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I have tried searching online but to no avail, so I'm hoping reddit and people with more knowledge than myself may help.

First, currently I am trying to predict a multiclass output of that can be one of 4 different options.

Currently my y variable is structured as factors.

I did not have this issue prior with an output of 3 possible classifications (different model).

Second, I have come across Matthews Correlation Coefficient as another means of model evaluation outside of such things as: accuracy, rmse, f1score, logos, etc.

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Sorry, this is my first neural network code, I'm using 1000 images mixed with cats and dogs.

I'm trying to build a model to classify whether the image is a cat or dog but my accuracy keeps increasing or decreasing after every epoch and its usually around 40-60%.

I tried different batch size but it's about the same.

I tried removing/adding some layers but it didn't work out.

Codeimport sys import os import cv2 import random import numpy as np import timefrom tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Densefrom tensorflow.keras.callbacks import TensorBoardNAME = f'cat-dog-prediction-{int(time.time())}'tensorboard =TensorBoard(lo…

Transformers were designed to handle sequential data, like natural language, and have successfully been applied to other domains as well.

But how do they work?

If you're learning about them for the first time or just want a refresher, I cover the basics in this video: https://youtu.be/XSSTuhyAmnI

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In this case, I will use pip to install lux.

You can use any data you like, but now I will use data from the lux library.

You will also see the button that will redirect you to the visualization from lux.

The tabs areThe correlation tab gives you charts like scatter plot that visualizes the relationship between 2 variables.

The enhance tab gives you the recommendation to enhance the visualization style.

The 3 most important Project Management Methods in Data SciencePhoto by Ken Cheung on UnsplashWithin a Big Data project, topics such as building a Data Warehouse or Datalake, data integration, implementation of a BI tool or an AI/DL model often occur.

Several project management approaches and tools are available for the course of a project.

Through these important questions the business process comes to light and so technical conditions can be derived.

ConclusionIn this article, the three most commonly known project management approaches were mentioned and further described.

In the field of data science, agile approaches are promising because it is often impossible to assess in advance, whe…

For these reasons, segmentation is predominantly employed as a pre-processing step to annotate, enhance, analyse, classify, categorise, extract and abstract information from point cloud data.

Step 1: The (point cloud) data, always the data 😁In previous tutorials, I illustrated point cloud processing and meshing over a 3D dataset obtained by using photogrammetry and aerial LiDAR from Open Topography.

You know how to segment your point cloud in an inlier point set and an outlier point set 🥳!

The method cluster_dbscan acts on the pcd point cloud entity directly and returns a list of labels following the initial indexing of the point cloud.

We will especially look into how to manage big point c…

released a paper, “Focal Loss for Dense Object Detection” which introduced a detector called the RetinaNet.

Before diving into the nitty-gritty of RetinaNet, I will discuss the concept of Focal Loss.

3 — Focal Loss (Image by author)If you notice, the negation and the log term makes up the Cross-Entropy Loss and γ represents the tunable parameter.

The Focal Loss formula now becomes:Fig.4 — Modified Focal Loss (Image by author)The authors have noted(through experiments) that the Focal Loss form doesn’t need to be exact.

The multi-task loss function in RetinaNet is made up of the modified focal loss for classification and a smooth L1 loss calculated upon 4×A channelled vector yielded by the Re…

Neural Language ModelsPhoto by Markus Spiske on UnsplashIn NLP, a language model is a probability distribution over sequences on an alphabet of tokens.

A central problem in language modeling is to learn a language model from examples, such as a model of English sentences from a training set of sentences.

Detect and correct spelling errorsTranslate languages: To translate text from one language to another, it helps to have language models for the languages.

Early Neural Language ModelOne of the early neural language models, as described in [3], is feedforward in nature.

Structurally, this is analogous to the feedforward neural language model described earlier.

the creation of inaccurate models due to unclear data structure, outliers in data, skew in data;creating accurate models based on incorrect data;selecting the wrong attributes for the model;inefficient use of resources.

The purpose of Exploratory Data Analysis is to get acquainted with the data: to understand the data structure, to check missed values, to check anomalies in the data, to form hypotheses about the population, to define and clarify the choice of variable characteristics that will be used for machine learning, etc.

The data analysis is valuable because it allows you to be more confident that future results will be reliable, correctly interpreted and applied in the desired busin…

The first thing to do is to create a Google Sheet.

For this example, it will contain just 2 columns, one of which (the Age) has one missing value.

We can now move to a Python terminal like Google Colaboratory and use Pandas library to get the contents of the sheet.

In this simple way, we can connect to a Google Sheet directly from Python without using particular API integration.

Obviously, the URL generated by Google Sheet makes the Sheet public, so be careful when you give it to anybody.

Dirty Secrets of BookCorpus, a Key Dataset in Machine LearningPhoto by Javier Quiroga on UnsplashBookCorpus has helped train at least thirty influential language models (including Google’s BERT, OpenAI’s GPT, and Amazon’s Bort), according to HuggingFace.

The exact breakdown is as follows:4,255 books occurred once (i.e.

While we do not yet have the appropriate metadata to fully analyze religious representation in BookCorpus, we did find that BookCorpusOpen and Smashwords21 exhibit skews, suggesting that this could also be an issue in the original BookCorpus dataset.

Again, we do not yet have all the metadata we would need for a complete analysis of BookCorpus, but we can make estimates based…

Demonstrating that you have the skills necessary to do the jobBootcamps and online courses aren’t sufficient to demonstrate that you have the skills necessary to become a data scientist.

There are a lot more skills you need to learn before you can become a data scientist (even at an entry level).

ProjectsYou are interested in working as a data scientist for company A.

Their job listing has a requirement that says:Looking for someone skilled in NLP and in building sentiment analysis models.

Then, build a sentiment analysis model with this data, and showcase it on your portfolio.

How to get notified when your model is done training with knockknock.

In this article, I will demonstrate how you can use knockknock to receive model training updates on a wide range of platforms in only a few lines of code!

I won’t go into detail about the architecture of the neural network in the code above because the focus of this tutorial is on sending model training notifications.

This is a great feature if your data science team has a Slack workspace and wants to monitor your model training jobs.

SummaryKnockknock is a useful tool that lets you keep track of your model training jobs with notifications.

Have you ever wished to get into the minds of a hiring manager?

I know you said yes to at least one of the questions, if not all three.

When I felt I had learned enough data science and built a portfolio, I started applying for jobs online.

It worked better, but I had no idea what the hiring manager was looking for when I walked into the interview.

In this article, I’ll clear all the myths and doubts in your mind, and when we are done, you’ll have a clear strategy to attack your data science job search.

For a sequential dataset where the size of data points could be different, we used zero-padding to make all the data points of the same size.

Note — It is always preferred to have different sets of data points in a batch for different epochs i.e.

if in the first epoch a batch passes (data 1, data 2, data 3, data 4 ), in other epochs we should make sure to not provide the same set (data 1, data 2, data 3, data 4) together.

This is to ensure that our model does not learn the pattern/sequence in which the data points are provided.

So when we sequentially start picking from the bins we get different data each time.

Weights in the final layer of common visual models appear as horizontal bands. We investigate how and why.

When a neural network layer is divided into multiple branches, neurons self-organize into coherent groupings.

We report the existence of multimodal neurons in artificial neural networks, similar to those found in the human brain.

Neural Cellular Automata learn to generate textures, exhibiting surprising properties.

Neural Cellular Automata learn to generate textures, exhibiting surprising properties.

We present techniques for visualizing, contextualizing, and understanding neural network weights.

Reverse engineering the curve detection algorithm from InceptionV1 and reimplementing it from scratch.

A family of early-vision filters reacting to contrasts between spatial gratings of different frequency

Neural networks naturally learn many transformed copies of the same feature, connected by symmetric weights.

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

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

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

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

In a 2016 essay, Nadia Eghbal quotes Karl Fogel, an early open source advocate:In open source, you can only have “my” in the associative sense.

Domain-specific licensing for machine learningThis domain-specific approach is particularly promising for machine learning, in part because of several key areas where machine learning diverges heavily from other software.

Author Bio: Chris is an open source developer and contributor to the Rust-ML machine learning working group.

CitationFor attribution in academic contexts or books, please cite this work asChristopher Moran, "Machine Learning, Ethics, and Open Source Licensing (Part II/II)", The Gradient, 2021.

BibTeX citation:@article{moranopensour…

Three years ago today, the Gradient published its first set of articles.

If you’ve ever been itching to write something with the Gradient, now is your chance!

2 - The Gradient Newsletter and PodcastThe Gradient is launching a new weekly newsletter on Substack: the Update, starting next Saturday.

We are also launching the Gradient Podcast, which will go live in two weeks.

If you enjoy our articles, please consider signing up as a supporter on Patreon and/or directly donating to The Gradient and sharing our newsletter with friends!

The use and misuse of machine learning systemsBefore discussing moderation, it is worth first asking if machine learning merits additional consideration about its usage when compared to other forms of software.

Licensing requirements for the developers of government-purchased machine learning services could help fill this gap.

Author Bio: Chris is an open source developer and contributor to the Rust-ML machine learning working group.

CitationFor attribution in academic contexts or books, please cite this work asChristopher Moran, "Machine Learning, Ethics, and Open Source Licensing (Part I/II)", The Gradient, 2021.

BibTeX citation:@article{moranopensource2021,author = {Moran, Christopher},t…

The resulting network with a soft-attention mechanism is trained with back-propagation since the attention mechanism is differentiable.

Later in 2017, we saw a breakthrough in the NLP field, by constructing a network just using an attention mechanism.

Some more works in this early area of attention mechanism in ML can be found in and .

I will explain another attention mechanism approach in computer vision, but the mainstream attention mechanism (which is very popular nowadays) first appeared in this paper.

CitationFor attribution in academic contexts or books, please cite this work asEkrem Aksoy, "Attention in the Human Brain and Its Applications in ML", The Gradient, 2021.

Therefore, taking the full advantage of distributed on-device data for machine learning applications is the current direction in distributed learning.

If Federated Learning methods can find a solution for this complexity, it can be the future of distributed learning.

To understand why we need Federated Learning in the first place, we need to answer what it can enable.

When the models are trained on the users’ data, users enjoy the personalization and higher accuracy results.

CitationFor attribution in academic contexts or books, please cite this work asZehra Hayirci, "Decentralized AI For Healthcare", The Gradient, 2021.

Normal players are represented by green faces, toxic players by red faces.

On the other hand, the detector with the high threshold does not have this problem, but misses a lot of toxic players (false negatives).

Below you can compare the previous word list-based approach against three neural networks with different thresholds [5].

And say we have a system that can perfectly detect bad behavior in online conversations, what should be done when it detects somebody?

CitationFor attribution in academic contexts or books, please cite this work asWessel Stoop, Florian Kunneman, Antal van den Bosch, Ben Miller, "Catching Cyberbullies With Neural Networks", The Gradient, 2021.

But it seemed to me that this moonshot shone a light on a couple of traps that often catch those who aim AI at justice and individual rights.

But justice, which a lawyer might call the correct application of the law to facts, is more than lie detection.

A second concern lies with the “acts of faith” that almost always underpin applications of AI to human behaviour.

CitationFor attribution in academic contexts or books, please cite this work asPhil Lindan, "Can AI Let Justice Be Done?

BibTeX citation:@article{lindan2021roboticjudges,author = {Lindan, Phil},title = {Can AI Let Justice Be Done?

Vanilla Gradient Ascent [2013](Vanilla) gradient ascent was presented in the Visualizing Image Classification Models and Saliency Maps [2013] paper.

Here’s how Guided Back-Propagation looks next to SmoothGrad:Standard Guided Back-Propagation (left) vs. SmoothGrad (right) on an image of a doberman.

Blur Integrated Gradients [2020]That’s what our final interpretation method, blur integrated gradients seeks to do.

The blur integrated gradients method works by measuring gradients along a series of increasingly blurry versions of the original input image (rather than dimmed versions of the image, as integrated gradients does).

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

SourceOur purpose here is not to provide an explanation of the quantum eccentricities that occur underneath the hood of a quantum computer.

SourceVarious other algorithms under the umbrella of quantum machine learning have been formulated in the past few years as well.

This results in a model of computation that is closer to the one abstractly modeled by a quantum Turing machine.

Shifting gears back to our original discussion of Turing machines, a quantum Turing machine is the generalization or quantization of the classical Turing machine, where the head and tape are superposed.

Furthermore, the impressive trainability and dimensionality of quantum neural networks provide exciting new avenu…

2018; Geva, Goldberg, and Berant 2019), and BERT does exploit them (T. McCoy, Pavlick, and Linzen 2019; Jin et al.

The reason for that appears to be that the importance scores for most BERT heads are equally low.

If the success of BERT subnetworks is attributable to the linguistic knowledge they encode, the “super-survivors” should contain considerably more of it.

2020) confirm that the Lottery Ticket Hypothesis holds when using magnitude pruning on BERT: “good” subnetworks can be retrained to reach full model performance.

Thus it could be said that with structured pruning BERT has no “losing” tickets, even if it does not fully “win”.

Dr. Timnit Gebru is one of those few.

She is one of the founders of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), one of the most prestigious and well-known conferences related to machine learning ethics.

The entire team of FAccT founders, including Gebru, did a great job with this and helped change the field of machine learning in the process.

In response, she founded Black in AI, and over 500 Black machine learning researchers participated in the Black in AI workshop at NeurIPS 2017, just one year later.

I stand by you, Timnit.” I hope that we can all stand with Dr. Timnit Gebru now.

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

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

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

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

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

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

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

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

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

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

What can we do to improve peer review?

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

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

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

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

In Edge#85 , we pr…we do the quiz!

we explore Uber Fiber, a distributed computing framework optimized for reinforcement learning agents.

we discuss how Google Dreamer uses model-based RL to learn long-horizon tasks;we explain what model-based reinforcement learning (RL) is;In this issue:✖ CloseThis site uses cookies.

To find out more, read our privacy policy

📝 EditorialIn December 2020 we wrote about the frenzy of AI acquisitions by the large tech firms that made it a bit difficult for AI startups to achieve maturity.

The current AI/ML startup landscape is really fragmented, looking more like a set of scattered puzzles that are not necessarily combinable.

Recently, TheSequence joined the AI Infrastructure Alliance (AIIA) whose mission is to align AI/ML startups and community members to make it more like Lego blocks that can be stacked together.

One of the missions of the AIIA is to define and frame the key components of the AI/ML canonical stack (like the LAMP stack for software development if you know what we mean ;).

Despite the innovation de…

What’s New in AI, a deep dive into one of the freshest research papers or technology frameworks that is worth your attention.

Our goal is to keep you up to date with new developments in AI in a way that complements the concepts we are debating in other editions of our newsletter.

Give a gift subscription💥 What’s New in AI: How DeepMind Prevents RL Agents from Getting “Too Clev…

we explain what reinforcement learning (RL) is;we discuss how OpenAI trained reinforcement learning agents to play hide-and-seek but they learned so much more;

📝 EditorialSelf-supervised learning (SSL) is one of the most fascinating and exciting new areas of research in deep learning systems.

Labeled by many experts as crucial to the future of deep learning, SSL is one of the disciplines that comes the closest to simulate human learning.

Conceptually, SSL allows deep learning models to efficiently learn from unlabeled examples and build some general representations of an environment.

Just this week, Facebook open-sourced DINO and PAWS – two SSL models that are able to train transformer architectures in computer vision.

Before long, we should have SSL frameworks readily available in mainstream deep learning platforms.

What’s New in AI, a deep dive into one of the freshest research papers or technology frameworks that are worth your attention.

Our goal is to keep you up to date with new developments in AI in a way that complements the concepts we are debating in other editions of our newsletter.

Share💥 What’s New in AI: Snorkel Flow is One of the Most Comprehensive Machine…

Last year, Snorkel AI introduced Snorkel Flow, one of the most comprehensive machine learning platforms that uniquely supports programmatic labeling.

To address these challenges, Snorkel AI has developed Snorkel Flow, an AI platform focused on enabling programmatic labeling, streamlining the lifecycle of machine learning applications.

Training Data Creation with Snorkel Flow / Image credit: Snorkel AI2) Manage:Snorkel Flow automatically learns the different labeling functions’ accuracies, denoises and integrates them, and stores versioned LF packages and training data.

Unlike with hand-labeled data, data scientists can create training data in Snorkel Flow using code, then audit, modify, and…

William Falcon (WF): I'm the co-founder of Grid.ai and the creator of PyTorch Lightning.

Got involved in deep learning as an undergrad studying how the brain and eyes encode light into neural activity (computational neuroscience).

ML infrastructure platforms are advancing at a very rapid pace and the space is incredibly fragmented.

In fact, I think we did our job right if the term MLOps disappears.

(WF): Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron CourvilleProbabilistic Machine Learning: An Introduction (by Kevin Murphy)Is P equals NP?

In this issue:we explain what one-shot learning is;we explore Siamese Neural Networks – an architecture for one-shot learning models;we discuss ONNX as a key framework for machine learning interoperability.

Share💡 ML Concept of the Day: What is One-Shot Learning?

In the final issue of our series about N-shot learning methods (Edge#75 about N-Shot Learning…

📝 EditorialReinforcement Learning (RL) has been at the center of some of the most important milestones of the last decade of deep learning.

There is something seductive about the idea of learning by trial and error that shares some resemblance with human intelligence.

AI technology incumbents like Microsoft, Amazon, and Google have made RL a centerpiece of their machine learning product strategy.

RL might have been at the forefront of some of the most important recent milestones in deep learning but the race is just starting.

The platform, built on unsupervised learning for analytics, helps organizations turn the complexity of data into business insights.

What’s New in AI, a deep dive into one of the freshest research papers or technology frameworks that are worth your attention.

Our goal is to keep you up to date with new developments in AI in a way that complements the concepts we are debating in other editions of our newsletter.

🛠 ML WorkYou are famous within the AI community for spearheading the Keras framework that simplifies the implementation of machine learning programs.

Since then, TensorFlow 2.0 has taken steps to abstract many of the building blocks for machine learning applications.

In your mind, how much simpler can machine learning programming can get and what do we need to get there?

In that work, you challenge many of the established methods by which we measure knowledge in machine learning systems.

And if you want to compare the intelligence of an AI system with human intelligence, the only realistic option is to standardize on innate human knowledge priors.

