода Построение векторных представлений для изменений программного кода

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 неделя, 4 дня назад @ youtube.com
Агрегация биологических и химических данных в DL
Агрегация биологических и химических данных в DL Агрегация биологических и химических данных в DL

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 неделя, 5 дней назад @ youtube.com
Маршрутизирующие сети
Маршрутизирующие сети Маршрутизирующие сети

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

Bitte verwende die offizielle YouTube App für Android oder iOS oder nutze YouTube Go.

3 недели, 2 дня назад @ youtube.com
Neural Kernel Methods
Neural Kernel Methods

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

1 месяц назад @ youtube.com
The AI Economist
The AI Economist

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

1 месяц назад @ youtube.com
Применение тематического моделирования в SE
Применение тематического моделирования в SE

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

1 месяц, 2 недели назад @ youtube.com
Building Implicit Vector Representations of Individual Coding Style
Building Implicit Vector Representations of Individual Coding Style

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

1 месяц, 3 недели назад @ youtube.com
Finding Similar Code Repositories
Finding Similar Code Repositories

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

3 месяца, 1 неделя назад @ youtube.com
Self-Tuning Deep Reinforcement Learning
Self-Tuning Deep Reinforcement Learning

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

3 месяца, 2 недели назад @ youtube.com
Генерация синтетических healthcare данных
Генерация синтетических healthcare данных

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

3 месяца, 2 недели назад @ youtube.com
Zero Shot Learning for Code Education
Zero Shot Learning for Code Education

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца назад @ youtube.com
A Spatiotemporal Epidemic Model to Quantify the Effects of Contact Tracing, Testing, and Containment
A Spatiotemporal Epidemic Model to Quantify the Effects of Contact Tracing, Testing, and Containment

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 1 неделя назад @ youtube.com
Sample Efficiency in RL
Sample Efficiency in RL

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 1 неделя назад @ youtube.com
Яндекс. Компьютерные науки Яндекс. Компьютерные науки
последний пост 3 месяца, 3 недели назад
Программирование ретрокомпьютеров: сборка демо
Программирование ретрокомпьютеров: сборка демо

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

3 месяца, 3 недели назад @ youtube.com
Программирование ретрокомпьютеров: визуальные эффекты. Часть 4
Программирование ретрокомпьютеров: визуальные эффекты. Часть 4

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

3 месяца, 4 недели назад @ youtube.com
Программирование ретрокомпьютеров: визуальные эффекты. Часть 3
Программирование ретрокомпьютеров: визуальные эффекты. Часть 3

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца назад @ youtube.com
Программирование ретрокомпьютеров: визуальные эффекты. Часть 2
Программирование ретрокомпьютеров: визуальные эффекты. Часть 2

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 1 неделя назад @ youtube.com
Программирование ретрокомпьютеров: визуальные эффекты. Часть 1
Программирование ретрокомпьютеров: визуальные эффекты. Часть 1

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 2 недели назад @ youtube.com
Машинное обучение. Нейронные сети и градиентные методы. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Нейронные сети и градиентные методы. К.В. Воронцов, Школа анализа данных, Яндекс.

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 3 недели назад @ youtube.com
Машинное обучение. Заключительная лекция. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Заключительная лекция. К.В. Воронцов, Школа анализа данных, Яндекс.

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 3 недели назад @ youtube.com
Машинное обучение. Активное обучение. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Активное обучение. К.В. Воронцов, Школа анализа данных, Яндекс.

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 3 недели назад @ youtube.com
Машинное обучение. Обучение с подкреплением. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Обучение с подкреплением. К.В. Воронцов, Школа анализа данных, Яндекс.

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 3 недели назад @ youtube.com
Машинное обучение. Тематическое моделирование. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Тематическое моделирование. К.В. Воронцов, Школа анализа данных, Яндекс.

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 3 недели назад @ youtube.com
Машинное обучение. Рекомендательные системы. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Рекомендательные системы. К.В. Воронцов, Школа анализа данных, Яндекс.

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 3 недели назад @ youtube.com
Машинное обучение. Обучение ранжированию. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Обучение ранжированию. К.В. Воронцов, Школа анализа данных, Яндекс.

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 3 недели назад @ youtube.com
Машинное обучение. Композиции классификаторов, часть 2. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Композиции классификаторов, часть 2. К.В. Воронцов, Школа анализа данных, Яндекс.