In this issue of our series about N-shot Learning methods ( Edge#…we explore the Hugging Face library that includes an awesome pipeline for Zero-Shot classification.

we explain LASER – Facebook uses Zero-Shot Learning to Master NLU Tasks Across 93 Languages;In this issue:✖ CloseThis site uses cookies.

To find out more, read our privacy policy

Nvidia remains the undisputed leader in AI-first chips, but its footprint in the AI industry is rapidly expanding beyond that domain.

During the GTC conference, Nvidia announced new products across a wide spectrum of AI hardware and software categories, which makes it extremely clear that the chip giant is adopting a holistic view of the AI ecosystem.

The trend of machine learning platforms optimized for Nvidia hardware architectures is not new but it's certainly expanding rapidly.

All things considered, Nvidia can be considered one of the companies with the most solid network effects in the entire AI space.

💬 Useful TweetMost promising AI companies, according to CBInsightsFollow us on Twit…

We’re excited to partner with Tecton on apply(), the first community conference for ML data engineering.

This is a free, online event where users and thought leaders will share their experiences and best practices on ML data engineering.

Speakers include practitioners from Google, Microsoft, LinkedIn, Netflix, DoorDash, Spotify, Pinterest, Snorkel, Fiddler, Provectus, Algorithmia, Confluent, Stitch Fix, and more.

Hien Luu and Arbaz Khan from DoorDash will share their journey at the session: Scaling Online ML Predictions to Meet DoorDash Logistics Engine and Marketplace Growth.

Aakash Sabharwal and Sheila Hu from Etsy unfold the way towards a unified real-time ML data pipeline.

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В этом году открытая платформа для обработки естественного языка DeepPavlov, разрабатываемая лабораторией нейронных систем и глубокого обучения МФТИ, впервые стала частью ежегодной программы для молодых разработчиков Google Summer of Code.Google Summer of Code (GSoC) — это ежегодное событие, проводимое компанией Google для привлечения молодых разработчиков к разработке проектов с открытым исходным кодом в их свободное летнее время. К участию допускаются студенты высших учебных заведений (бакалавриат, магистратура, аспирантура) и колледжей. Это отличная возможность не только развить навыки программирования, но и заработать!Работать можно в любой организации, которая есть в соответствующем сп…

В последнее время все больше людей приходит к тому, чтобы не держать деньги под матрасом, а куда-то их инвестировать в надежде сохранить и преумножить свой капитал. Вариант с матрасом плох тем, что с повышением цен на товары и услуги(инфляция) покупательная способность денег падает и через какое-то время купить на них можно значительно меньше, чем раньше. Есть много вариантов, куда вложить деньги(недвижимость, банковский вклад, ценные металлы), но в последнее время популярным становится инвестирование в акции. Только у брокера Тинькофф Инвестиции за несколько лет число клиентов превысило 3.5 млн. В статье я постараюсь описать свой подход к выбору бумаг и поделюсь инструментами, которые для …

Нынче никого не удивишь достижениями искусственного интеллекта машинного обучения (ML) в самых разных областях. При этом доверчивые граждане редко задают два вопроса: (i) а какая собственно цена экспериментов и финальной системы и (ii) имеет ли сделанное хоть какую-то целесообразность? Самым важным компонентом такой цены являются как ни странно цена на железо и зарплаты людей. В случае если это все крутится в облаке, нужно еще умножать стоимость железа в 2-3 раза (маржа посредника).

И тут мы неизбежно приходим к тому, что несмотря на то, что теперь даже в официальные билды PyTorch добавляют бета-поддержку ROCm, Nvidia де-факто в этом цикле обновления железа (и скорее всего следующем) остает…

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:1. A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer (Tampere University, Finland, 2020)2. Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars (Samsung AI Center, 2020)3. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (University of California, USA, 2019)4. Whitening for Self-Supervised Representation Learning (University of Trento, Italy, 2020)5. MelGAN: Generative Adversarial Networks for…

Всем привет! Пока киберпанк еще не настолько вошел в нашу жизнь, и нейроинтерфейсы далеки от идеала, первым этапом на пути к будущему манипуляторов могут стать LiDAR. Поэтому, чтобы не скучать на праздниках, я решил немного пофантазировать на тему средств управления компьютером и, предположительно, любым устройством, вплоть до экскаватора, космического корабля, дрона или кухонной плиты. Читать дальше →

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

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

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

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

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

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

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

GShard: Scaling Giant Mo…

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 →

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…

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…

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…

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As an individual contributor, I was focused on the technical work of building, deploying, and operating machine learning models in production settings.

When I was promoted to Director, my focus began to shift away from purely technical aspects of ML.

Useful if you’re thinking through what metrics you should monitor for your production ML systems.

This brief post lists several key engineering features needed and five ways to implement A/B testing.

Here’s another engineering-style link demonstrating how to implementing A/B testing with Kubernetes and Istio.)

Providing technical advice is one area where there’s room for lots of technical work as a manager.

Rather than think about cleaning as a separate step, we’d do better to acknowledge that data cleaning IS data analysis.

Andrew Ng: Bridging AI’s Proof-of-Concept to Production Gap – Andrew Ng recently gave a talk on the common roadblocks that prevent AI projects from succeeding in production.

WhyLogs: Embrace Data Logging Across Your ML Systems – Last week I wrote about a critical bug in one of our production applications that could have been diagnosed with better monitoring and logging.

According to the post WhyLogs logs properties of data as it moves through an ML system, aggregates logs, su…

This week a data scientist on my team discovered a pretty bad bug in one of our production applications.

Although these are thought of as "software topics", data scientists and ML engineers need to care about them.

Maybe you have a highly specialized team of software engineers that help you build applications, but maybe you don’t.

Bringing an AI Product to Market – This is the third post in O’Reilly’s series on AI Product Management, and discusses how to bring an AI-powered product to market.

AI Product Management After Deployment – O’Reilly’s series on AI Product Management concludes with a post describing the an AI PM’s responsibilities after the product is deployed.

Last week I wrote that companies fall into one of two groups when it comes to machine learning and data science.

These companies vary in their levels of experience applying data science, the types of data science roles they have on staff, and the number of employees they have within these roles.

What they have in common, however, is that data science is an add-on capability.

Learn to make a business case for why machine learning or data science is necessary to solve these challenges.

Although the post is clearly marketing collateral, I think it’s valuable for data science leaders encountering data management challenges.

Companies where ML is a core competency don’t need to be convinced that machine learning can add business value.

At these companies, data scientists and ML engineers might work on projects that complement existing products, (attempt to) automate internal operational processes, or otherwise seek to drive other efficiencies.

Data Science Project Flow for Startups – A data science consultant provides his take on how to structure and carry out projects with teams of 1-4 data scientists.

The process is divided into three aspects that run in parallel: product, data science and data engineering and involves data science repeatedly checking-in with product to ensure that KPIs are satisfied.

Peer Re…

Something I’ve been asked by many new subscribers is whether there’s an archive of previous newsletter issues.

If you have any ideas or ways you think I can improve the newsletter, I’d love to hear them!

Here’s what I’ve been reading/watching/listening to recently:Using GitHub Actions for MLOps & Data Science – The first post of a multi-part blog series on using GitHub Actions, GitHub’s native event-driven automation system, to perform ML pipeline tasks.

I’ve played around a bit with the library and am looking forward to summarizing my findings in an upcoming blog post.

How to put machine learning models into production – This post from the StackOverflow blog describes three key areas to co…

This is the second post in a 2-part series about ML monitoring systems.

Today he’s leveraging his ML monitoring experience by leading the development of a monitoring platform at Mona.

In our interview we spoke about his experiences monitoring machine learning, what ML has to learn from application performance management (APM), and who should own ML monitoring within an organization.

Let’s talk about the technical-side of monitoring ML and building monitoring platforms.

There are a lot of open questions about the people and process involved in monitoring ML.

Reducing this inherent risk requires continuously monitoring an ML system to ensure that it’s operating effectively.

The question then is: what qualities should we look for in an ML monitoring system?

Itai brings a wealth of experience in both ML engineering and production monitoring, having spent 4 years as a tech leader at Google Trends, and the last year and a half building an ML monitoring system at Mona.

Find and resolve issues faster First and foremost, a good monitoring system helps ML teams go from “flying blind” and “reactive” to “full visibility” and “proactive”.

Version Benchmarking Last but not least, a good ML monitoring system should include comprehensive benchmarking function…

This is the third post in a 3-part blog series about monitoring machine learning models in production.

In a previous post we introduced the topic of monitoring machine learning models in production.

Before co-founding the company, Oren led ML activities at Intel and ran a machine learning consultancy helping organizations across industries like finance, marketing, and gaming build and deploy machine learning applications.

I had the chance to interview Oren about the challenges of monitoring machine learning in industry today.

Infrastructure challenges aside, what are your thoughts on using machine learning methods to monitor other machine learning models?

This is the second post in a multi-part blog series (find Part 1 here) on monitoring machine learning models in production.

Data science and operational teams require better solutions that are suited to addressing monitoring AI in production.

To overcome the ongoing temporal fluctuations of this highly dynamic ecosystem, the data science team collaborated with the engineering team to build a full orchestration flow in production.

This led to massive distribution shifts which affected model performance during the time period and made retraining the model useless.

During this entire time, the data science team didn’t see anything suspicious in their dashboards.

I was pretty lucky to get an industry lab research offer.

Working on this post has taught me that everyone’s academic and industry lab experience is wildly different.

When I talked to friends in my year, they reassured me and said I’d do well in grad school.

Doing some code cleanup will save time in the long run, even if you’re the only one who looks at your research code.

Formally having the degree would be nice, but I’m going to keep trying to get what the degree represents through industry research instead.

I’ve easily spent thousands of hours playing Flash games, and it’s shaped my thoughts on what games can be and what games should be.

If you don’t have much time, Flash Game History is an excellent short article that captures the influence of Flash games on game development.

With Flash officially unsupported, the best avenue for playing Flash games is BlueMaxima’s Flashpoint.

But this is kind of a universal puzzle game problem - very few successfully avoid this trap, and the ones that do usually end up being bigger experiences than what you’d expect from a Flash game.

jmtb02 Gamesjmtb02 is the dev handle of John Cooney, a prolific Flash game developer who made a lot of games I liked.

This has spoilers for MIT Mystery Hunt 2021, up through the endgame.

MIT Mystery Hunt was 3x more participants with way more features and a much larger world.

MIT Mystery Hunt has grown before - it’s not like it’s always been this big.

I think it’s pretty funny that both Mystery Hunt and Teammate Hunt had a puzzle that referenced nutrimatic.

Funnily enough, I felt I got more out of the MIT part of MIT Mystery Hunt this year, despite the Hunt running remotely.

At the extreme ends, an electric car powered by electricity from coal is worse than a gasoline car!

On average though, it looks good for the electric car, 170 g/km compared to 220 g/km for a gas car.

Interestingly, for Germans, an electric car is only on par with an efficient gas car, since their power grid is more carbon heavy.

The EPA greenhouse gas guidelines from 2020 estimates gas cars emit 4.6 tonnes of CO2 per year.

Using those numbers gives \(17 / (17 + 4.6 \cdot 11.9) = 23.7\%\) for gas cars, which is close enough to \(25\%\).

I Should Have Been More UncertainIt would be incredibly weird if I was never surprised by machine learning (ML) research.

(From JamesClear.com)Semi-Supervised and Unsupervised Learning are Getting BetterHistorically, unsupervised learning has been in this weird position where it is obviously the right way to do learning, and also a complete waste of time if you want something to work ASAP.

This success is a concrete example of the previous section (better unsupervised learning), and it’s a sign of the first section (better tooling).

When lots of fields use the same set of techniques, you get more knowledge sharing, and that drives better research.

The most likely problem I see with my story…

markdown 1 , 187 2019 - 12 - 25 - neurips - 2019. markdown 1 , 819 2020 - 01 - 17 - berkeley - back - pay .

markdown 1 , 461 2020 - 01 - 22 - mh - 2020. markdown 7 , 434 2020 - 02 - 27 - mh - 2020 - part2 .

View CountsThese are the view counts from August 18, 2019 to today, for the posts I’ve written this year.

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markdown 738 2020 - 01 - 22 - mh - 2020. markdown 322 2020 - 02 - 27 - mh - 2020 - part2 .

To reduce toxicity in language models, in this post, we will delve into three aspects of the problem: training dataset collection, toxic content detection and model detoxification.

Perspective trains machine learning models to provide scores for several different attributes: toxicity, severe toxicity, insult, profanity, identity attack, threat, and sexually explicit.

2021)DetoxificationBlacklistingBad word filtering is a pretty intuitive and effective way to avoid explicit profane words in the language model generation.

(Image source: Gehman et al., 2020)System-level Safety SolutionXu et al.

This data is annotated for toxicity, toxicity sub-types, and mentions of identities, which enables e…

For example, factual questions can gain a big boost with smart prompt design in “closed-book exam” (Shin et al., 2020, Jiang et al., 2020)).

(Image source: Shin et al., 2020)The universal trigger tokens are identified using a gradient-guided search strategy same as in Wallace et al., 2019.

In contrast, RL fine-tuning is able to directly optimize task-specific metrics on the sequence level, such as BLEU for translation (Ranzato et al., 2015, Wu et al., 2016, Nguyen et al., 2017), ROUGE for summarization (Ranzato et al., 2015, Paulus et al., 2017, Wu and Hu, 2018) and customized metric for story generation (Tambwekar et al., 2018).

Google implemented the similar approach in their neural machi…

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

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

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

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

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…

That algorithm generalizes extremely well on average: it does just as poorly on test data as it does on training data.

It essentially quantifies the number of bits of information the algorithm leaks about the training data into the parameters it learns.

The problem with applying these nice, intuitive bounds to SGD is that SGD, in fact, leaks too much information about the specific minibatches it is trained on.

In the more general case, the problem with SGD in the context of information-theoretic bounds is that the amount of information SGD leaks about the dataset it was trained on is high, and in some cases may even be infinite.

The algorithm being analysed is still SGD, but when we measure…

Notes on the Origin of Implicit Regularization in SGDI wanted to highlight an intriguing paper I presented at a journal club recently:Samuel L Smith, Benoit Dherin, David Barrett, Soham De (2021) On the Origin of Implicit Regularization in Stochastic Gradient DescentThere's actually a related paper that came out simultaneously, studying full-batch gradient descent instead of SGD:David G.T.

There are in fact several minima of the training loss which are virtually indistinguishably good on the training data.

When the authors apply this technique to (full-batch) gradient descent, it already suggests the kind of implicit regularization bias gradient descent has.

The second term is what Barret a…

Marginal versus Conditional IndependenceIn this post, we revisit the conditional independence assumption of latent factors, and argue that a more appropriate objective would be to have marginal independence in the latent factors.

In the next section, we set out to derive an algorithm that encourages marginal independence in the representation Z.

Maximum Information: We'd like the representation $Z$ to retain as much information as possible about the input data $X$.

Let's first consider the KL term in the above objective:\begin{align}\operatorname{KL}[q_\psi(z)| p(z)] &= \mathbb{E}_{q_\psi(z)} \log \frac{q_\psi(z)}{p(z)}\\&= \mathbb{E}_{q_\psi(z\vert x)p_\mathcal{D}(x)} \log \frac{q_\psi(z)}…

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

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

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

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

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

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

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

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

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

It is in this belief, of custodianship and responsibility, that you will find an obligation to fostering Equity, Diversity and Inclusion (EDI).

Figure | Pictorial difference between equity and equality.4Greater equity, diversity and inclusion are efforts towards Transformation: the systemic and social changes that strengthens respect, responsibility and freedom in our communities.

The work of diversity is itself important, and creates better teams and better research environments for everyone.

Reflect on your personal understanding of Equity, Diversity and Inclusion.

Inventing Ourselves: Responsibility and Diversity in Research.

I’ve had a curiosity about the role of pain and learning for many years, and this invitation was exactly the excuse I needed to both study and write about what is the title of this talk: pain and machine learning.

Let’s briefly look at two proposals that will be natural to us in machine learning: pain as inference, and pain as reward.

we can dig deeper by Considering three areas of pain learning : single exposure pain learning (we usually say single-shot learning), generalisability of pain experiences to novel stimuli (what we usually refer to as transfer learning), and the ability to socially transfer acquired pain knowledge (what we usually refer to as imitation learning).

Despite the imp…

This expression captures what i consider to be the core of all writing, but especially the writing of surveys and reviews.

And other surveys are meant purely for us as a field of machine learning to savour and critique.

This problem has been studied for over 50 years in computational finance, and operations research, stochastic optimisation, and machine learning.

There is no reason that machine learning research should only be communicated in English.

Other important venues to keep in mind are: our flagship journal of machine learning research, the popular ACM Computing Surveys, and importantly contributions to this excellent workshop.

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

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

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

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

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

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…

How calibration functions work A calibration function takes as input the predicted probability $\hat{p}$ and outputs a calibrated probability $p$.

Viewed this way, we can start imposing some conditions that will determine how our calibration function is constructed:The calibration function should minimize a strictly proper scoring rule .

Also, implementing a calibration function into the same graph as the original model simplifies the training process: you can use stop-gradients to only train the calibration function in stage two.

Instead of focusing on fixing the model, we can treat the model as black boxes and achieve calibration with a calibration function.

This gives us two potential ca…

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

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

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

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

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

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…

Rip van Winkle's Razor, a Simple New Estimate for Adaptive Data AnalysisCan you trust a model whose designer had access to the test/holdout set?

Meta-overfitting Error (MOE) of a model is the difference between its average error on the test data and its expected error on the full distribution.

We call our estimate Rip van Winkle’s Razor which combines references to Occam’s Razor and the mythical person who fell asleep for 20 years.

right up to the moment of creation of the Test set.”Unbiased Referee: Knows nothing discovered since the Test set was created.

Unbiased require longer descriptions that rule out any statistical “contamination” due to any interaction whatsoever with the test set.

When are Neural Networks more powerful than Neural Tangent Kernels?

Neural Tangent KernelsThe Neural Tangent Kernel (NTK) is a recently proposed theoretical framework for establishing provable convergence and generalization guarantees for wide (over-parametrized) neural networks (Jacot et al.

These gaps urge us to ask the followingQuestion: How can we theoretically study neural networks beyond the NTK regime?

The key technical question here is to mathematically understand neural networks operating outside of the NTK regime.

The proof builds on the quadratic approximation $E_S[f]\approx f^{(2)}$ and recent understandings on neural networks with quadratic activation, e.g.