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 3 недели назад @ youtube.com
Машинное обучение. Линейные композиции, бустинг. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Линейные композиции, бустинг. К.В. Воронцов, Школа анализа данных, Яндекс.

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 3 недели назад @ youtube.com
Машинное обучение. Нейронные сети глубокого обучения. К.В. Воронцов, Школа анализа данных, Яндекс.
Машинное обучение. Нейронные сети глубокого обучения. К.В. Воронцов, Школа анализа данных, Яндекс.

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 3 недели назад @ youtube.com
ML Trainings ML Trainings
последний пост 20 часов назад
Data Fest Online: highlights of the 1st day
Data Fest Online: highlights of the 1st day Data Fest Online: highlights of the 1st day

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

20 часов назад @ youtube.com
Как зарегистрироваться на Data Fest 2020
Как зарегистрироваться на Data Fest 2020 Как зарегистрироваться на Data Fest 2020

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

5 дней, 22 часа назад @ youtube.com
Data Fest 2020_Day 2
Data Fest 2020_Day 2 Data Fest 2020_Day 2

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 неделя назад @ youtube.com
Data Fest 2020
Data Fest 2020 Data Fest 2020

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

1 неделя назад @ youtube.com
Как задетектить Deepfake и выиграть $500k — Селим Сефербеков
Как задетектить Deepfake и выиграть $500k — Селим Сефербеков

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

2 месяца, 2 недели назад @ youtube.com
Прогнозирование распространения COVID-19 — Владислав Крамаренко
Прогнозирование распространения COVID-19 — Владислав Крамаренко

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

3 месяца, 2 недели назад @ youtube.com
Реальная жизнь и работа в Кремниевой долине — ODS Heroes
Реальная жизнь и работа в Кремниевой долине — ODS Heroes

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

4 месяца, 1 неделя назад @ youtube.com
Каким клиентам нужно отправить SMS — Дмитрий Свирчков
Каким клиентам нужно отправить SMS — Дмитрий Свирчков

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Публичные решения и щепотка соли: обзор третьего решения второй задачи — Михаил Трофимов
Публичные решения и щепотка соли: обзор третьего решения второй задачи — Михаил Трофимов

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Как расставить товары по полкам — Александр Желубенков
Как расставить товары по полкам — Александр Желубенков

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Какие товары клиент вероятно купит в следующий раз — Антон Протопопов
Какие товары клиент вероятно купит в следующий раз — Антон Протопопов

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 2 недели назад @ youtube.com
Повышаем качество вопросно-ответных систем (Kaggle Google QUEST Q&A Labeling) — Дмитрий Абулханов
Повышаем качество вопросно-ответных систем (Kaggle Google QUEST Q&A Labeling) — Дмитрий Абулханов

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

5 месяцев, 3 недели назад @ youtube.com
Конкурс по комбинаторной оптимизации (Kaggle Santa's Workshop Tour 2019) — Николай Попов
Конкурс по комбинаторной оптимизации (Kaggle Santa's Workshop Tour 2019) — Николай Попов

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

6 месяцев, 2 недели назад @ youtube.com
Как пережить шейкап (Kaggle 2019 Data Science Bowl) — Дмитрий Симаков, Сергей Фиронов
Как пережить шейкап (Kaggle 2019 Data Science Bowl) — Дмитрий Симаков, Сергей Фиронов

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

6 месяцев, 3 недели назад @ youtube.com
Ускорение МРТ нейросетями (FastMRI Challenge) — Сергей Кастрюлин
Ускорение МРТ нейросетями (FastMRI Challenge) — Сергей Кастрюлин

This page appears when Google automatically detects requests coming from your computer network which appear to be in violation of the Terms of Service .

The block will expire shortly after those requests stop.

In the meantime, solving the above CAPTCHA will let you continue to use our services.This traffic may have been sent by malicious software, a browser plug-in, or a script that sends automated requests.

If you share your network connection, ask your administrator for help — a different computer using the same IP address may be responsible.

Learn more Sometimes you may be asked to solve the CAPTCHA if you are using advanced terms that robots are known to use, or sending requests very qu…

6 месяцев, 3 недели назад @ youtube.com
🎧 Podcasts
Lex Fridman AI Podcast Lex Fridman AI Podcast
последний пост 3 дня, 14 часов назад
#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 …

3 дня, 14 часов 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.

On some podcast players you should be able to click the timestamp to jump to that time.

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 неделя, 1 день 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.

On some podcast players you should be able to click the timestamp to jump to that time.

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 неделя, 4 дня 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…

2 недели, 1 день 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.

On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:00:00 – In memory of my grandmother02…

2 недели, 2 дня 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.

On some podcast players you should be able to click the timestamp to jump to that time.

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

2 недели, 4 дня 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.

3 недели, 1 день назад @ lexfridman.com
#120 – François Chollet: Measures of Intelligence
#120 – François Chollet: Measures of Intelligence #120 – François Chollet: Measures of Intelligence

François Chollet is an AI researcher at Google and creator of Keras.

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 some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:00:00 – Introduction05:04 – Early influence06:23 – Language12:50 – Thinking with mind maps23:42 – Definition of intelligence42:24 – GPT-353:07 – Semantic web57:22 – Autonomous driving1:09:30 – Tests of intelligence1:13:59 – Tests of human intelligence1:27:18 – IQ tests1:35:59 – ARC Challenge1:59:11 – Generalization2:09:50 – Turing Test2:20:44 – Hutter prize2:27:44 – Meaning of life

3 недели, 3 дня назад @ lexfridman.com
#119 – David Eagleman: Neuroplasticity and the Livewired Brain
#119 – David Eagleman: Neuroplasticity and the Livewired Brain #119 – David Eagleman: Neuroplasticity and the Livewired Brain

David Eagleman is a neuroscientist at Stanford.

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 some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:00:00 – Introduction05:05 – Livewired16:39 – Hardware vs software25:53 – Brain-computer interfaces35:12 – 2020 is a challenge for neuroplasticity46:08 – Free will50:43 – Nature of evil58:55 – Psychiatry1:06:28 – GPT-31:13:31 – Intelligence in the brain1:21:51 – Neosensory1:31:27 – Book recommendations1:34:07 – Meaning of life1:36:53 – Advice for young people

4 недели назад @ lexfridman.com
New Name: Lex Fridman Podcast
New Name: Lex Fridman Podcast New Name: Lex Fridman Podcast

New podcast name.

AI is still my passion, but this gives me a bit more freedom to talk to interesting folks from all over.

Thanks for the support & the 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.

1 месяц назад @ lexfridman.com
#118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown
#118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown

Grant Sanderson is a math educator and creator of 3Blue1Brown.

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.

OUTLINE:00:00 – Introduction05:13 – Richard Feynman09:41 – Learning deeply vs broadly13:56 – Telling a story with visualizations18:43 – Topology23:52 – Intuition about exponential growth32:28 – Elon Musk’s exponential view of the world40:09 – SpaceX and space exploration45:28 – Origins of the Internet49:50 – Does teaching on YouTube get lonely?

54:31 – Daily routine1:00:20 – Social media1:10:38 – Online education in a time of COVID…

1 месяц назад @ lexfridman.com
#117 – Sheldon Solomon: Death and Meaning
#117 – Sheldon Solomon: Death and Meaning #117 – Sheldon Solomon: Death and Meaning

Sheldon Solomon is a social psychologist, a philosopher, co-developer of Terror Management Theory, co-author of The Worm at the Core.

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 some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:00:00 – Introduction05:34 – Role of death in life22:57 – Jordan Peterson53:02 – Humans are both selfish and cooperative56:57 – Civilization collapse1:10:07 – Meditating on your mortality1:16:10 – Kierkegaard and Heidegger1:33:25 – Elon Musk1:36:56 – Thinking deeply about death1:45:53 – Religion1:56:59 – Consciousness2:0…

1 месяц назад @ lexfridman.com
#116 – Sara Seager: Search for Planets and Life Outside Our Solar System
#116 – Sara Seager: Search for Planets and Life Outside Our Solar System #116 – Sara Seager: Search for Planets and Life Outside Our Solar System

Sara Seager is a planetary scientist at MIT, known for her work on the search for exoplanets.

Support this podcast by supporting our sponsors.

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.

OUTLINE:00:00 – Introduction05:32 – Falling in love with the stars09:55 – Are we alone in the universe?

1 месяц, 1 неделя назад @ lexfridman.com
#115 – Dileep George: Brain-Inspired AI
#115 – Dileep George: Brain-Inspired AI #115 – Dileep George: Brain-Inspired AI

Dileep George is a researcher at the intersection of neuroscience and artificial intelligence, co-founder of Vicarious, formerly co-founder of Numenta.