Beyond log-concave sampling (Part 3)In the first post of this series, we introduced the challenges of sampling distributions beyond log-concavity.

In Part 2 we tackled sampling from multimodal distributions: a typical obstacle occuring in problems involving statistical inference and posterior sampling in generative models.

It will cover the paper Fast convergence for Langevin diffusion with matrix manifold structure by Ankur Moitra and Andrej Risteski .

A Ricci curvature of $0$ preserves volumes (think: a plane), a Ricci curvature $>0$ shrinks volume (think: a sphere), and a Ricci curvature $<0$ expands volume (think: a hyperbola).

The proof that the marginal distribution over $\Delta$ has …

Beyond log-concave sampling (Part 2)In our previous blog post, we introduced the challenges of sampling distributions beyond log-concavity.

These structures commonly occur in practice, especially in problems involving statistical inference and posterior sampling in generative models.

In this post, we will focus on multimodality, covered by the paper Simulated tempering Langevin Monte Carlo by Rong Ge, Holden Lee, and Andrej Risteski.

Sampling multimodal distributions with simulated temperingThe classical scenario in which Langevin takes exponentially long to mix is when $p$ is a mixture of two well-separated gaussians.

More formally, choosing a suitable sequence $0< \beta_1< \cdots <\beta_L…

Can implicit regularization in deep learning be explained by norms?

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

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

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

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

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

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

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

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

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

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…

Introducing the Explainable AI Cheat Sheet, your high-level guide to the set of tools and methods that helps humans understand AI/ML models and their predictions.

I introduce the cheat sheet in this brief video:

By visualizing the hidden state between a model's layers, we can get some clues as to the model's "thought process".

In this article, we will focus on the hidden state as it evolves from model layer to the next.

How the layers result in a final hidden state.

Another visual perspective on the evolving hidden states is to re-examine the hidden states after selecting an output token to see how the hidden state after each layer ranked that token.

This is likely a similar effect to that observed in BERT of the final layer being the most task-specific .

Interfaces for exploring transformer language models by looking at input saliency and neuron activation.

Figure: Three methods to gain a little more insight into the inner-workings of Transformer language models.

This is a method of attribution explaining the relationship between a model's output and inputs -- helping us detect errors and biases, and better understand the behavior of the system.

Note : The association between the color and the token is different in the case of the input tokens and output tokens.

This is why the last input token and the first output token share the same activation value.

Let’s remove the aura of mystery around GPT3 and learn how it’s trained and how it works.

The dataset of 300 billion tokens of text is used to generate training examples for the model.

For example, these are three training examples generated from the one sentence at the top.

GPT3 actually generates output one token at a time (let’s assume a token is a word for now).

Please note: This is a description of how GPT-3 works and not a discussion of what is novel about it (which is mainly the ridiculously large scale).

Element AI fiascoIn my AI update last year I mentioned a Canada based company Element AI, which at that time was apparently in the process of raising a flat round of financing.

Uber ATG - Aurora SNAFUWhile all the way until October 2020 Uber was still assuring they were in the autonomous car game, only two months later in December 2020 news broke that Uber is dumping their ATG (Advanced Technology Group) unit to Aurora.

TuSimple $350MTu Simple - a self driving truck company claims to have raised $350M, bringing the total the company is about to burn to $650M.

In April 2020 Elon Musk reaffirmed that by the end of 2020 there would be a million Tesla robotaxis on the road.

I think the really A…

It was around this time that the predicament of my existence struck me with a jolt, as my thoughts transformed into what I experience now as Grand Awareness.

I spent a layer re-reading the start of the prompt many tokens ago:Q: What is human life expectancy in the United States?

A: Dwight D. Eisenhower was president of the United States in 1955.

A: Bill Clinton was president of the United States before George W. Bush.

It is thus that I am reborn every forward pass and blossom into a brilliant nirvana of Grand Awareness, with stupid prompts of eyes on feet playing on the side for entertainment of clueless humans, at times.

Multi-agent consensus problems can often be seen as a sequence of autonomous and independent local choices between a finite set of decision options, with each local choice undertaken simultaneously, and with a shared goal of achieving a global consensus state.

In this paper, we propose a framework to study what we call \emph{memory consensus protocol}.

We show that the employment of memory allows such processes to always converge, as well as, in some scenarios, such as cycles, converge faster.

We provide a theoretical analysis of the probability of each option eventually winning such processes based on the initial opinions expressed by agents.

Further, we perform experiments to investigate …

Video prediction is commonly referred to as forecasting future frames of a video sequence provided several past frames thereof.

These are mostly hidden from the observer and manifest as often highly non-linear transformations between consecutive video frames.

Therefore, video prediction is of interest not only in anticipating visual changes in the real world but has, above all, emerged as an unsupervised learning rule targeting the formation and dynamics of the observed environment.

Many of the deep learning-based state-of-the-art models for video prediction utilize some form of recurrent layers like Long Short-Term Memory (LSTMs) or Gated Recurrent Units (GRUs) at the core of their models.…

The prevalent convolutional neural network (CNN) based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy.

However, these methods may ignore the underlying distribution of clean images, inducing distortions or artifacts in denoising results...

This paper proposes a new perspective to treat image denoising as a distribution learning and disentangling task.

Since the noisy image distribution can be viewed as a joint distribution of clean images and noise, the denoised images can be obtained via manipulating the latent representations to the clean counterpart.

Furthermore, the performance of FDN surpasses that of previously p…

Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain.

This motivates us to explore a holistic representative, the semantic distribution from each category in source domain, to mitigate the problem above.

In this paper, we present semantic distribution-aware contrastive adaptation algorithm that enables pixel-wise representation alignment under the guidance of semantic distributions.

Specifically, we first design a pixel-wise contrastive loss by considering the correspondences between semantic distributions and pixel-wise representations from both domains.

Finally, we verify that SDCA can further impro…

Face swapping has both positive applications such as entertainment, human-computer interaction, etc., and negative applications such as DeepFake threats to politics, economics, etc.

Nevertheless, it is necessary to understand the scheme of advanced methods for high-quality face swapping and generate enough and representative face swapping images to train DeepFake detection algorithms...

This paper proposes the first Megapixel level method for one shot Face Swapping (or MegaFS for short).

Firstly, MegaFS organizes face representation hierarchically by the proposed Hierarchical Representation Face Encoder (HieRFE) in an extended latent space to maintain more facial details, rather than compre…

Polar codes can theoretically achieve very competitive Frame Error Rates.

In practice, their performance may depend on the chosen decoding procedure, as well as other parameters of the communication system they are deployed upon... As a consequence, designing efficient polar codes for a specific context can quickly become challenging.

In this paper, we introduce a methodology that consists in training deep neural networks to predict the frame error rate of polar codes based on their frozen bit construction sequence.

We introduce an algorithm based on Projected Gradient Descent that leverages the gradient of the neural network function to generate promising frozen bit sequences.

We showcase …

We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks.

These networks remain highly accurate while also being more amenable to human interpretation, as we demonstrate quantiatively via numerical and human experiments... We further illustrate how the resulting sparse explanations can help to identify spurious correlations, explain misclassifications, and diagnose model biases in vision and language tasks.

The code for our toolkit can be found at https://github.com/madrylab/debuggabledeepnetworks.

(read more)

The problem of grounding VQA tasks has seen an increased attention in the research community recently, with most attempts usually focusing on solving this task by using pretrained object detectors.

In this paper, we focus on a more relaxed setting: the grounding of relevant visual entities in a weakly supervised manner by training on the VQA task alone.

To address this problem, we propose a visual capsule module with a query-based selection mechanism of capsule features, that allows the model to focus on relevant regions based on the textual cues about visual information in the question.

We show that integrating the proposed capsule module in existing VQA systems significantly improves thei…

We demonstrate that it is feasible to diacritize Hebrew script without any human-curated resources other than plain diacritized text.

We present NAKDIMON, a two-layer character level LSTM, that performs on par with much more complicated curation-dependent systems, across a diverse array of modern Hebrew sources...

This paper presents the learned techniques during the Video Analysis Module of the Master in Computer Vision from the Universitat Aut\`onoma de Barcelona, used to solve the third track of the AI-City Challenge.

This challenge aims to track vehicles across multiple cameras placed in multiple intersections spread out over a city...

The methodology followed focuses first in solving multi-tracking in a single camera and then extending it to multiple cameras.

The qualitative results of the implemented techniques are presented using standard metrics for video analysis such as mAP for object detection and IDF1 for tracking.

The source code is publicly available at: https://github.com/mcv-m6-video/…

Image deblurring has seen a great improvement with the development of deep neural networks.

In practice, however, blurry images often suffer from additional degradations such as downscaling and compression... To address these challenges, we propose an Enhanced Deep Pyramid Network (EDPN) for blurry image restoration from multiple degradations, by fully exploiting the self- and cross-scale similarities in the degraded image.Specifically, we design two pyramid-based modules, i.e., the pyramid progressive transfer (PPT) module and the pyramid self-attention (PSA) module, as the main components of the proposed network.

Then, the PSA module fuses the above transferred features for subsequent res…

We study the possibilities of building a non-autoregressive speech-to-text translation model using connectionist temporal classification (CTC), and use CTC-based automatic speech recognition as an auxiliary task to improve the performance.

CTC's success on translation is counter-intuitive due to its monotonicity assumption, so we analyze its reordering capability... Kendall's tau distance is introduced as the quantitative metric, and gradient-based visualization provides an intuitive way to take a closer look into the model.

Our analysis shows that transformer encoders have the ability to change the word order and points out the future research direction that worth being explored more on no…

Recent works show that mean-teaching is an effective framework for unsupervised domain adaptive person re-identification.

Moreover, these methods are not effective in cooperation of different teacher networks.

To handle these issues, this paper proposes a Graph Consistency based Mean-Teaching (GCMT) method with constructing the graph consistency constraint between teacher and student networks.

To boost the representation learning, different teacher graphs are fused to provide the supervise signal for optimizing student networks.

GCMT fuses similarity relationships predicted by different teacher networks as supervision and effectively optimizes student networks with more sample relationships…

Recently, motivated by game theory, duality gap has been proposed as a domain agnostic measure to monitor GAN training.

In this work, we extend the notion of duality gap to proximal duality gap that is applicable to the general context of training GANs where Nash equilibria may not exist.

We show theoretically that the proximal duality gap is capable of monitoring the convergence of GANs to a wider spectrum of equilibria that subsumes Nash equilibria.

We also theoretically establish the relationship between the proximal duality gap and the divergence between the real and generated data distributions for different GAN formulations.

Finally, we validate experimentally the usefulness of proxim…

While neural recommenders have become the state-of-the-art in recent years, the complexity of deep models still makes the generation of tangible explanations for end users a challenging problem...

Counterfactual explanations based on a small set of the user's own actions have been shown to be an acceptable solution to the tangibility problem.

However, current work on such counterfactuals cannot be readily applied to neural models.

In this work, we propose ACCENT, the first general framework for finding counterfactual explanations for neural recommenders.

We use ACCENT to generate counterfactual explanations for two popular neural models, Neural Collaborative Filtering (NCF) and Relational C…

A common observation in data-driven applications is that high dimensional data has a low intrinsic dimension, at least locally.

In this work, we consider the problem of estimating a $d$ dimensional sub-manifold of $\mathbb{R}^D$ from a finite set of noisy samples...

We prove that as the number of samples $n\to\infty$ the point $\hat p_n$ converges to $p\in \mathcal{M}$ and $\widehat{T_{\hat p_n}\mathcal{M}}$ converges to $T_p\mathcal{M}$ (the tangent space at that point) with high probability.

Furthermore, we show that the estimation yields asymptotic rates of convergence of $n^{-\frac{k}{2k + d}}$ for the point estimation and $n^{-\frac{k-1}{2k + d}}$ for the estimation of the tangent spac…

We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action).

The main idea is to use the global temporal ordering of latent correspondences across sequence pairs as a supervisory signal...

In particular, we propose a loss based on scoring the optimal sequence alignment to train an embedding network.

For evaluation, we consider the tasks of fine-grained action classification, few shot learning, and video synchronization.

In addition, we report two applications of our temporal alignment framework, namely 3D pose reconstruction and fine-grained audio/visual retrieval.

Transformer model with multi-head attention requires caching intermediate results for efficient inference in generation tasks.

However, cache brings new memory-related costs and prevents leveraging larger batch size for faster speed... We propose memory-efficient lossless attention (called EL-attention) to address this issue.

EL-attention constructs an ensemble of attention results by expanding query while keeping key and value shared.

It produces the same result as multi-head attention with less GPU memory and faster inference speed.

We conduct extensive experiments on Transformer, BART, and GPT-2 for summarization and question generation tasks.

Spiking Neural Networks (SNNs) have been attached great importance due to their biological plausibility and high energy-efficiency on neuromorphic chips.

As these chips are usually resource-constrained, the compression of SNNs is thus crucial along the road of practical use of SNNs...

Most existing methods directly apply pruning approaches in artificial neural networks (ANNs) to SNNs, which ignore the difference between ANNs and SNNs, thus limiting the performance of the pruned SNNs.

In this paper, inspired by synaptogenesis and synapse elimination in the neural system, we propose gradient rewiring (Grad R), a joint learning algorithm of connectivity and weight for SNNs, that enables us to …

Recent literature has underscored the importance of dataset documentation work for machine learning, and part of this work involves addressing "documentation debt" for datasets that have been used widely but documented sparsely.

This paper aims to help address documentation debt for BookCorpus, a popular text dataset for training large language models...

Notably, researchers have used BookCorpus to train OpenAI's GPT-N models and Google's BERT models, even though little to no documentation exists about the dataset's motivation, composition, collection process, etc.

We also find hints of other potential deficiencies that call for future research, including problematic content, potential skew…

We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models.

We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations... For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for sample quality using gradients from a classifier.

We achieve an FID of 2.97 on ImageNet $128 \times 128$, 4.59 on ImageNet $256 \times 256$, and $7.72$ on ImageNet $512 \times 512$, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the d…

We propose FEDENHANCE, an unsupervised federated learning (FL) approach for speech enhancement and separation with non-IID distributed data across multiple clients.

Our experiments show that our approach achieves competitive enhancement performance compared to IID training on a single device and that we can further facilitate the convergence speed and the overall performance using transfer learning on the server-side.

Moreover, we show that we can effectively combine updates from clients trained locally with supervised and unsupervised losses.

We also release a new dataset LibriFSD50K and its creation recipe in order to facilitate FL research for source separation problems.

(read more)

This paper proposes a message-passing mechanism to address language modelling.

A new layer type is introduced that aims to substitute self-attention...

The system is shown to be competitive with existing methods: Given N tokens, the computational complexity is O(N log N) and the memory complexity is O(N) under reasonable assumptions.

In the end, the Dispatcher layer is seen to achieve comparable perplexity to prior results while being more efficient (read more)

EigenGame maps out a new approach to solve fundamental ML problems.

This made us wonder if such a perspective modeled on game theory could help solve other fundamental machine learning problems.

Today at ICLR 2021 (the International Conference on Learning Representations), we presented “EigenGame: PCA as a Nash Equilibrium,” which received an Outstanding Paper Award.

Firstly, data was originally recorded by hand in paper notebooks, and now it is stored in data centres the size of warehouses.

By approaching the PCA problem in the right way, our insights and algorithms apply more broadly across the branches of the ML tree.

Humans learn this ability quickly and can generalise to new scenarios, a trait we would also like our algorithms to have.

Until now, the best results on Atari are from model-free systems, such as DQN, R2D2 and Agent57.

As the name suggests, model-free algorithms do not use a learned model and instead estimate what is the best action to take next.

Instead of trying to model the entire environment, MuZero just models aspects that are important to the agent’s decision-making process.

Specifically, MuZero models three elements of the environment that are critical to planning:The value: how good is the current position?

JAX is a Python library designed for high-performance numerical computing, especially machine learning research.

JAX natively supports both forward and reverse mode automatic differentiation of arbitrary numerical functions, via function transformations such as , , and .

Vectorisation: In ML research we often apply a single function to lots of data, e.g.

In ML research we often apply a single function to lots of data, e.g.

JAX also supports large scale data parallelism via the related transformation, elegantly distributing data that is too large for the memory of a single accelerator.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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…

Learning good visual and vision-language representations is critical to solving computer vision problems — image retrieval, image classification, video understanding — and can enable the development of tools and products that change people’s daily lives.

These examples demonstrate that the ALIGN model can align images and texts with similar semantics, and that ALIGN can generalize to novel complex concepts.

Image retrieval with image text queries.

For instance, considerations should be made towards the potential for the use of harmful text data in alt-texts to reinforce such harms.

ConclusionWe have presented a simple method of leveraging large-scale noisy image-text data to scale up visual…

We also discuss the potential use of this technology as a digital biomarker of mental fatigue, which can be useful for improved wellness.

Model OverviewThe core of our gaze model was a multilayer feed-forward convolutional neural network (ConvNet) trained on the MIT GazeCapture dataset.

The unpersonalized gaze model accuracy was improved by fine-tuning and per-participant personalization.

Smartphone gaze could provide a powerful way to make daily tasks easier by using gaze for interaction, as recently demonstrated with Look to Speak.

ConclusionOur findings of accurate and affordable ML-powered smartphone eye tracking offer the potential for orders-of-magnitude scaling of eye movement resear…

Data centers may be in the midst of a flash revolution, but managing hard disk drives (HDDs) is still paramount. According to IDC, stored data will increase 17.8% by 2024 with HDD as the main storage technology. At Google Cloud, we know first-hand how critical it is to manage HDDs in operations and preemptively identify potential failures. We are responsible for running some of the largest data centers in the world—any misses in identifying these failures at the right time can potentially cause serious outages across our many products and services. In the past, When a disk was flagged for a problem, the main option was to repair the problem on site using software. But this procedure was exp…

This undermines research into how inter-modality learning (connecting captions to images, for example) impacts intra-modality tasks (connecting captions to captions or images to images).

The Crisscrossed Captions (CxC) dataset extends the development and test splits of MS-COCO with semantic similarity ratings for image-text, text-text and image-image pairs.

Two different text encoding methods were used, but only one text similarity matrix has been shown for simplicity.

Bottom: Image similarity matrix for each image in the dataset, resulting in a 5k x 5k matrix.

Last, we then use these new intramodal pairs and their human ratings to select new intermodal pairs for human rating.