From the early work on Hierarchical temporal memory to Recursive Cortical Networks to today, Dileep’s always sought to engineer intelligence that is closely inspired by the human brain.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

OUTLINE:0:00 – Introduction4:50 – Building a model of the brain17:11 – Visual cortex27:50 – Probabilistic graphical models31:35 – Encoding information in the brain36:56 – Recursive Cortical Network51:09 – Solving CAPTCHAs algorithmically1:06:48 – H…

1 месяц, 1 неделя назад @ lexfridman.com
#114 – Russ Tedrake: Underactuated Robotics, Control, Dynamics and Touch
#114 – Russ Tedrake: Underactuated Robotics, Control, Dynamics and Touch #114 – Russ Tedrake: Underactuated Robotics, Control, Dynamics and Touch

Russ Tedrake is a roboticist and professor at MIT and vice president of robotics research at TRI.

He works on control of robots in interesting, complicated, underactuated, stochastic, difficult to model situations.

Support this podcast by supporting our sponsors.

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, 2 недели назад @ lexfridman.com
DeepMind: The Podcast DeepMind: The Podcast
последний пост 1 год назад
Microsoft Research Podcast Microsoft Research Podcast
последний пост 2 месяца, 2 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.

2 месяца, 2 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

3 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

3 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.

4 месяца 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…

4 месяца, 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

4 месяца, 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…

4 месяца, 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…

4 месяца, 4 недели 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…

5 месяцев 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.

5 месяцев, 1 неделя 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…

5 месяцев, 2 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

5 месяцев, 3 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

6 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

6 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…

6 месяцев, 2 недели назад @ blubrry.com
NLP Highlights NLP Highlights
последний пост 2 недели, 6 дней назад
119 - Social NLP, with Diyi Yang
119 - Social NLP, with Diyi Yang 119 - Social NLP, with Diyi Yang

JavaScript is disabledTo continue, go to Settings and turn it on

2 недели, 6 дней назад @ soundcloud.com
118 - Coreference Resolution, with Marta Recasens
118 - Coreference Resolution, with Marta Recasens 118 - Coreference Resolution, with Marta Recasens

JavaScript is disabledTo continue, go to Settings and turn it on

4 недели 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

JavaScript is disabledTo continue, go to Settings and turn it on

1 месяц, 1 неделя назад @ soundcloud.com
116 - Grounded Language Understanding, with Yonatan Bisk
116 - Grounded Language Understanding, with Yonatan Bisk 116 - Grounded Language Understanding, with Yonatan Bisk

JavaScript is disabledTo continue, go to Settings and turn it on

2 месяца, 3 недели назад @ soundcloud.com
115 - AllenNLP, interviewing Matt Gardner
115 - AllenNLP, interviewing Matt Gardner 115 - AllenNLP, interviewing Matt Gardner

JavaScript is disabledTo continue, go to Settings and turn it on

3 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

JavaScript is disabledTo continue, go to Settings and turn it on

4 месяца 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

JavaScript is disabledTo continue, go to Settings and turn it on

4 месяца 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

JavaScript is disabledTo continue, go to Settings and turn it on

4 месяца, 1 неделя 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

JavaScript is disabledTo continue, go to Settings and turn it on

4 месяца, 4 недели 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

JavaScript is disabledTo continue, go to Settings and turn it on

5 месяцев, 2 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

JavaScript is disabledTo continue, go to Settings and turn it on

5 месяцев, 3 недели 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

JavaScript is disabledTo continue, go to Settings and turn it on

6 месяцев 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

JavaScript is disabledTo continue, go to Settings and turn it on

7 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

JavaScript is disabledTo continue, go to Settings and turn it on

7 месяцев, 1 неделя назад @ soundcloud.com
105 - Question Generation, with Sudha Rao
105 - Question Generation, with Sudha Rao 105 - Question Generation, with Sudha Rao

JavaScript is disabledTo continue, go to Settings and turn it on

7 месяцев, 2 недели назад @ soundcloud.com
Data Skeptic
последний пост 2 дня, 22 часа назад
Crowdsourced Expertise
Crowdsourced Expertise Crowdsourced Expertise

Derek Lim joins us to discuss the paper Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform.