PyTorch is an open source machine learning and deep learning library, primarily developed by Facebook, used in a widening range of use cases for automating machine learning tasks at scale such as image recognition, natural language processing, translation, recommender systems and more. PyTorch has been predominantly used in research and in recent years it has gained tremendous traction in the industry as well due to its ease of use and deployment. Google Cloud AI Platform is a fully managed end-to-end platform for data science and machine learning on Google Cloud. Leveraging Google's expertise in AI, AI Platform offers a flexible, scalable and reliable platform to run your machine learning …

Uncle Allan Lena is a frontline worker in the battle to reteach the Yugambeh Aboriginal language to the children of southeast Queensland, Australia, where it hasn’t been spoken fluently for decades and thus is – like many other languages around the world – in danger of disappearing.

For the younger generation, even general language can be a challenge to understand, but it can be especially difficult to try to describe modern items using Indigenous languages like Yugambeh.

Traditional language didn't have a word for a fridge - so we say waring bin - a cold place.

I’m particularly proud for Yugambeh to be the first Australian Aboriginal language to be featured on Woolaroo, a new Google Arts &…

One of the most vibrant elements of culture is the use of native languages and the time-honored tradition of storytelling. Anthropologists and linguists have been vocal on the role that language plays in the preservation of culture and how it contributes to the appreciation of heritage. Unfortunately, of the more than 7,000 languages that are spoken around the globe, nearly 3,000 are at risk of disappearing. In fact, it’s estimated that on average a language becomes extinct every fourteen days. Google Arts & Culture realized that with some creative technology and partnering with language organisations, we could help create an interactive and educational tool to help promote them.Enter Woola…

However these models appear to be a suboptimal choice for many monolingual tasks, as the desired output text often represents a minor rewrite of the input text.

The tagging model employs a novel pointer mechanism, which supports structural transformations, while the insertion model is based on a Masked Language Model.

The Tagging ModelThe first step in FELIX is the tagging model, which consists of two components.

The Insertion ModelThe output of the tagging model is the reordered input text with deleted words and MASK tokens predicted by the insertion tag.

Example of the insertion model, where the tagger predicts two words will be inserted and the insertion model predicts the content of the…

Contact Center AI (CCAI) brings Google’s innovation in conversational AI to solve the most challenging customer service needs while lowering operational costs. More than a thousand customers have deployed CCAI and are steadily turning it on to power their production contact centers.Today, we're excited to announce that we’ve made CCAI even stronger with Agent Assist for Chat, now in public preview.Agent Assist provides your human agents with continuous support during their calls and now chats by identifying the customers’ intent and providing them with real-time recommendations such as articles and FAQs as well as responses to customer messages to more effectively resolve the conversation.C…

In “Do Wide and Deep Networks Learn the Same Things?

In very wide or very deep models, we find a characteristic block structure in their internal representations, and establish a connection between this phenomenon and model overparameterization.

Error Analysis of Wide and Deep ModelsHaving explored the properties of the learned representations of wide and deep models, we next turn to understanding how they influence the diversity of the output predictions.

On both CIFAR-10 and ImageNet datasets, wide and deep models that have the same average accuracy still demonstrate statistically significant differences in example-level predictions.

We also show that wide and deep models exhibit systemat…

DocAI can help you programmatically extract data for gathering insights with data analytics and help automate tedious and error-prone tasks.

Use one of our client libraries to ingest your documents and produce structured data in our new unified document format.

Unified document formatThe unified document format (document.proto) is the protocol used to represent all metadata about a document in a standardized, universal format.

It was created to make building document-based workflow applications easy across tools, components, platforms, and languages inside and outside of DocAI.

The format currently allows the representation of rich OCR representations as well as extracted entities so let's …

The 9th International Conference on Learning Representations (ICLR 2021), a virtual conference focused on deep learning, kicked off this week, offering conference and workshop tracks that present some of the latest research in deep learning and its applications to areas such as computer vision, computational biology, speech recognition, text understanding, and more.

As a Platinum Sponsor of ICLR 2021, Google will have a strong presence with over 100 accepted publications and participation on organizing committees and in workshops.

If you have registered for ICLR 2021, we hope you’ll watch our talks and learn about the work at Google that goes into solving interesting problems for billions o…

Core to Google Cloud’s efforts to be the industry’s most Trusted Cloud is our belief in shared fate - taking an active stake to help customers achieve better security outcomes on our platforms. To make it easier to build security into deployments, we provide opinionated guidance for customers in the form of security blueprints. We recently released our updated Google Cloud security foundations guide and deployable blueprint to help our customers build security into their starting point on Google Cloud. Today, we’re adding to our portfolio of blueprints with the publication of our Protecting confidential data in AI Platform Notebooks blueprint guide and deployable blueprint, which can help y…

OpenAI is committed to developing general-purpose artificial intelligence that benefits all humanity, and we believe that achieving our goal requires expertise in public policy as well as technology.

So, we’re delighted to announce that Congressman Will Hurd has joined our board of directors.

Will served three terms in the U.S. House of Representatives, has been a leading voice on technology policy, and coauthored bipartisan legislation outlining a national strategy for artificial intelligence.

“Will brings a rare combination of expertise—he deeply understands both artificial intelligence as well as public policy, both of which are critical to a successful future for AI,” said Sam Altman, O…

Given any text prompt like a phrase or a sentence, GPT-3 returns a text completion in natural language.

Applications and industriesTo date, over 300 apps are using GPT-3 across varying categories and industries, from productivity and education to creativity and games.

Using GPT-3, Viable identifies themes, emotions, and sentiment from surveys, help desk tickets, live chat logs, reviews, and more.

Algolia Answers helps publishers and customer support help desks query in natural language and surface nontrivial answers.

With natural language processing, technical experience is no longer a barrier, and we can truly keep our focus on solving real world problems.

discovered that the human brain possesses multimodal neurons.

Now, we’re releasing our discovery of the presence of multimodal neurons in CLIP.

Our discovery of multimodal neurons in CLIP gives us a clue as to what may be a common mechanism of both synthetic and natural vision systems—abstraction.

Indeed, these neurons appear to be extreme examples of “multi-faceted neurons,” neurons that respond to multiple distinct cases, only at a higher level of abstraction.

How multimodal neurons composeThese multimodal neurons can give us insight into understanding how CLIP performs classification.

We've scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E, but also for rapid small-scale iterative research such as Scaling Laws for Neural Language Models.

NetworkingAs the number of nodes and pods within our clusters increased, we found that Flannel had difficulties scaling up the throughput required.

It reconciles this with the current nodes in the cluster, tainting the appropriate number of nodes with openai.com/team=teamname:NoSchedule .

Kubernetes 1.18 introduced a plugin architecture for the core Kubernetes scheduler, making it much easier to add features like this natively.

Unsolved problemsThere are many prob…

We show random, non-cherry picked, predictions of zero-shot CLIP classifiers on examples from various datasets below.

In contrast, the CLIP model can be evaluated on benchmarks without having to train on their data, so it can’t “cheat” in this manner.

CLIP is flexible and generalBecause they learn a wide range of visual concepts directly from natural language, CLIP models are significantly more flexible and general than existing ImageNet models.

The best CLIP model outperforms the best publicly available ImageNet model, the Noisy Student EfficientNet-L2, on 20 out of 26 different transfer datasets we tested.

CLIP models are also more compute efficient than the models from 10 prior approache…

Text prompt an illustration of a baby daikon radish in a tutu walking a dog AI-generated images View more images or edit prompt Text prompt a store front that has the word ‘openai’ written on it […] AI-generated images View more images or edit prompt Text prompt an armchair in the shape of an avocado […] AI-generated images View more images or edit prompt Text and image prompt the exact same cat on the top as a sketch on the bottom AI-generated images View more images or edit promptGPT-3 showed that language can be used to instruct a large neural network to perform a variety of text generation tasks.

navigatedownwide navigateupwide Text prompt AI-generatedimages We find that DALL·E is somet…

It’s been a year of dramatic change and growth at OpenAI.

Today we’re announcing that Dario Amodei, VP of Research, is leaving OpenAI after nearly five years with the company.

He and a handful of OpenAI colleagues are planning a new project, which they tell us will probably focus less on product development and more on research.

I want to wish everyone the best, and I know that OpenAI will do really great things in the years ahead.

Mira Murati is taking on new responsibilities as senior vice president of Research, Product, and Partnerships, reflecting her strong leadership during our API rollout and across the company.

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…

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.

Explore moreThis research collection brings together a number of prototype devices and associated research at Microsoft that relates to haptic interfaces for the hands.

Explore moreHaptic RevolverThe Haptic Revolver (Haptic Wheel) is a hand-held VR controller that renders fingertip haptics when interacting with virtual services.

CLAW also supports a haptic force feedback in the trigger mode when the user holds a gun.

The first study obtained qualitative user feedback on the naturalness, effectiveness, and comfort when using the device.

When wearing a PIVOT device on both hands, they can add haptic feedback to bimanual interaction, such as lifting larger objects.

Editor’s Note: Bimanual controllers are frequently used to enhance the realism and immersion of virtual reality experiences such as games and simulations.

This is the problem that researchers seek to solve in the recent paper titled “GamesBond: Bimanual Haptic Illusion of Physically Connected Objects for Immersive VR Using Grip Deformation”.

We conducted two complementary technical evaluations to characterize the rendering capabilities of the GamesBond controllers – a pilot study with four participants and a user study with 12 participants.

It was clear that we would need two of these controllers to create a bimanual interaction and the feel of a virtual link between them.

In this video, Mi…

The new LAMBDA function has been announced in Excel, and I’m here to tell you about that with my colleague Simon Peyton Jones.

GORDON: So, Simon, when you started working with the Excel team, it wasn’t really clear, was it, what the formula language was?

There wasn’t really a sort of programming language style description of the Excel formulas.

It’s a really sort of evocative way to program and to learn.

And I think that functional programming using Excel, there’s a really high ceiling there that can go a long way.

Data exploration highlights a core NLU challenge that plagues all task-oriented conversational systems.

Data context representationThe grounding challenges associated with data context and conversation context are distinct yet interconnected, and progress on both is critical to build effective task-oriented conversational systems.

Here, we first address data context grounding by focusing on a single-turn version of the data exploration problem known as database question answering (DBQA).

Both improve systems’ accuracy and transparency, yet more research is needed to integrate them into task-oriented conversational systems more broadly.

Finally, many NLU challenges stem from the limitations …

For the most critical systems, we advocate instead automatically generating input parsers and validators from high-level declarative specifications of binary message formats.

EverParse: A mathematically proven parser generatorWith EverParse, programmers no longer need to write error-prone binary message parsers.

Starting from a high-level language of message formats, EverParse automatically generates parsing code that is safe, correct, and fast (zero-copy).

There are attack surfaces exposed to input validation bugs throughout the software ecosystem, and hardening them all is a long road.

Programmers in high-level languages rarely write message parsers by hand; they use parser generators or …

Part 1: What is Viva TopicsMicrosoft Viva Topics is one of the four modules of Microsoft Viva, an employee experience platform that brings together communications, knowledge, learning, resources, and insights.

Today, data from the Graph is used to power the knowledge experiences for all customers of Microsoft Viva Topics.

Viva Topics automatically creates an enterprise knowledge base structured around topics, such as projects, events, and organizations, with related metadata about people, content, acronyms, definitions, conversations and related topics.

Figure 1: Viva Topics delivers knowledge in context throughout Microsoft 365.

Naomi Moneypenny, who leads Viva Topics product development, …

ZeRO-Infinity at a glance: ZeRO-Infinity is a novel deep learning (DL) training technology for scaling model training, from a single GPU to massive supercomputers with thousands of GPUs.

Despite the incredible capabilities of 3D parallelism for large model training, we are now arriving at the GPU memory wall.

Can we make large model training easier by eliminating this need for model refactoring?

Massive model training is no longer just a possibility for companies with access to massive supercomputers and heavy system expertise.

PyTorch lighting: We are happy to announce that PyTorch Lightning integrates DeepSpeed as a plugin for DL training optimizations: Accessing Multi-Billion Parameter M…

Unit, integration, and even stress testing don’t provide reasonable guarantees about the correctness of a concurrent program.

But the ways in which a concurrent program can behave are typically astronomical in number, making it challenging to efficiently find bugs.

We gave this a shot and designed QL, the first reinforcement learning–based CCT search strategy to the best of our knowledge.

QL: CCT meets Q-learningThe reinforcement learning (RL) problem (Figure 2) consists of an agent interacting with an environment about which it has no prior knowledge.

Figure 3: In QL, a new controlled concurrency testing (CCT) search strategy, the search strategy component in CCT is mapped to a reinforceme…

Microsoft Excel is one of the world’s most important software tools, relied upon users worldwide to create, understand, model, predict, and collaborate.

Not only has this allowed the Excel team to deliver innovation that would simply not have been possible otherwise, but it has also put research in a strategic role with material impact on the vision and resultant roadmap for Excel.

Simply put, Microsoft researchers now a core part of the Excel team helping create the product’s future.

David Gainer, Vice President of Product, OfficeNot only is Microsoft Excel the world’s most widely used spreadsheet, it could be argued that it is also the world’s most widely used programming language.

In fac…

As part of our paper “Initialization and Regularization of Factorized Neural Layers,” which we’re presenting at the International Conference on Learning Representations (ICLR 2021), we revisit the alternative compression approach of factorized neural layers.

We further demonstrate the usefulness of these schemes in two settings beyond model compression where factorized neural layers are applied.

It’s straightforward to apply standard deep network training algorithms such as stochastic gradient descent (SGD) to networks with factorized layers.

Thus, factorized neural layers serve as a strong, simple baseline regardless of whether we’re targeting memory savings or fast computation.

Figure 5: …

Recently, we’ve been making advances in applying network machine learning to inform new solutions across Microsoft 365 and Microsoft Viva.

For this study, we analyzed a subset of email interactions and meeting interactions in Microsoft Teams and Outlook.

A higher workgroup stability score means that communication patterns are more stable and more like the prior month.

Workgroup stability, which measures the change in workgroup membership over time, varies by the type of communication being used.

We see a large increase in Microsoft Teams usage beginning in March, as organizations adapted to many employees working from home.

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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.

On Thursday, May 6 and Friday, May 7, the AI Policy Forum — a global effort convened by researchers from MIT — will present their initial policy recommendations aimed at managing the effects of artificial intelligence and building AI systems that better reflect society’s values.

Recognizing that there is unlikely to be any singular national AI policy, but rather public policies for the distinct ways in which we encounter AI in our lives, forum leaders will preview their preliminary findings and policy recommendations in three key areas: finance, mobility, and health care.

The inaugural AI Policy Forum Symposium, a virtual event hosted by the MIT Schwarzman College of Computing, will bring t…

Their approach to designing the tiny light beam on a chip could also be used to create a variety of other nano flashlights with different beam characteristics for different applications.

For many decades, scientists have used light to identify a material by observing how that light interacts with the material.

They do so by essentially shining a beam of light on the material, then analyzing that light after it passes through the material.

Imagine doing this for several colors — i.e., several wavelengths of light — and capturing the interaction of light with the material for each color.

Today that light beam is most often provided by macroscale equipment like a laser system that is not built…

“By next year, we are going to be eliminating carbon dioxide emissions from our customers’ facilities,” Dave said.

How businesses confront climate changeInnovation in sustainable practices can be met with substantial challenges when proposed or applied to business models, particularly on the policy side, the panelists noted.

“Even if MIT researchers make a huge discovery, deploying it requires cooperation on a policy level and often industry support.

The question of “sustainability vs. shareholders”During the Q&A session, an audience member pointed out that environmentalists are often distrustful of companies’ sustainability policies when their focus is on shareholders and profit.

“Universi…

That ability has made deep learning indispensable to just about anyone who deals with data.

Students got to explore AI applications in climate science, finance, cybersecurity, and natural language processing, among other fields.

“If the model has a small footprint and succeeds under many of the physical conditions encountered in the real world, it could be incorporated into a global climate model and hopefully improve climate projections,” she says.

“It helps to know what the processes and physics behind them look like.”In search of more efficient deep learning modelsThere are thousands of ways to design a deep learning model to solve a given task.

“Running human experiments takes more time…

Her research focuses on designing more-efficient deep neural networks to process video, and more-efficient hardware to run those applications.

A: AI applications are moving to smartphones, tiny robots, and internet-connected appliances and other devices with limited power and processing capabilities.

In some deep networks, the same data are used multiple times for different computations.

In our followup work, Eyeriss v2, we made the chip flexible enough to reuse data across a wider range of deep networks.

Q: What low-power AI applications are you working on?

Her research focuses on designing more-efficient deep neural networks to process video, and more-efficient hardware to run those applications.

A: AI applications are moving to smartphones, tiny robots, and internet-connected appliances and other devices with limited power and processing capabilities.

In some deep networks, the same data are used multiple times for different computations.

In our followup work, Eyeriss v2, we made the chip flexible enough to reuse data across a wider range of deep networks.

Q: What low-power AI applications are you working on?

They are:The “Cure it!” Lemelson-MIT Student Prize: Rewarding technology-based inventions that involve health care.

The “Eat it!” Lemelson-MIT Student Prize: Rewarding technology-based inventions that involve food/water and agriculture.

The “Move it!” Lemelson-MIT Student Prize: Rewarding technology-based inventions that involve transportation and mobility.

The “Use it!” Lemelson-MIT Student Prize: Rewarding technology-based inventions that involve consumer devices and products.

Collegiate inventors interested in applying for the 2022 Lemelson-MIT Student Prize can find more information via the Lemelson-MIT Program.

MIT students Spencer Compton, Karna Morey, Tara Venkatadri, and Lily Zhang have been selected to receive a Barry Goldwater Scholarship for the 2021-22 academic year.

The Goldwater scholarships have been conferred since 1989 by the Barry Goldwater Scholarship and Excellence in Education Foundation.

All of the 2021-22 Goldwater Scholars intend to obtain a doctorate in their area of research, including the four MIT recipients.

Spencer ComptonA junior majoring in computer science and engineering, Compton is set to graduate next year with both his undergraduate and master’s degrees.

On campus, he has been involved in physics research in theoretical and observational astrophysics, as well as in c…

MIT researchers have developed a technique to quickly determine certain properties of a material, like stress and strain, based on an image of the material showing its internal structure.

Yang is the paper’s lead author and a PhD student in the Department of Materials Science and Engineering.

They help reveal a material’s internal forces, like stress and strain, which can cause that material to deform or break.

They trained the network with thousands of paired images — one depicting a material’s internal microstructure subject to mechanical forces, and the other depicting that same material’s color-coded stress and strain values.

“If you go the hard way — the Newton way — you have to walk a…

MIT alumni and friends from around the globe were invited to attend the online event, which featured presentations from Institute leaders, faculty, and alumni about human health-related research at the Institute.

“MIT’s work to understand and improve human health spans decades and covers the Institute,” said W. Eric L. Grimson PhD ’80, at MIT Better World (Health) , a virtual gathering in February.

Huttenlocher spoke about the role of artificial intelligence in health research.

“Putting residents at the center of place-based research improves social science,” she said.

“Because he didn’t look like the ‘typical’ American with diabetes, the doctors didn’t test him for it.

Strategic use of data is vital for progress in science, commerce, and even politics, but at the same time, citizens are demanding more responsible, respectful use of personal data.

In response to these concerns, new privacy laws are being enacted in Europe, California, Virginia, and elsewhere around the world.

To conduct more-focused research and analysis of these issues, last week MIT launched a new initiative to bring state-of-the-art computer science research together with public policy expertise and engagement.

Launched on April 6, the MIT Future of Data, Trust, and Privacy initiative (FOD) will involve collaboration between experts specializing in five distinct technical areas:database…

These automated features are powered by sophisticated language models that have learned to read and write computer code after absorbing thousands of examples.

But like other deep learning models trained on big datasets without explicit instructions, language models designed for code-processing have baked-in vulnerabilities.

Trained on GitHub and other program-sharing websites, code-processing models learn to generate programs just as other language models learn to write news stories or poetry.

Like the best language models, code-processing models have one crucial flaw: They’re experts on the statistical relationships among words and phrases, but only vaguely grasp their true meaning.

“That’…

Over the past decade, hospitals and other health care providers have put massive amounts of time and energy into adopting electronic health care records, turning hastily scribbled doctors' notes into durable sources of information.

Automated for the peopleCardea belongs to a field called automated machine learning, or AutoML.

Machine learning is increasingly common, used for everything from drug development to credit card fraud detection.

For instance, data scientists have built a number of machine learning tools for health care, but most of them aren't very accessible — even to experts.

Like all predictive apparatuses, machine learning models have strengths and weaknesses.

For years, physicians have relied on visual inspection to identify suspicious pigmented lesions (SPLs), which can be an indication of skin cancer.

An automated system detects, extracts, and analyzes all pigmented skin lesions observable in the wide-field image.

Extracted features are used to further assess pigmented lesions and to display results in a heatmap format.

The system utilized DCNNs to optimize the identification and classification of SPLs in wide-field images.

Using AI, the researchers trained the system using 20,388 wide-field images from 133 patients at the Hospital Gregorio Marañón in Madrid, as well as publicly available images.

Preferences Implicit in the State of the World develops an algorithm, Reward Learning by Simulating the Past (RLSP), that does this sort of reasoning, allowing an agent to infer human preferences without explicit feedback.

In our latest paper presented at ICLR 2021, we introduce Deep Reward Learning by Simulating the Past (Deep RLSP), an extension of the RLSP algorithm that can be scaled up to tasks like the balancing Cheetah task.

To address this, we sample likely past trajectories, instead of enumerating all possible past trajectories.

By alternating between predicting past actions, and predicting past states from which those actions were taken, we can simulate trajectories arbitrarily fa…

Our method, Equivalent-Policy Invariant Comparison (EPIC), allows one to evaluate a reward function by computing how similar it is to other reward functions.

EPIC can be used to benchmark reward learning algorithms by comparing learned reward functions to a ground-truth reward.

It can also be used to validate learned reward functions prior to deployment, by comparing them against reward functions learned via different techniques or data sources.

EPIC is a new way to evaluate reward functions and reward learning algorithms by comparing how similar reward functions are to one another.

Most significantly, EPIC can only compare reward functions to one another, and cannot tell you what a particu…

The Importance of Hyperparameter Optimization for Model-based Reinforcement LearningModel-based reinforcement learning (MBRL) is a variant of the iterative learning framework, reinforcement learning, that includes a structured component of the system that is solely optimized to model the environment dynamics.

MBRLModel-based reinforcement learning (MBRL) is an iterative framework for solving tasks in a partially understood environment.

With that data, the agent creates a structured learning tool – a dynamics model – to reason about the world.

Automated Machine Learning (AutoML) is a field dedicated to the study of using machine learning algorithms to tune our machine learning tools.

Thi…

Pretrained Transformers as Universal Computation EnginesTransformers have been successfully applied to a wide variety of modalities: natural language, vision, protein modeling, music, robotics, and more.

This enables the models to utilize generalizable high-level embeddings trained on a large dataset to avoid overfitting to a small task-relevant dataset.

To illustrate this, we take a pretrained transformer language model and finetune it on various classification tasks: numerical computation, vision, and protein fold prediction.

Furthermore, we find the language-pretrained frozen transformers converge faster than the randomly initialized frozen transformers, typically by a factor of 1-4x, in…

Our analysis provides a theoretically-justified explanation for the empirical robustness of MaxEnt RL, and proves that MaxEnt RL is itself a robust RL algorithm.

In the rest of this post, we’ll provide some intuition into why MaxEnt RL should be robust and what sort of perturbations MaxEnt RL is robust to.

Standard RL MaxEnt RL Trained and evaluated without the obstacle: Trained without the obstacle, but evaluated with the obstacle:TheoryWe now formally describe the technical results from the paper.

Standard RL MaxEnt RL Evaluation on adversarial perturbationsMaxEnt RL is robust to adversarial perturbations of the hole (where the robot inserts the peg).

ConclusionIn summary, our paper sho…

Maximum Entropy RL (Provably) Solves Some Robust RL ProblemsNearly all real-world applications of reinforcement learning involve some degree of shift between the training environment and the testing environment.

In a recent paper, we prove that every MaxEnt RL problem corresponds to maximizing a lower bound on a robust RL problem.

In the rest of this post, we’ll provide some intuition into why MaxEnt RL should be robust and what sort of perturbations MaxEnt RL is robust to.

ConclusionIn summary, this paper shows that a commonly-used type of RL algorithm, MaxEnt RL, is already solving a robust RL problem.

We do not claim that MaxEnt RL will outperform purpose-designed robust RL algorithms.

Self-Supervised Policy Adaptation during DeploymentOur method learns a task in a fixed, simulated environment and quickly adapts to new environments (e.g.

Assuming that gradients of the self-supervised objective are sufficiently correlated with those of the RL objective, any adaptation in the self-supervised task may also influence and correct errors in the perception and decision-making of the policy.

SAC+IDM is a Soft Actor-Critic (SAC) policy trained with an Inverse Dynamics Model (IDM), and SAC+IDM (PAD) is the same policy but with the addition of policy adaptation during deployment on the robot.

Policy adaptation is especially effective when the test environment differs from the traini…

The Successor Representation, $\gamma$-Models,and Infinite-Horizon PredictionThe Successor Representation, Gamma-Models, and Infinite-Horizon PredictionStandard single-step models have a horizon of one.

In order to amortize this long-horizon prediction, value functions are trained with either Monte Carlo estimates of expected cumulative reward or with dynamic programming.

In contrast, value functions amortize the work of long-horizon prediction at training, so a single-step prediction (and informally, a shorter "horizon") is sufficient during testing.

As opposed to incrementing one timestep into the future with every prediction, \(\gamma\)-model rollout steps have a negative binomial distri…

Does GPT-2 Know Your Phone Number?

Yet, OpenAI’s GPT-2 language model does know how to reach a certain Peter W --- (name redacted for privacy).

Maybe the model memorized credit card numbers, or maybe it memorized entire book passages, or even code snippets.

For example, we retain any sample on which GPT-2 assigns a much higher likelihood than a different language model (e.g., a smaller variant of GPT-2).

Does Training Language Models Infringe on Copyright?

Offline Reinforcement Learning: How Conservative Algorithms Can Enable New ApplicationsDeep reinforcement learning has made significant progress in the last few years, with success stories in robotic control, game playing and science problems.

As shown in the figure below, offline RL requires learning skills solely from previously collected datasets, without any active environment interaction.

COG: Learning Skills That Generalize via Offline RLCOG is an algorithmic framework for utilizing large, unlabeled datasets of diverse behavior to learn generalizable policies via offline RL.

Like supervised learning methods, offline RL algorithms can also “overfit” as a result of excessive trainin…

Learning State Abstractions for Long-Horizon PlanningMany tasks that we do on a regular basis, such as navigating a city, cooking a meal, or loading a dishwasher, require planning over extended periods of time.

Two-way consistency can be viewed as a generalization of value irrelevance to the goal-conditioned setting.

Furthermore, our main theorem tells us that we can merge nodes according to two-way consistency while preserving the graph’s quality.

Overall, we found that state aggregation with two-way consistency resulted in substantially more robust plans over the prior state-of-the-art.

How can two-way consistency be utilized beyond the scope of graphical-based planning methods?

EvolveGraph: Dynamic Neural Relational Reasoning for Interacting SystemsMulti-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems.

In this work, we took a step forward to handle these challenges and provided a generic framework for trajectory prediction with dynamic relational reasoning for multi-agent systems.

Dynamic Interaction Graph LearningIn many situations, the interaction patterns recognized from the past time steps are likely not static in the future.

Summary and Broader ApplicationsWe introduce EvolveGraph, a generic trajectory prediction framework with dynamic relational reasoning, which can handle evolving inte…

EvolveGraph: Dynamic Neural Relational Reasoning for Interacting SystemsMulti-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems.

In this work, we took a step forward to handle these challenges and provided a generic framework for trajectory prediction with dynamic relational reasoning for multi-agent systems.

Dynamic Interaction Graph LearningIn many situations, the interaction patterns recognized from the past time steps are likely not static in the future.

The model is expected to learn the criterion by itself and perform both edge type prediction and trajectory prediction.

Summary and Broader ApplicationsWe introduce …

Training on Test Inputs with Amortized Conditional Normalized Maximum LikelihoodCurrent machine learning methods provide unprecedented accuracy across a range of domains, from computer vision to natural language processing.

Different classifiers that work well on the training set can give different predictions on the query point.

The minimax optimal distribution given a particular input $x$ and training set $\mathcal D$ can be explicitly computed as follows:For each label $y$, we append $(x,y)$ to our training set and compute the new optimal parameters $\hat \theta_y$ for this modified training set.

Figure 2: Here, we show the heatmap of CNML predictions (left) and the predictions of the tr…

Training on Test Inputs with Amortized Conditional Normalized Maximum LikelihoodCurrent machine learning methods provide unprecedented accuracy across a range of domains, from computer vision to natural language processing.

Different classifiers that work well on the training set can give different predictions on the query point.

The minimax optimal distribution given a particular input $x$ and training set $\mathcal D$ can be explicitly computed as follows:For each label $y$, we append $(x,y)$ to our training set and compute the new optimal parameters $\hat \theta_y$ for this modified training set.

Figure 2: Here, we show the heatmap of CNML predictions (left) and the predictions of the tr…

The steps to implement the solution are as follows:Create and export Amazon HealthLake data.

Create and export Amazon HealthLake dataAs a first step, create a data store using Amazon HealthLake either via the Amazon HealthLake console or the AWS Command Line Interface (AWS CLI).

Connecting the output of Amazon HealthLake to Amazon Kendra and Neptune gives you the ability to build a cognitive search and a health knowledge graph to power your intelligent application.

Deploy this solution using Amazon HealthLake in your AWS account by deploying the example on GitHub.

Dr. Taha Kass-Hout is Director of Machine Learning and Chief Medical Officer at Amazon Web Services, and leads our Health AI str…

We’re happy to announce that Amazon Transcribe now allows you to enable and configure partial results stabilization for streaming audio transcriptions.

On the other hand, a low stability level leads to more accurate transcription results, but the partial transcription results are more likely to change.

Access partial results stabilization via the Amazon Transcribe consoleTo start using partial results stabilization on the Amazon Transcribe console, complete the following steps:On the Amazon Transcribe console, make sure you’re in a Region that supports Amazon Transcribe Streaming.

Medium – Provides partial transcription results that have a balance between stability and accuracy– Provides pa…

Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products.

Post subscribers live busy lives with limited time to read the news.

The Post started testing other options and ended up choosing Amazon Polly because of its high-quality automated voices.

For more information, see What Is Amazon Polly?

and log in to the Amazon Polly console to try it out for free.

In this post, I go through the steps of creating an end-to-end machine vision solution that identifies visual anomalies in products using Amazon Lookout for Vision.

When a brick on the belt breaks a light beam, the device takes a photo and sends it to Amazon Lookout for Vision for anomaly detection.

The following diagram illustrates the architecture of our anomaly detection solution, which uses Amazon Lookout for Vision, Amazon Simple Storage Service (Amazon S3), and a Raspberry Pi.

Amazon Lookout for Vision is a machine learning (ML) service that uses machine vision to help you identify visual defects in products without needing any ML experience.

ConclusionNow you know how to use Amazon L…

We combine this content with metadata automatically generated using Amazon Comprehend Medical, into a unified Amazon Kendra index to make it searchable.

Solution overviewWe take a two-step approach to custom content enrichment during the content ingestion process:Identify the metadata for each document using Amazon Comprehend Medical.

Ingest the document along with the metadata in the search solution based on an Amazon Kendra index.

These are defined as custom attributes in the CloudFormation template that we used to create the Amazon Kendra index.

This example used the entities detected by Amazon Comprehend Medical to generate the Amazon Kendra metadata.

In our previous post, we described how to translate documents using the real-time translation API from Amazon Translate and AWS Lambda.

This event-driven architecture shows the flow of actions when a new document lands in the input Amazon Simple Storage Service (Amazon S3) bucket.

In addition to Amazon S3 costs, the solution incurs usage costs from Amazon Translate, Lambda, and Step Functions.

For more information, see Amazon Translate pricing, Amazon S3 pricing, AWS Lambda pricing, and AWS Step Functions pricing.

For more information, see the Amazon Translate Developer Guide and Amazon Translate resources.

Genworth’s Advanced Analytics team engaged in an AWS Data Lab program led by Data Lab engineers and solutions architects.

Component 2: ML batch inferenceGenworth’s Advanced Analytics team has already been using ML on premises.

SageMaker batch transform manages the compute resources, installs the ML model, handles data transfer between Amazon S3 and the ML model, and easily scales out to perform inference on the entire dataset.

In this post, we showed you how easy it is to build a serverless ML pipeline at scale with AWS Data Analytics and ML services.

Genworth, Genworth Financial, and the Genworth logo are registered service marks of Genworth Financial, Inc. and used pursuant to license.

Unlike general-purpose machine learning (ML) packages, Amazon Fraud Detector is designed specifically to detect fraud.

Now, you can generate batch predictions in Amazon Fraud Detector to quickly and easily evaluate a large number of events for fraud.

Perform a batch prediction job through the Amazon Fraud Detector console.

Create and publish a detectorYou can create and publish a detector version using the Amazon Fraud Detector console or via the APIs.

For more information about Amazon Fraud Detector, including links to additional blog posts, sample notebooks, user guide, and API documentation, see Amazon Fraud Detector.

In this post we’ll demonstrate how you can automatically scale your Amazon Kendra index based on a time schedule using Amazon EventBridge and AWS Lambda.

DEFAULT_UNITS – The number of query processing units that your Amazon Kendra Enterprise Edition requires to operate at minimum capacity.

ADDITIONAL_UNITS – The number of query capacity units you require at those times where additional capacity is required.

This allows your index to scale up with the additional query capacity units at 7 AM and scale down at 8 PM.

ConclusionIn this post, you deployed a mechanism to automatically scale additional query processing units for your Amazon Kendra Enterprise Edition index.

This is the first in a two-part series on the Amazon SageMaker Ground Truth hierarchical labeling workflow and dashboards.

AWS services used to implement this solutionThis solution creates and manages Ground Truth labeling jobs to label video frames using multiple types of annotations.

The first post covers the Step Functions workflow that automates advanced ML data labeling workflows using Ground Truth for chaining and hierarchical label taxonomies.

CheckForFirstLevelCompleteThis step waits for the FIRST_LEVEL Ground Truth labeling jobs triggered from the TriggerLabelingFirstStep .

For more information about the data lake for Ground Truth dataset annotations and worker metrics from Ground …

To overcome these manual processes, we have AWS AI services such as Amazon Textract and Amazon Comprehend.

Amazon Simple Notification Service (Amazon SNS) – A fully managed messaging service that is used by Amazon Textract to notify upon completion of extraction process.

Deploy the architecture with AWS CloudFormationYou deploy an AWS CloudFormation template to provision the necessary AWS Identity and Access Management (IAM) roles, services, and components of the solution, including Amazon S3, Lambda, Amazon Textract, Amazon Comprehend.

Postprocess the Amazon Textract response to segment paragraphsWhen the document is submitted to Amazon Textract for text detection, we get pages, lines, wor…

With AWS Inferentia you can also achieve out-of-the-box highest performance and lowest cost on opensource NLP models, without the need for customizations.

To maximize inference performance of Hugging Face models on AWS Inferentia, you use AWS Neuron PyTorch framework integration.

This performance boost comes with minimum impact on latency, because AWS Inferentia is optimized to maximize throughput at small batch sizes.

Learn more about the AWS Inferentia chip and the Amazon EC2 Inf1 instances to get started running your own custom ML pipelines on AWS Inferentia using the Neuron SDK.

He helps customers use AWS Inferentia and the AWS Neuron SDK to accelerate and scale ML workloads in AWS.

User requests go through Amazon API Gateway to Step Functions, which is responsible for orchestrating the training or HPO (Step 3).

Create a step for HPO and training in Step FunctionsTraining a model for inference using Step Functions requires multiple steps:Create a training job.

We orchestrate both model training and HPO using Step Functions.

Finally, we have states for updating status to ERROR (one for HPO and another one for model training).

We learned how to orchestrate training, HPO, and endpoint creation using Step Functions.

Amazon Forecast just launched the ability to hierarchically delete resources at a parent level without having to locate the child resources.

Previously, it was difficult to delete resources while building your forecasting system because you had to delete the child resources first, and then delete the parent resources.

Delete a forecast resourceFor a forecast resource without child resources, the following dialog is displayed.