2 дня, 22 часа 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 неделя, 2 дня назад @ dataskeptic.com
Consensus Voting
Consensus Voting Consensus Voting

Consensus VotingMashbat Suzuki joins us to discuss the paper How Many Freemasons Are There?

The Consensus Voting Mechanism in Metric Spaces.

Check out Mashbat’s and many other great talks at the 13th Symposium on Algorithmic Game Theory (SAGT 2020)

2 недели, 2 дня назад @ dataskeptic.com
Voting Mechanisms
Voting Mechanisms Voting Mechanisms

Voting MechanismsSteven Heilman joins us to discuss his paper Designing Stable Elections.

3 недели, 2 дня 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.

1 месяц 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.

1 месяц, 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.

1 месяц, 1 неделя 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.

1 месяц, 4 недели 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.

2 месяца 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.

2 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.

2 месяца, 2 недели назад @ dataskeptic.com
Interpretability Practitioners
Interpretability Practitioners Interpretability Practitioners

Sungsoo Ray Hong joins us to discuss the paper Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs.

2 месяца, 4 недели назад @ dataskeptic.com
Facial Recognition Auditing
Facial Recognition Auditing Facial Recognition Auditing

Deb Raji joins us to discuss her recent publication Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing.

3 месяца назад @ dataskeptic.com
Robust Fit to Nature
Robust Fit to Nature Robust Fit to Nature

Uri Hasson joins us this week to discuss the paper Robust-fit to Nature: An Evolutionary Perspective on Biological (and Artificial) Neural Networks.

3 месяца, 1 неделя назад @ dataskeptic.com
Black Boxes Are Not Required
Black Boxes Are Not Required Black Boxes Are Not Required

Black Boxes are not RequiredDeep neural networks are undeniably effective.

They rely on such a high number of parameters, that htey are appropriately described as “black boxes”.

While black boxes lack desirably properties like interpretability and explainability, in some cases, their accuracy makes them incredibly useful.

But does achiving “usefulness” require a black box?

We discuss her recent paper with co-author Joanna Radin Why Are We Using Black Box Models in AI When We Don’t Need To?

3 месяца, 2 недели назад @ dataskeptic.com
Linear Digressions Linear Digressions
последний пост 1 месяц, 4 недели назад
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!—Katie and Ben

1 месяц, 4 недели 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…

2 месяца назад @ lineardigressions.com
A Data Science Take on Open Policing Data
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:

2 месяца, 1 неделя назад @ lineardigressions.com
Procella: YouTube's super-system for analytics data storage
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 thi…

2 месяца, 2 недели назад @ lineardigressions.com
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:Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community

2 месяца, 3 недели назад @ lineardigressions.com
Rock the ROC Curve
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.

3 месяца назад @ lineardigressions.com
Criminology and data science
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 …

3 месяца, 1 неделя назад @ lineardigressions.com
Racism, the criminal justice system, and data science
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.

We dig into this algorithm a little more deeply, unpacking how different metrics give different pictures into the “fairness” of the predictions and what is causing its racially disparate output (to wit: race is explicitly not an input to the algorithm, and yet the algorithm gives outputs that correlate with race—what gives?)

Unfortunately it’s not an open-and-shut case of a tuning parameter being off, or the wrong metric being used: instea…

3 месяца, 2 недели назад @ lineardigressions.com
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.

3 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:

3 месяца, 3 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:

4 месяца назад @ lineardigressions.com
Protecting Individual-Level Census Data with Differential Privacy
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.

4 месяца, 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.

4 месяца, 2 недели назад @ lineardigressions.com
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:A La…

4 месяца, 3 недели назад @ lineardigressions.com
Gaussian Processes
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?

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 gets complex, and the links below contain some excellent visualizations…

5 месяцев назад @ lineardigressions.com
SuperDataScience SuperDataScience
последний пост 13 часов назад
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

JavaScript is disabledTo continue, go to Settings and turn it on

13 часов назад @ soundcloud.com
SDS 402: Face Your Demons
SDS 402: Face Your Demons SDS 402: Face Your Demons

JavaScript is disabledTo continue, go to Settings and turn it on

6 дней 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

JavaScript is disabledTo continue, go to Settings and turn it on

1 неделя назад @ soundcloud.com
SDS 400: Think Bigger
SDS 400: Think Bigger SDS 400: Think Bigger