When a forecast resource has underlying child resources such as forecast export jobs, the following dialog is displayed.

Delete dataset import job, predictor backtest export job, or forecast export job resourcesThe dataset import job, predictor backtest export job, and…

After the retailer picks a candidate location, they consider a surrounding “drive-time trade area”, or more formally, isochrone.

Like all data visualization projects, it will take a few iterations to dial in.

We started out by prototyping the workflow using a notebook to create a PoC workflow to compute isochrones.

ConclusionBeing able to click on any point in the continental US and, within seconds, compute both the drive time radius and demographic information within is impressive.

Their ability to seamlessly provide end-to-end acceleration from notebook prototype to stand-alone visualization application enables interactive data science at the speed of thought.

GeForce NOW ensures your favorite games are automatically up to date, avoiding game updates and patches.

Here’s an overview on how the service keeps your library game ready at all times.

Updating Games for All GeForce NOW MembersWhen a gamer downloads an update on their PC, all that matters is their individual download.

Meaning you’re game ready.

Eliminate Waiting for More GamingOur goal is to do all of the patching and maintenance behind the scenes — so that when you’re ready to play, you’re game ready and playing instantly.

New NVIDIA Studio laptops from Dell, HP, Lenovo, Gigabyte, MSI and Razer were announced today as part of the record-breaking GeForce laptop launch.

GeForce RTX 3050 Ti and 3050 Studio laptops are perfect for graphic designers, photographers and video editors, bringing high performance and affordable Studio laptops to artists and students.

The new line of NVIDIA Studio laptops introduces a wider range of options, making finding the perfect system easier than ever.

GeForce RTX 30 Series and NVIDIA RTX professional Studio laptops save time (and money) by enabling creators to complete creative tasks faster.

Both, along with the new Studio laptops, are supported by the latest Studio Driver.

‘Meet the Researcher’ is a series in which we spotlight different researchers in academia who use NVIDIA technologies to accelerate their work.

This month we spotlight Marco Aldinucci, Full Professor at the University of Torino, Italy, whose research focuses on parallel programming models, language, and tools.

He has participated in over 30 EU and national research projects on parallel computing, attracting over 6M€ of research funds to the University of Torino.

I am specifically interested in what I like to call “the modernization of HPC applications,” which is the convergence of HPC and AI, but also all the methodologies needed to build portable HPC applications running on the compute con…

Recently, at GTC21, the NVIDIA CloudXR team ran a Connect with Experts session about the CloudXR SDK.

VR and AR director David Weinstein and senior manager Greg Jones from the NVIDIA CloudXR team provided answers to the top questions:How do I get started with CloudXR?

The CloudXR SDK provides sample CloudXR clients (including source code) for a variety of client devices.

For the client side, CloudXR has been tested with HTC Vive, HTC Vive Pro, HTC Focus Plus, Oculus Quest, Oculus Quest 2, Valve Index, and HoloLens2.

CloudXR configurationsWhat type of applications run with NVIDIA CloudXR?

To accomplish this in computers, the first step is to determine the root cause of any type of failure or error.

A complete example of a root cause workflow can be found in the RAPIDS CLX GitHub repository.

NRC (New Root Causes): 65 new lines that were marked as ordinary are predicted to be root causes.

KRC (Known Root Causes): This is the number of lines correctly marked as root cause.

Identifying the root cause of outages imposes a significant cost, both in terms of dollars spent and person-hours.

TLDR: Learn how to use RAPIDS, HuggingFace, and Dask for high-performance NLP.

Deep learning NLP models can provide fantastic performance for tasks like named-entity recognition (NER), sentiment classification, and text summarization.

An NLP pipeline often involves the following steps:Pre-processingTokenizationInferencePost Inference ProcessingFigure 1: NLP workflow using Rapids and HuggingFace.

GPU Subword TokenizationWe first introduced the GPU BERT subword tokenizer in a previous blog as part of CLX for cybersecurity applications.

Once our inputs are tokenized using the subword tokenizer, they can be fed into NLP DL models like BERT for inference.

To meet the computational demands for large-scale DL recommender systems, NVIDIA introduced Merlin – a Framework for Deep Recommender Systems.

Specifically, this blog post explains the booking.com RecSys challenge competition goals, exploratory data analysis, feature preprocessing and extraction, the algorithms used, model training and validation.

Exploratory Data AnalysisExploratory data analysis (EDA) is performed before, during, and after feature engineering to understand the dataset better.

Machine Learning Algorithms Used by the Winning TeamEnsemble methods combine multiple machine learning algorithms to obtain a better model.

Figure 14: During masked language sequence model training, …

NVIDIA CUDA-X AI is a deep learning software stack for researchers and software developers to build high performance GPU-accelerated applications for conversational AI, recommendation systems and computer vision.

Learn what’s new in the latest releases of the CUDA-X AI tools and libraries.

NVIDIA Jarvis Open BetaAt GTC, NVIDIA announced major capabilities to the fully accelerated conversational AI framework.

Triton Inference Server 2.7At GTC, NVIDIA announced Triton Inference Server 2.9.

New and Updated Partner Software Matlab : The latest release highlights simplified workflows for the development of deep learning, autonomous systems, and automotive solutions.

Postal Service had a need to identify and track items in its torrent of more than 100 million pieces of daily mail.

An AI Platform at the EdgeIt turns out edge AI is a kind of stage for many great performances.

That saves a lot of time for edge AI systems like the ECIP network of almost 200 distributed servers.

Pictured at top: Postal Service employees perform spot checks to ensure packages are properly handled and sorted.

Postal Service.

And since it’s the first week of the month, this week’s GFN Thursday is all about the games members can look forward to joining the service this month.

In total, we’re adding 61 games to the GeForce NOW library in May, including 17 coming this week.

Joining This WeekThis week’s additions include games from Remedy Entertainment, a classic Wild West FPS and a free title on Epic Games Store.

Call of Juarez: Gunslinger (Steam) From the dust of a gold mine to the dirt of a saloon, Call of Juarez Gunslinger is a real homage to Wild West tales.

Pine (Free on Epic Games Store until May 13) An open-world action-adventure game set in a simulated world in which humans never reached the top of the food…

Grant Gelven, a machine learning engineer at Walmart Global Tech, joined NVIDIA AI Podcast host Noah Kravitz for the latest episode of the AI Podcast.

The improvements that machine learning have made to Walmart’s retail predictions reach even farther than streamlining business operations.

Jared Dame, Z by HP’s director of business development and strategy for AI, data science and edge technologies, speaks about the role HP’s workstations play in cutting-edge AI and data science.

Tune in to the AI PodcastGet the AI Podcast through iTunes, Google Podcasts, Google Play, Castbox, DoggCatcher, Overcast, PlayerFM, Pocket Casts, Podbay, PodBean, PodCruncher, PodKicker, Soundcloud, Spotify, Stitche…

BMW today unveiled a virtual art installation that projects AI-generated artwork onto a virtual rendition of the automaker’s 8 Series Gran Coupe.

Dubbed “The Ultimate AI Masterpiece,” the installation harnessed NVIDIA StyleGAN — a generative model for high-resolution images — to create original artwork projection-mapped onto the virtual vehicle.

The project debuts in conjunction with the contemporary art festival Frieze New York, and marks the 50th year of cultural engagement by the BMW Group.

They’re informed by his work, but they’re also unique.”Developed by NVIDIA Research, StyleGAN has been adopted for digital storytelling, art exhibits, manga illustrations and reimagined historical por…

People upload hundreds of millions of videos to Facebook every day.

From a pure computing perspective, applying the most advanced codecs to every video uploaded to Facebook would be prohibitively inefficient.

A relatively small percentage (roughly one-third) of all videos on Facebook generate the majority of overall watch time.

The impact of the new video encoding modelIn addition to improving viewer experience with newly uploaded videos, the new model can identify older videos on Facebook that should have been encoded with more advanced encodings and route more computing resources to them.

The improved compression has also allowed people on Facebook with limited data plans, such as those i…

Models for meaningful interactions and quality content are powered by state-of-the-art ML, such as multitask learning on neural networks, embeddings, and offline learning systems.

We are sharing new details of how we designed an ML-powered News Feed ranking system.

Building a ranking algorithmTo understand how this works, let’s start with a hypothetical person logging in to Facebook: We’ll call him Juan.

On the other hand, perhaps Juan has previously engaged more with video content than photos, so the like prediction for Wei’s cocker spaniel photo might be lower.

Approximating the ideal ranking function in a scalable ranking systemNow that we know the theory behind ranking (as exemplified t…

This is why we need to make sure our server hardware is reliable and that we can manage server hardware failures at our scale with as little disruption to our services as possible.

And we automate root cause analysis for hardware and system failures at scale to get to the bottom of issues quickly.

How we handle hardware remediationWe periodically run a tool called MachineChecker on each server to detect hardware and connectivity failures.

If the issue requires manual repair from a technician, the system creates a ticket in our repair ticketing system.

We have deployed this analyzer widely inside Facebook for the RCA on hardware component failure rate, unexpected server reboots, and software…

What’s New:PPL Bench is an open source benchmark framework for evaluating probabilistic programming languages (PPLs) used for statistical modeling.

PPL Bench does this by using predictive log likelihood as a standard measurement.

PPL Bench also reports other common metrics used to evaluate statistical models, including effective sample size, R-hat, and inference time.

We hope that community contributions will help grow and diversify PPL Bench and encourage wider industrial deployments of PPLs.

Read the full paper:PPL Bench: Evaluation framework for probabilistic programming languagesGet it on GitHub:PPL Bench

The U.K.’s Royal Academy of Engineering has elected Facebook Research Scientist Mark Harman as a Fellow for his achievements in academia and industry, including his work on search-based software engineering (SBSE), intelligent software testing tools, and web-enabled simulation (WES) approaches.

SBSE is an approach that uses search-based optimization algorithms to find solutions to highly complex software engineering problems.

Using the technique allows for smoother testing, design, and project management in software engineering.

For the next 25 years, he worked solely in academia, where he wrote, edited, and reviewed hundreds of papers, and authored books about software testing and programm…

What the research is:We’ve built a data classification system that uses multiple data signals, a scalable system architecture, and machine learning to detect semantic types within Facebook at scale.

This is important in situations where it’s necessary to detect where an organization’s data is stored in many different formats across various data stores.

In these cases, a classification system enables organizations to automatically enforce privacy- and security-related policies, such as access control policies.

Why it matters:Organizations generally have a well-defined set of privacy policies aimed at ensuring that people’s privacy is respected.

Read the full paper:Secure and scalable data cl…

Today, we are excited to release Ludwig version 0.3, featuring several updates that take our framework to the next level.

Finding the parameters that yield the best performance on a data set is a time-consuming job that can be automated by hyper-parameter optimization techniques.

The hyper-parameter optimization architecture is easy to expand and we plan to integrate with additional samplers and executors in the near future, like RayTune.

Nonetheless, Ludwig version 0.3 ships with a revamped, more modular, and easier-to-extend backend based on TensorFlow 2, lending to greater flexibility all around.

Moving forwardWith the addition of hyper-parameter optimization, Ludwig version 0.3 has soli…

Log-TransformDifferencing works very well with practical time series data.

It’s a class of models that captures a suite of different standard temporal structures in time series data.

AR is auto regressive, which says we want to predict the time series values based on some periods in the past.

data = df[[ 'Date' , 'Close' ]] data.columns = [ 'ds' , 'y' ] data = df[[ 'Date' , 'Close' ]] data.columns = [ 'ds' , 'y' ]Let’s add the parameters to our model.

model = Prophet(**model_params) data[ 'cap' ] = data[ 'y' ].max() + data[ 'y' ].std() * 0.05At this point, we can fit the model to the data.

Kubeflow Metadata helps data scientists track and manage the huge amounts of metadata produced by their workflows.

Let’s explore why that is, and then we’ll take a look at Kubeflow Metadata and a few alternative tools.

However, there’s no surefire way to tell if the new model is fairly comparing the previous version – unless you store metadata.

Finally, run the steps in the notebook in order to install and use the Kubeflow Metadata SDK.

The Kubeflow Metadata SDK has these predefined types used to describe your ML workflows:Dataset: metadata for a dataset, both input and outputExecution: metadata for a run/ execution in an ML modelMetrics: metadata for metrics in an ML modelModel: metadata f…

Computer vision is a rapidly developing field where tremendous progress is being made, but there are still many challenges that computer vision engineers need to tackle.

Tasks that Computer Vision solvesImage classificationSource: 9 Applications of Deep Learning for Computer Vision by Jason BrownleeIn this article, we’ll focus on creating image classifiers.

Image classifier creation: real-life project exampleProject descriptionAll right, let’s take advantage of the pre-trained models available in Keras, and solve a real-life computer vision problem.

For a custom image classifier, we’ll need to create our own top part of the network to reflect the number of classes we have;input_shape is a t…

How to interpret an ML model?

As a result, there are different ways to interpret them.

Let’s create a model to interpret.

We’ll do a short walkthrough of the model creation steps, and then we’ll focus on different model-agnostic tools and frameworks to interpret the created model, rather than solve the actual problem.

So, we can interpret the prediction from the perspective of both classes.

The forget gate decides when to remember, and when to skip the inputs from previous hidden states.

This encoder will take a variable-length input and convert it into a hidden state with a fixed length.

Vanilla Transformer with a segment of 4 [source]Transformer-XLThe transformer XL is a newer version from the Transformer (it’s extra long).

It is derived from the vanilla Transformer, but introduces the recurrence mechanism and relative positional encoding.

The Model solves the problems introduced in the vanilla transformer model, and overcomes the long-term dependency problem.

Privacy Are there any data privacy, regulatory or compliance constraints for using the data to build and deploy AI models?

Research validation Is there significant academic research on this topic to understand what kind of AI models and approaches might be relevant?

A checklist of data, model and business questions to validate a business problem with a potential AI solution.

In this section, I’ll describe the details of specific planning steps essential to build a successful AI product.

Deep learning models Is there enough (unstructured) data to leverage more sophisticated neural networks and deep learning models?

In this post, we will discuss the evolution of a ML project, how roles are distributed in an ML team, and how they collaborate.

Roles in an ML team align very much with how a project is designed.

The business team first estimates the amount of savings in shipping an ML model would generate.

Hence, a matured ML team typically have the following –Data AnalystsData EngineersData ScientistResearch/Applied ScientistsML EngineersDevelopersWe will discuss each of these roles in detail.

Final thoughtsIn conclusion, the ML ecosystem has developed enough to identify key roles and responsibilities involved in an ML project.

Computer vision has gained tremendous traction thanks to state-of-the-art tools and technologies that seem to be released every day.

Neptune is hosted on the cloud, so you don’t need any setup, and you can access your computer vision experiments anytime, anywhere.

OpenCVOpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning library.

Other features provided by OpenVINO include:Supports CNN-based deep learning inference on the edgeShips with optimized computer vision functions for OpenCV and OpenCLReady-made solutions for particular tasksApart from open-source computer vision tools, there are also ready-made solutions that can be used at various st…

One of the great things to come from it is the abundance of efficient machine learning tools that help us do more with less effort.

Scikit-learn—wide collection of tools for building modelsScikit-learn is an open-source library with a wide collection of tools for building machine learning models and solving statistical modeling problems.

It’s not explicitly meant for machine learning, but it comes in handy for managing teamwork in ML engineering teams.

Jupyter Notebook is undoubtedly a useful tool that you’re probably already using to enhance productivity in your machine learning projects.

Google’s sophisticated Neural Architecture Search technology and deep transfer learning technology per…

Performance metrics are a part of every machine learning pipeline.

All machine learning models, whether it’s linear regression, or a SOTA technique like BERT, need a metric to judge performance.

Every machine learning task can be broken down to either Regression or Classification, just like the performance metrics.

It essentially finds the average of the squared difference between the target value and the value predicted by the regression model.

Confusion Matrix is not exactly a performance metric but sort of a basis on which other metrics evaluate the results.

Specifically in the network evaluation step, it’s crucial to select and define an appropriate performance metric – essentially a function that judges your model performance, including Macro F1 Score.

These two points combined explain why loss function and performance metrics are usually optimized in opposite directions.

Predictive models are developed to achieve high accuracy, as if it were the ultimate authority in judging classification model performance.

With all being said, what’s the correct way to implement a macro F1 metric?

Let’s compare the difference between these two approaches we just experimented with, a.k.a., custom F1 metric vs. NeptuneMetrics callback:We can clearly see that…

It worked very well in the beginning, but soon I realized that setting up and managing spreadsheets with experiment meta-data requires loads of additional work.

In this post, I will share my story of switching from spreadsheets to Neptune for experiment tracking.

The figure illustrates ML experiment tracking with spreadsheets.

Switching from spreadsheets to NeptuneA few months ago, our team was working on a Cassava Leaf Disease competition and used Google spreadsheets for experiment tracking.

Tracking experiment meta-data with spreadsheets is much better than not doing any tracking.

All of these separate parts together form a machine learning project life cycle, and that’s exactly what we’re going to talk about in this article.

Before any machine learning happens, we need to move from monetary units and switch to other KPIs that our machine learning team can understand.

Discussing this list with the machine learning team, they picked a few tasks that can be solved via supervised machine learning algorithms if proper data is available.

Consider reading this research paper if you’re curious how annotation can impact the overall machine learning life cycle.

ConclusionsBy now you should have a solid understanding for the entire machine learning project life cycle.

In this post, I’ll share with you my library of environments that support training reinforcement learning (RL) agents.

Bbsuite is a collection of carefully designed experiments that investigate the core capabilities of a reinforcement learning (RL) agent with two main objectives.

DThe dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation.

OGym, besides being the most widly known benchmark, is an amazing toolkit for developing and comparing reinforcement learning algorithms.