JavaScript is disabledTo continue, go to Settings and turn it on

1 неделя, 6 дней 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

JavaScript is disabledTo continue, go to Settings and turn it on

2 недели назад @ soundcloud.com
SDS 398: Emotional Burnout
SDS 398: Emotional Burnout SDS 398: Emotional Burnout

JavaScript is disabledTo continue, go to Settings and turn it on

2 недели, 6 дней назад @ soundcloud.com
SDS 397: The Importance of Data Science Literacy
SDS 397: The Importance of Data Science Literacy SDS 397: The Importance of Data Science Literacy

JavaScript is disabledTo continue, go to Settings and turn it on

3 недели назад @ soundcloud.com
SDS 396: Five Job Hunting Tips
SDS 396: Five Job Hunting Tips SDS 396: Five Job Hunting Tips

JavaScript is disabledTo continue, go to Settings and turn it on

3 недели, 6 дней назад @ soundcloud.com
SDS 395: How to Tell Stories with Data
SDS 395: How to Tell Stories with Data SDS 395: How to Tell Stories with Data

JavaScript is disabledTo continue, go to Settings and turn it on

4 недели назад @ soundcloud.com
SDS 394: Teach It
SDS 394: Teach It SDS 394: Teach It

JavaScript is disabledTo continue, go to Settings and turn it on

1 месяц назад @ soundcloud.com
SDS 393: The Importance of Keeping Science in Data Science
SDS 393: The Importance of Keeping Science in Data Science SDS 393: The Importance of Keeping Science in Data Science

JavaScript is disabledTo continue, go to Settings and turn it on

1 месяц назад @ soundcloud.com
SDS 392: Start Your Own Morning Ritual
SDS 392: Start Your Own Morning Ritual SDS 392: Start Your Own Morning Ritual

JavaScript is disabledTo continue, go to Settings and turn it on

1 месяц, 1 неделя назад @ soundcloud.com
SDS 391: Data Science Campfire Tales with John Elder
SDS 391: Data Science Campfire Tales with John Elder SDS 391: Data Science Campfire Tales with John Elder

JavaScript is disabledTo continue, go to Settings and turn it on

1 месяц, 1 неделя назад @ soundcloud.com
SDS 390: Perception vs. Emotion
SDS 390: Perception vs. Emotion SDS 390: Perception vs. Emotion

JavaScript is disabledTo continue, go to Settings and turn it on

1 месяц, 2 недели назад @ soundcloud.com
SDS 389: Becoming Good Enough: Jumpstarting Your Data Science Career
SDS 389: Becoming Good Enough: Jumpstarting Your Data Science Career SDS 389: Becoming Good Enough: Jumpstarting Your Data Science Career

JavaScript is disabledTo continue, go to Settings and turn it on

1 месяц, 2 недели назад @ soundcloud.com
Data Science at Home Data Science at Home
последний пост 1 неделя, 1 день назад
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 неделя, 1 день назад @ datascienceathome.com
Testing in machine learning: checking deep learning models (Ep. 118)
Testing in machine learning: checking deep learning models (Ep. 118) Testing in machine learning: checking deep learning models (Ep. 118)

September 4, 2020 podcastIn this episode I speak with Adam Leon Smith, CTO at DragonFly and expert in testing strategies for software and machine learning.

We cover testing with deep learning (neuron coverage, threshold coverage, sign change coverage, layer coverage, etc.

On September 15th there will be a live@Manning Rust conference.

In one Rust-full day you will attend many talks about what’s special about rust, building high performance web services or video game, about web assembly and much more.

If you want to meet the tribe, tune in september 15th to the live@manning rust conference.

2 недели, 6 дней назад @ datascienceathome.com
Testing in machine learning: generating tests and data (Ep. 117)
Testing in machine learning: generating tests and data (Ep. 117) Testing in machine learning: generating tests and data (Ep. 117)

August 29, 2020 podcastIn this episode I speak with Adam Leon Smith, CTO at DragonFly and expert in testing strategies for software and machine learning.

On September 15th there will be a live@Manning Rust conference.

In one Rust-full day you will attend many talks about what’s special about rust, building high performance web services or video game, about web assembly and much more.

If you want to meet the tribe, tune in September 15th to the live@manning Rust conference.

3 недели, 4 дня 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

1 месяц, 1 неделя 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.

1 месяц, 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…

2 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.

2 месяца 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.

2 месяца 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.

2 месяца, 2 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.

2 месяца, 3 недели назад @ datascienceathome.com