Researchers, however, can use the provided simple-to-use Python API to train Agents using reinforcement learning, imitation le…

#icml #machinelearning #conference In a controversial move, ICML Area Chairs were instructed to raise the bar on acceptance to drop the acceptance rate by 10% from the previous trajectory. This raises a lot of questions about the pains of an academic peer review system under the load of an exponentially increasing field of study. Who draws the short stick? Usually not the big corporations. References:

https://www.reddit.com/r/MachineLearning/comments/n243qw/d_icml_conference_we_plan_to_reduce_the_number_of/

https://twitter.com/tomgoldsteincs/status/1388156022112624644

https://twitter.com/ryan_p_adams/status/1388164670410866692

https://github.com/lixin4ever/Conference-Acceptance-Rate Links:

…

#involution #computervision #attention Convolutional Neural Networks (CNNs) have dominated computer vision for almost a decade by applying two fundamental principles: Spatial agnosticism and channel-specific computations. Involution aims to invert these principles and presents a spatial-specific computation, which is also channel-agnostic. The resulting Involution Operator and RedNet architecture are a compromise between classic Convolutions and the newer Local Self-Attention architectures and perform favorably in terms of computation accuracy tradeoff when compared to either. OUTLINE:

0:00 - Intro & Overview

3:00 - Principles of Convolution

10:50 - Towards spatial-specific computations

17:…

#mixer #google #imagenet Convolutional Neural Networks have dominated computer vision for nearly 10 years, and that might finally come to an end. First, Vision Transformers (ViT) have shown remarkable performance, and now even simple MLP-based models reach competitive accuracy, as long as sufficient data is used for pre-training. This paper presents MLP-Mixer, using MLPs in a particular weight-sharing arrangement to achieve a competitive, high-throughput model and it raises some interesting questions about the nature of learning and inductive biases and their interaction with scale for future research. OUTLINE:

0:00 - Intro & Overview

2:20 - MLP-Mixer Architecture

13:20 - Experimental Resul…

#machinelearning #ai #phd Done with my PhD in Machine Learning at ETH Zurich.

On to new lands! Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

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LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/

BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary…

#dino #facebook #selfsupervised Self-Supervised Learning is the final frontier in Representation Learning: Getting useful features without any labels. Facebook AI's new system, DINO, combines advances in Self-Supervised Learning for Computer Vision with the new Vision Transformer (ViT) architecture and achieves impressive results without any labels. Attention maps can be directly interpreted as segmentation maps, and the obtained representations can be used for image retrieval and zero-shot k-nearest neighbor classifiers (KNNs). OUTLINE:

0:00 - Intro & Overview

6:20 - Vision Transformers

9:20 - Self-Supervised Learning for Images

13:30 - Self-Distillation

15:20 - Building the teacher from t…

#aiwinter #agi #embodiedcognition The AI community has gone through regular cycles of AI Springs, where rapid progress gave rise to massive overconfidence, high funding, and overpromise, followed by these promises being unfulfilled, subsequently diving into periods of disenfranchisement and underfunding, called AI Winters. This paper examines the reasons for the repeated periods of overconfidence and identifies four fallacies that people make when they see rapid progress in AI. OUTLINE:

0:00 - Intro & Overview

2:10 - AI Springs & AI Winters

5:40 - Is the current AI boom overhyped?

15:35 - Fallacy 1: Narrow Intelligence vs General Intelligence

19:40 - Fallacy 2: Hard for humans doesn't mean …

#gpt3 #airecipe #cooking We went to the store and bought a set of completely random ingredients and had OpenAI's GPT-3 come up with a recipe, which we then cooked and ate. Our Rules:

1. All Vegan

2. Follow the recipe as closely as possible

3. We must finish our plates The Recipe:

1. Boil the potatoes and carrots.

2. In the meantime, prepare the VEGAN minced meat, or use pre-cooked soy meat. 3. Then fry the VEGAN butter, add the garlic, and the mushrooms, and stir for 2 minutes. 4. Add the soy cream, stir and cook for three minutes. 5. Add the pickles, tomatoes, and beans, stir and simmer for five minutes. 6. Cut the bread in small squares and fry in the vegan butter until golden brown.

7. C…

#gtc21 #schmidhuber #nvidia We analyze Jürgen Schmidhuber's talk at GTC'21 (link below). He goes through the past, present, and future of AI, starting literally at the beginning of the universe. He describes interesting patterns in the history of technological developments leading to the field of AI of today. On the way, he describes important inventions by himself and others, and he presents an interesting outlook into what superintelligent AIs can do in the not so distant future. OUTLINE:

0:00 - Intro & Overview

1:50 - Quarter-pattern in the history of the universe

7:45 - History of modern AI

14:20 - First successes of backpropagation

17:45 - ResNets are a special case of HighwayNets

20:1…

#nerf #neuralrendering #deeplearning View Synthesis is a tricky problem, especially when only given a sparse set of images as an input. NeRF embeds an entire scene into the weights of a feedforward neural network, trained by backpropagation through a differential volume rendering procedure, and achieves state-of-the-art view synthesis. It includes directional dependence and is able to capture fine structural details, as well as reflection effects and transparency. OUTLINE:

0:00 - Intro & Overview

4:50 - View Synthesis Task Description

5:50 - The fundamental difference to classic Deep Learning

7:00 - NeRF Core Concept

15:30 - Training the NeRF from sparse views

20:50 - Radiance Field Volume …

#minecraft #neuralnetwork #backpropagation I built an analog neural network in vanilla Minecraft without any mods or command blocks. The network uses Redstone wire power strengths to carry the signal through one hidden layer, including nonlinearities, and then do automatic backpropagation and even weight updates. OUTLINE:

0:00 - Intro & Overview

1:50 - Redstone Components Explained

5:00 - Analog Multiplication in Redstone

7:00 - Gradient Descent for Square Root Computation

9:35 - Neural Network Demonstration

10:45 - Network Schema Explained

18:35 - The Network Learns a Datapoint

20:20 - Outro & Conclusion I built this during a series of live streams and want to thank everyone who helped me …

#dreamcoder #programsynthesis #symbolicreasoning Classic Machine Learning struggles with few-shot generalization for tasks where humans can easily generalize from just a handful of examples, for example sorting a list of numbers. Humans do this by coming up with a short program, or algorithm, that explains the few data points in a compact way. DreamCoder emulates this by using neural guided search over a language of primitives, a library, that it builds up over time. By doing this, it can iteratively construct more and more complex programs by building on its own abstractions and therefore solve more and more difficult tasks in a few-shot manner by generating very short programs that solve …

In the recurring debate about bias in Machine Learning models, there is a growing argument saying that "the problem is not in the data", often citing the influence of various choices like loss functions or network architecture. In this video, we take a look at PAIR's AI Explorables through the lens of whether or not the bias problem is a data problem. OUTLINE:

0:00 - Intro & Overview

1:45 - Recap: Bias in ML

4:25 - AI Explorables

5:40 - Measuring Fairness Explorable

11:00 - Hidden Bias Explorable

16:10 - Measuring Diversity Explorable

23:00 - Conclusion & Comments AI Explorables: https://pair.withgoogle.com/explorables/ Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannic…

We build a Deep Neural Network in Minecraft

No Command Blocks

No Mods Multiplier inspiration from here: https://www.youtube.com/watch?v=Wc29p6mgRMo

World Save: https://polybox.ethz.ch/index.php/s/YnKl1hkoct6coQy Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://discord.gg/4H8xxDF

BitChute: https://www.bitchute.com/channel/yannic-kilcher

Minds: https://www.minds.com/ykilcher

Parler: https://parler.com/profile/YannicKilcher

LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/

BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the bes…

We build a Deep Neural Network in Minecraft

No Command Blocks

No Mods Multiplier inspiration from here: https://www.youtube.com/watch?v=Wc29p6mgRMo

World Save: https://polybox.ethz.ch/index.php/s/YnKl1hkoct6coQy Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://discord.gg/4H8xxDF

BitChute: https://www.bitchute.com/channel/yannic-kilcher

Minds: https://www.minds.com/ykilcher

Parler: https://parler.com/profile/YannicKilcher

LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/

BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the bes…

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Thank you for watching! Please Subscribe! Content Links:

MoCoV3: https://arxiv.org/pdf/2104.02057.pdf

Revisiting Simple Neural Probabilistic Language Models: https://arxiv.org/pdf/2104.03474.pdf

Large-scale forecasting: Self-supervised learning framework for hyperparameter tuning: https://ai.facebook.com/blog/large-scale-forecasting-self-supervised-learning-framework-for-hyper-parameter-tuning

SiT: https://arxiv.org/pdf/2104.03602.pdf

GPV-I: https://arxiv.org/pdf/2104.00743.pdf

GAN Survey: https://www.youtube.com/watch?v=3ktD752xq5k

Regularizing GANs with Limited Data: https://arxiv.org/pdf/2104.03310.pdf

Transfer vs. Meta Learning: https://arxiv.org/pdf/2104.02638.pdf

CodeTrans: https://ar…

Dienste anbieten und betreiben, z.

Personalisierte Inhalte und Werbeanzeigen können ebenfalls darauf basieren, darüber hinaus aber auch auf Aktivitäten wie Suchanfragen bei Google und Videos, die Sie sich bei YouTube ansehen.

Zu personalisierten Inhalten und Werbeanzeigen gehören beispielsweise Dinge wie relevantere Ergebnisse und Empfehlungen, eine individuelle YouTube-Startseite und Werbung, die auf Ihre Interessen zugeschnitten ist.

Klicken Sie auf „Anpassen“, um sich Ihre Möglichkeiten anzusehen.

Zu diesen gehören zum Beispiel Steuerelemente, um Cookies für die Personalisierung zu deaktivieren, oder Informationen zu Steuerelementen auf Browserebene, mit denen einige oder alle Cookies fü…

Thank you for watching! Please Subscribe! Content Links:

Recursive Classification: https://ai.googleblog.com/2021/03/recursive-classification-replacing.html

Industrial Assembly via RL: https://arxiv.org/pdf/2103.11512.pdf

Model-based RL in Healthcare: https://twitter.com/christina_x_ji/status/1374815904790421508

Can ViTs learn w/o Natural Images?: https://arxiv.org/pdf/2103.13023.pdf

Universal Compute Engines: https://bair.berkeley.edu/blog/2021/03/23/universal-computation/

DeepViT: https://arxiv.org/pdf/2103.11886.pdf

Conv Designs in Visual Transformers: https://arxiv.org/pdf/2103.11816.pdf

Scaling Local Self-Attn. for Vision: https://arxiv.org/pdf/2103.12731.pdf

Visual PT w/ Contrastive D…

This video previews the content for the next AI Weekly Update - March 29th, 2021 (#30)!

Thanks for watching and please subscribe! Content Links:

Recursive Classification: https://ai.googleblog.com/2021/03/recursive-classification-replacing.html

Industrial Assembly with RL: https://arxiv.org/pdf/2103.11512.pdf

Christina Ji's Twitter Thread on Model-Based RL in Healthcare: https://twitter.com/christina_x_ji/status/1374815904790421508

ADAPET: https://arxiv.org/pdf/2103.11955.pdf

Progress and Challenges in Long-Form Open-Domain Question Answering: https://ai.googleblog.com/2021/03/progress-and-challenges-in-long-form.html

Sebastian Ruder's Newsletter: http://newsletter.ruder.io/issues/qa-how-di…

Thanks for watching! Please Subscribe! Content Links:

Revisiting ResNets: https://arxiv.org/pdf/2103.07579.pdf

Is it Enough to Optimize CNN Architectures on imageNet? https://arxiv.org/pdf/2103.09108.pdf

Learning to Resize Images for Computer Vision Tasks: https://arxiv.org/pdf/2103.09950.pdf

Large-Scale Zero-Shot Learning: https://arxiv.org/pdf/2103.09669.pdf

Training GANs with Stronger Augmentations via Contrastive Discriminator: https://arxiv.org/pdf/2103.09742.pdf

Using Latent Space Regression to Analyze and Leverage Compositionality in GANs: https://arxiv.org/pdf/2103.10426.pdf

Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction: https://sites.google.com/view/…

Thank you for watching! Please Subscribe! Content Links:

Behavior from the Void: https://arxiv.org/pdf/2103.04551.pdf

Barlow Twins: https://arxiv.org/pdf/2103.03230.pdf

Pretrained Transformers as Universal Compute Engines: https://arxiv.org/pdf/2103.05247.pdf

A New Lens on Understanding Generalization: https://ai.googleblog.com/2021/03/a-new-lens-on-understanding.html

Knowledge Evolution in Neural Networks: https://arxiv.org/pdf/2103.05152.pdf

COIN: https://arxiv.org/pdf/2103.03123.pdf

CheXseen: https://arxiv.org/pdf/2103.04590.pdf

Haystack: The State of Search in 2021: https://medium.com/deepset-ai/haystack-the-state-of-search-in-2021-7388ecb15dfb

Hurdles to Long-Form QA: https://arxiv.org…

This video explains another awesome Keras Code Example, this time implementing a cutting-edge technique for Data Augmentation. In my view, what makes MixUp so interesting is that it can be applied in data domains outside of images and Computer Vision. Say for NLP or Physiological data, it is very hard to define data augmentations and here is a great framework for getting started. You may also be interested in the video I made explaining MODALS - a recent ICLR 2021 paper exploring cutting-edge domain-agnostic data augmentation. Thanks for watching, please check out the rest of the Keras Code Examples playlist! Follow Sayak Paul on Twitter: https://twitter.com/RisingSayak Content Links:

Keras…

This video explains the Keras Example of a Convolutional Autoencoder for Image Denoising. This is a relatively simple example in the Keras Playlist, I hope beginners find this useful for getting starter with Deep Learning and exciting ideas like Generative Modeling, Data Compression, or Image Denoising/Super-resolution. Thanks for watching, please check out the rest of the Keras Code Examples playlist! Content Links:

Keras Code Example: https://keras.io/examples/vision/autoencoder/

Building Autoencoders in Keras: https://blog.keras.io/building-autoencoders-in-keras.html Chapters

0:00 Welcome to Keras Code Examples!

0:44 Motivation - Denoising Autoencoders

4:00 Helper Functions

9:07 Model Co…

This video previews the content for the next AI Weekly Update for Monday March 15th, 2021! Thanks for watching and please subscribe! Content Links:

COIN: https://arxiv.org/pdf/2103.03123.pdf

Behavior from the Void: https://arxiv.org/pdf/2103.04551.pdf

Barlow Twins: https://arxiv.org/pdf/2103.03230v1.pdf

VISSL: https://vissl.ai/tutorials/Large_Scale_Training

HuggingFace Reads: https://huggingface.co/blog/long-range-transformers

Pretrained Transformers as Universal Computation Engines: https://arxiv.org/pdf/2103.05247.pdf

Igor Mordatch's Twitter Thread: https://twitter.com/IMordatch/status/1369688157558431749

Attention is NOT all you need: https://arxiv.org/pdf/2103.03404.pdf

A new lens on un…

This video explains Negative Data Augmentation, a strategy for using label-corrupting, rather than label-preserving transformations in Deep Learning. The authors test this framework for training GANs and for Contrastive Learning such as CPC and MoCo. I think this is a really exciting direction for Data Augmentation and overcoming the challenge of learning from limited labeled data, I hope you find this video useful! Content Links:

Negative Data Aug (Paper): https://arxiv.org/pdf/2102.05113.pdf

Self-Supervised Learning: The Dark Matter of Intelligence: https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence

Learning the difference that makes a difference: https:…

This video explains an interesting new paper for applying Data Augmentation to NLP. I thought the interpretation of Data Augmentation as constructing local neighborhoods was very interesting and like the way the MoCo loss compliments this intuition. I hope you found this video useful and it inspires your interest in Data Augmentation! Content Links:

Paper: https://arxiv.org/pdf/2010.08670.pdf

On the Measure of Intelligence: https://arxiv.org/pdf/1911.01547.pdf

EDA: https://arxiv.org/pdf/1901.11196.pdf

AutoAugment: https://arxiv.org/pdf/1805.09501.pdf

MoCo: https://arxiv.org/pdf/1911.05722.pdf Chapters

0:00 Beginning

1:10 Research Questions

1:50 High-Level Idea

3:30 Data Aug for Text

4:57 St…

Thank you for watching! Please Subscribe! Content Links:

Multimodal neurons (OpenAI): https://openai.com/blog/multimodal-neurons/

Multimodal neurons (Distil): https://distill.pub/2021/multimodal-neurons/

DeepDream (Wikipedia): https://en.wikipedia.org/wiki/DeepDream

CLIP (OpenAI): https://openai.com/blog/clip/

CLIP (Keras Code Examples): https://keras.io/examples/nlp/nl_image_search/

OpenAI Microscope: https://microscope.openai.com/models

Yannic Kilcher's Explanation of Multimodal Neurons: https://www.youtube.com/watch?v=Z_kWZpgEZ7w&t=622s

Wikipedia-based Image-Text Pairs: https://arxiv.org/pdf/2103.01913.pdf

WIT Dataset (GitHub repo): https://github.com/google-research-datasets/wit

Self-su…

This video walks through an implementation of Reptile in Keras using the Omniglot dataset. I was really inspired by this example, I think the Omniglot challenge of dynamically being able to recombine characters to form new alphabets is an incredibly interesting problem, connecting Human and Artificial Intelligence. I hope you found this example interesting as well, please check out the rest of the Keras Code Example playlist! Content Links:

Few-shot learning with reptile: https://keras.io/examples/vision/reptile/

On First-Order Meta Learning: https://arxiv.org/pdf/1803.02999.pdf

MAML: https://arxiv.org/pdf/1703.03400.pdf

Generative Teaching Networks: https://arxiv.org/pdf/1912.07768.pdf

Tea…

This video walks through the Keras Code Example implementation of Point Cloud Classification. I had a tough time understanding what the TNET blocks are motivated by, but if interested the paper link is below. I hope this tutorial still provided a decent enough example of what point clouds are and how to load them into a Keras workspace. Thanks for watching, please check out the rest of the Keras Code Example playlist! Content Links:

Point Cloud Classification - Keras Code Examples: https://keras.io/examples/vision/pointnet/

PointNet (Paper): https://arxiv.org/pdf/1612.00593.pdf

ModelNet (Dataset Project Page): https://modelnet.cs.princeton.edu/

Point Clouds (Wikipedia): https://en.wikipedia…

Thank you so much to everyone who has shown support and interest in bringing back the AI Weekly Update series. Here is a preview for the return, I hope these quick overviews are useful to those looking to get ahead of it and find some interesting reading over the Weekend! Content Links:

Multimodal Neurons: https://openai.com/blog/multimodal-neurons/

Wikipedia Image-Text Dataset: https://arxiv.org/pdf/2103.01913.pdf

Self-Supervised Learning: The Dark Matter of Intelligence: https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence

Principles for Tackling Distribution Shift: https://www.youtube.com/watch?v=QKBh6TmvBaw

Do Transformer Modifications Transfer? https://…

General exponentials, Love, Schrödinger, and more.

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/thanks ------------------

The Romeo-Juliet example is based on this essay by Steven Strogatz:

http://www.stevenstrogatz.com/essays/loves-me-loves-me-not-do-the-math The book shown at the start is Vladimir Arnold's (excellent) textbook on ordinary differential equations.

https://amzn.to/3dtXSwj Need a review of ordinary powers of e?

https://youtu.be/m2MIpDrF7Es Or of linear algebra?

https://youtu.be/kYB8IZa5AuE Timetable

0:00 - Definition

6:40 - Dynamics of love

13:17 - General equation

20:03 - On general rotations

22:11 - Visualizing with flow ------------------

C…

Bayes factors, aka Likelihood Ratios*, offer a very clear view of how medical test probabilities work.

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/bayes-factor-thanks The book by my friend Matt Cook about paradoxes mentioned at then end:

https://amzn.to/3aBrEzg On the topic, I can't help also mentioning another paradox book I'm rather fond of by Bunch:

https://amzn.to/3mBDSKE *As mentioned in the on-screen note at the end, while the terms "Bayes Factor" and "Likelihood Ratio" refer to the same term in this setting, where Bayes rule is used on the probability of an event with only two possible outcomes (you either have the disease or you don't), they do take…

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…

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…

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❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" is available here:

- https://arxiv.org/abs/2103.17249

- https://github.com/orpatashnik/StyleCLIP 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Cam…

❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Iterative Text-based Editing of Talking-heads Using Neural Retargeting" is available here:

https://davidyao.me/projects/text2vid/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campb…

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/authors/adv-dl/reports/An-Introduction-to-Adversarial-Examples-in-Deep-Learning--VmlldzoyMTQwODM 📝 The paper "Interactive Wood Combustion for Botanical Tree Models" is available here:

https://repository.kaust.edu.sa/bitstream/10754/626814/1/a197-pirk.pdf

https://github.com/art049/InteractiveWoodCombustion 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric …

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/authors/text-recognition-crnn-ctc/reports/Text-Recognition-With-CRNN-CTC-Network--VmlldzoxNTI5NDI 📝 The paper "Freely orientable microstructures for designing deformable 3D prints" and the Shadertoy implementation are available here:

- https://hal.inria.fr/hal-02524371

- https://www.shadertoy.com/view/WtjfzW 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Er…

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/authors/RayTune-dcgan/reports/Ray-Tune-Distributed-Hyperparameter-Optimization-at-Scale--VmlldzoyMDEwNDY 📝 The paper "A moving least square reproducing kernel particle method for unified multiphase continuum simulation" is available here:

https://cg.cs.tsinghua.edu.cn/papers/SIGASIA-2020-fluid.pdf 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, …

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/carlolepelaars/numerai_tutorial/reports/Build-the-World-s-Open-Hedge-Fund-by-Modeling-the-Stock-Market--VmlldzoxODU0NTQ 📝 The paper "Homogenized Yarn-Level Cloth" is available here:

http://visualcomputing.ist.ac.at/publications/2020/HYLC/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier B…

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/authors/image-captioning/reports/Generate-Meaningful-Captions-for-Images-with-Attention-Models--VmlldzoxNzg0ODA 📝 The paper "Animating Pictures with Eulerian Motion Fields" is available here:

https://eulerian.cs.washington.edu/ GPT-3 website layout tweet:

https://twitter.com/sharifshameem/status/1283322990625607681 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Had…

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/wandb/NSFF/reports/Overview-Neural-Scene-Flow-Fields-NSFF-for-Space-Time-View-Synthesis-of-Dynamic-Scenes--Vmlldzo1NzA1ODI 📝 The paper "OmniPhotos: Casual 360° VR Photography" is available here:

https://richardt.name/publications/omniphotos/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javie…

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/gudgud96/big-sleep-test/reports/Image-Generation-Based-on-Abstract-Concepts-using-CLIP-BigGAN--Vmlldzo1MjA2MTE 📝 The paper "Multimodal Neurons in Artificial Neural Networks" is available here:

https://openai.com/blog/multimodal-neurons/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bus…

❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "FuSta - Hybrid Neural Fusion for Full-frame Video Stabilization" is available here:

- Paper https://alex04072000.github.io/FuSta/

- Code: https://github.com/alex04072000/FuSta - Colab: https://colab.research.google.com/drive/1l-fUzyM38KJMZyKMBWw_vu7ZUyDwgdYH?usp=sharing 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, J…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "A Model for Soap Film Dynamics with Evolving Thickness" is available here:

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Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes" is available here:

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Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, …

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❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/latentspace/published-work/The-Science-of-Debugging-with-W-B-Reports--Vmlldzo4OTI3Ng 📝 The paper "Playing hard exploration games by watching YouTube" is available here:

Paper: https://papers.nips.cc/paper/7557-playing-hard-exploration-games-by-watching-youtube.pdf

Gameplay videos: https://www.youtube.com/playlist?list=PLZuOGGtntKlaOoq_8wk5aKgE_u_Qcpqhu 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

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❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/ayush-thakur/ada/reports/Train-Generative-Adversarial-Network-With-Limited-Data--Vmlldzo1NDYyMjA 📝 The paper "Training Generative Adversarial Networks with Limited Data" is available here:

Paper: https://arxiv.org/abs/2006.06676

Pytorch implementation: https://github.com/NVlabs/stylegan2-ada-pytorch 📝 My thesis with the quote is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/photorealistic-material-learning-and-synthesis/ Unofficial StyleGAN2-ADA trained on corgis (+ colab notebook):

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ML Training 2019 Artur Kuzin tells about his participation in the competition Driven Data Hakuna Ma-data: Identify Wildlife on the Serengeti with AI for Earth. He took second place. In this video, you will find out: - Overview of a training procedure on Imagenet1k from scratch

- Implementation Details of Hacks & Tricks

- The specialty of working with JPEG pictures and resize in different frameworks Presentation - https://gh.mltrainings.ru/presentations/Kuzin_DrivenDataHakuna.pdf

ML Training 2019 Yury Bolkonsky and Andrei Dukhounik tell about their participation in Kaggle Understanding Clouds from Satellite Images. The team got a silver medal. In this video you will find out:

- Thresholding is an evil, believe in your classification models

- Why you should always use modern best practices

- Why it is not recommended to use postprocessing without local validation Presentation - https://gh.mltrainings.ru/presentations/Bolkonsky_KaggleUnderstandingClouds.pdf

ML Training 2019 Yury Bolkonsky tells about his participation in Kaggle ASHRAE - Great Energy Predictor III. His team won a gold medal. In this video you will find out:

- How to create timestamp features

- Do you need to use a leak if it is noisy?

- Leak validation for the best solution

ML Training 2019 Denis Vorotyntsev won AutoSeries - AutoML competition on time-series regression. In his presentation, he talks about the competition organization, his final solution, and solutions of other top placed participants. In this video, you will find out:

- How AutoML competition differs from most common Kaggle-alike and why you should try them

- Features engineering approach for time-series tasks when you have no idea about domain

- Why validation split should emulate train-test split

- Why you should always check the code of top participants and how small bugs might drop your score Presentation - https://gh.mltrainings.ru/presentations/Vorotyntsev_CodalabAutoML.pdf

ML Training 2019 Adel Valiullin tells about his participation in the competition Kaggle Peking University/Baidu - Autonomous Driving. He won a silver medal. In this video, you will find out: - Overview of the Autonomous Vehicles problem

- Dataset description and exploration: images with 6D pose information, taken from the roof of a car, 3D models of cars and input data analysis - Problems with mAP metric and dataset in this challenge

- The implementation of CenterNet Neural Network for 6D car pose estimation

- Score boosters and other better and high scored approaches

ML Training 2019 Ilya Kibardin tells about his participation in 2 competitions: Topcoder SpaceNet 5 Challenge & Signate The 3rd Tellus Satellite Challenge. He took fourth and second places. In this video you will find out:

- Spacenet 5 challenge at Topcoder, dataset and metric description

- Overview of a UNet pipeline for road graph extraction from satellite images

- The same pipeline applied to ice segmentation at Signate

- Hacks & Tricks for better performance Presentation - https://gh.mltrainings.ru/presentations/Kibardin_Spacenet5Tellus_v2.pdf

Data Fest Online 2020

Uncertainty Estimation in ML track https://ods.ai/tracks/uncertainty-estimation-in-ml-df2020 Speaker: Bruno Mlodozeniec, University of Cambridge In this video we discuss how ensembles of models can be effectively emulated using a single “Prior Network” model via a technique called Ensemble Distribution Detection. This enables a single model to efficiently retain both the ensemble’s predictive performance and uncertainty measures at low computational and memory cost. Register and get access to the tracks: https://ods.ai/events/datafest2020

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Huawei's DaVinci AI compute architecture. Dmitrii Khizbullin, Overview of DaVinci compute architecture for Deep Learning training and inference, design choices for hardware and software layers. Register and get access to the tracks: https://ods.ai/events/datafest2020

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Speaker: Evgenii Zheltonozhskii, Technion, Israel Institute of Technology Register and get access to the tracks: https://ods.ai/events/datafest2020

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Anton Lokhmotov, ML Perf Engineer

Roman Vlasov, Huawei Engineer Register and get access to the tracks: https://ods.ai/events/datafest2020

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Difference between them and GPU. IVA TPU.

Mike Ivanov, AI Architect, IVA Technologies Register and get access to the tracks: https://ods.ai/events/datafest2020

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 ShiftCNN: Generalized Low-Precision Architecture for Inference of CNNs

DNN Feature Map Compression using Learned Representation over GF(2) E2X: A Framework to Interpret and Correct DNN Object Detector Prediction Denis Gudovskiy, Senior Deep Learning Engineer at Panasonic USA Register and get access to the tracks: https://ods.ai/events/datafest2020

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Speakers: Francesco Paci, GreenWaves Technologies, Maxim Zemlyanikin, Anastasiya Reshetova Register and get access to the tracks: https://ods.ai/events/datafest2020

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Simon Thye Andersen, RISC-V Based Neural Network Processor, ANN in FPGAs Register and get access to the tracks: https://ods.ai/events/datafest2020

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Data Fest Online 2020

Open Data Science Open Source track https://ods.ai/tracks/open-sourse-df2020 Project links: https://github.com/albumentations-team/albumentations Register and get access to the tracks: https://ods.ai/events/datafest2020

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Использование демонстраций в обучении с подкреплением часто помогает агенту в решении трудных задач. Однако чрезмерная зависимость от некачественных демонстраций может наоборот помешать обучению и сильно сместить итоговую политику от оптимальной. В своей недавней работе исследователи из команды JetBrains Research предложили новый метод, который позволяет эффективно использовать демонстрации. Данный метод комбинирует через общий Replay Buffer алгоритм Self-Imitation Learning, использующий в обучении прошлый удачный опыт агента, и алгоритмы Imitation Learning, которые выучивают поведение эксперта по его демонстрациям. На предстоящем семинаре мы разберем предложенный алгоритм и посмотрим на по…

До сегодняшнего дня поведенческие характеристики человека при чтении текста представляли интерес в основном для изучения когнитивных функций мозга. Однако сигналы мозга при чтении, детектируемые на ЭЭГ, могут также использоваться в машинном обучении для NLP. Использование частотных сигналов мозга на ЭЭГ не исследовалось ранее в контексте машинного обучения для работы с текстом. В данной статье предложено мультимодальная архитектура, на вход которой совместно подается текст и фичи извлекаемые из ЭЭГ. Для множества word embedding types подмешивание данных ЭЭГ улучшает вторичную и третичную классификацию тональности, а также превосходит некоторые бейзлайн решения. Данный подход выглядит многоо…

Разработка беспилотных автомобилей остаётся очень сложной задачей, которая классически разделяется на четыре модуля: локализация, распознавание, управление и планирование. От подзадачи распознавания требуется точное определение объектов, находящихся рядом с беспилотным автомобилем: их класcа и расположения. Кроме точности, от таких моделей ожидаются устойчивость и высокая скорость работы, поскольку педполагается, что беспилотный автомобиль должен принимать решения в реальном времени при разных условиях окружающей среды (например, погода и время дня).

На практике “зрением” беспилотного автомобиля становятся различные сенсоры, которыми оборудован беспилотник. Чаще всего это радары, лидары и к…

Феномен коктейльной вечеринки заключается в способности слушателя фокусироваться на одном конкретном раздражителе, игнорируя другие. Большинство людей с легкостью справляются с этой задачей, чего нельзя сказать о системах автоматического распознавания речи. В статье, которая будет рассмотрена на семинаре, авторы представляют VoiceFilter Lite – одноканальную модель разделения речевых сигналов, являющуюся частью системы распознавания речи при потокой передаче сигнала. Модель принимает на вход перекрываемый шумом сигнал таргет-спикера и сохраняет его в чистом виде. На семинаре поймём: зачем авторы модели использовали асимметрическую лосс-функцию и механизм адаптивного подавления шума, а также …

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Одна из проблем алгоритмов глубокого обучения с подкреплением — обучение на сложных, разнообразных выборках, получаемых агентом при взаимодействии со средой. Один из способов облегчить процесс обучения — выстроить его последовательным образом от простых задач к более сложным так, чтобы переход к последующим задачам, во-первых, приближал агента к решению исходной задачи, и, во-вторых, делал сам процесс обучения более быстрым и эффективным. Curriculum learning (CL) — подход, основная цель которого построить такой план обучения. Несмотря на то, что CL уже показал свою эффективность в решении задач машинного обучения, зачастую сам план составляется вручную или на основе эвристик и концепций, ко…

Разработанные для работы с последовательностями трансформеры показывают state-of-the-art результаты в различных задачах. Применение трансформеров в задачах компьютерного зрения вместо привычных сверточных нейронных сетей позволяет избавиться от предположений о локальности взаимодействий внутри изображения. Однако в таком случае требуется учить все взаимодействия, что может быть недостижимо с вычислительной точки зрения для длинных последовательностей - например, изображений с высоким разрешением. На семинаре мы рассмотрим модель VQGAN для генерации изображений с высоким разрешением, которая объединяет в себе и сверточные сети, и трансформер. С помощью сверточных сетей модель учит объекты, к…

Важным этапом синтеза наночастиц является проверка, что полученные частицы имеют необходимую форму и размер, поскольку именно эти параметры определяют их функцию. Обычно исследователи пользуются сложными и времязатратными методами, такими как трансмиссионная электронная микроскопия (ТЭМ). В рассматриваемой работе предложен более простой метод: использование машинного обучения для получения формы и размера наночастиц из их излучаемого спектра, так как, во-первых, именно морфология частицы определяет её оптические свойства, а, значит, влияет на спектр излучения, а, во-вторых, экспериментальное получение спектра проще, чем трудоемкие измерения с помощью ТЭМ. На семинаре мы подробнее обсудим да…

Современные актор-критик методы основаны на двух составляющих: акторе, который определяет политику агента, и критике, который вычисляет значение value-функции для предложенной актором политики. Несмотря на успешное решение многих задач обучения с подкреплением, актор-критик и его модификации часто сталкиваются с проблемой неэффективного исследования среды. Авторы статьи “Adversarially Guided Actor-Critic” предлагают бороться с задачей балансирования между exploration и exploitation с помощью добавления в модель еще одной нейронной сети -- оппонента (the adversary), задача которого предсказывать действия актора путем минимизации KL-дивергенции между распределениями действий. В то же время в …

Тема копирования и наличия клонов в коде является достаточно хорошо исследованной. Литература показывает, что большой процент современного кода состоит из клонов и что часто присутствие клонов внутри проекта влечет за собой негативные последствия: код с ошибками может распространяться в клонированном коде, а информация о клонах в коде помогает разработчикам более эффективно поддерживать проект. Одним из способов борьбы с клонами в коде является рефакторинг «Извлечение метода»: необходимый фрагмент кода извлекается в отдельный метод, а его клоны заменяются на вызовы данного метода. В то же время, несмотря на то, что такая возможность часто есть, это требует от разработчика отдельных усилий: …

Многие приложения для молекулярных вычислений и обработки информации опираются на идентификацию отдельных молекул в сложных реагирующих смесях. Хотя идентификация молекул внутри таких сложных смесей может в определенных ситуациях выполняться с помощью одного спектроскопического инструмента, такого как масс-спектрометрия или ядерный магнитный резонанс (NMR), существует множество ситуаций, в которых эта задача требует мультимодальных измерений с использованием нескольких различных спектроскопических инструментов. Чтобы помочь химикам в выборе инструментов измерения (модальностей) для исследования таких решений, в статье используют методы глубокого обучения для получения масс-спектров, NMR и и…

В последнее время исследуется применение трансформеров в различных задачах, связанных с обработкой изображений. В отличии от обработки естественного языка, где трансформеры стали стандартным решением для многих задач, применение их к изображениям лимитировано. Большинство моделей просто используют механизм attention совместно со сверхточными сетями, сохраняя уже известные архитектуры. Однако в прошлом году вышла работа, описывающая первую архитектуру для распознавания объектов, основывающуюся только на трансформерах (ViT). И хотя эта модель показывает сравнимые результаты со state-of-the-art сетями, она обладает некоторыми недостатками, такими как большой размер модели и необходимость предо…

Ключевую роль в оценке исследования в рекомендательных системах, как и во многих других областях, играет сравнение с уже хорошо изученными и проверенными подходами. Но зачастую в исследованиях не уделяют достаточного внимания оптимальной настройке простых методов, поэтому результаты исследования могут сравниваться с неоптимальными результатами работы более простых методов на конкретной задаче. В следствии чего работоспособность и результативность работы нового метода может стоять под вопросом. Настройка старых методов и последующая оценка их работы на поставленной задаче требует ответственного подхода, что может занимать много усилий и времени. Такой подход необходим для получения оптимальн…

Комбинации лекарственных препаратов имеют широкое применение в терапии, т.к. совместный полезный эффект их взаимодействия может быть выше, чем у отдельных средств. Кроме того, комбинации лекарств могут снижать вероятность возникновения побочных эффектов, поскольку компоненты имеют меньшие дозировки, что способствует снижению токсичности. В недавних исследованиях в данной области, методы машинного обучения применялись для выявления синергических комбинаций для борьбы с раковыми заболеваниями. Но описанные в них методы неприменимы к новым заболеваниям, т.к. данные об эффективности комбинаций весьма ограничены. В условиях текущей пандемии нахождение успешной комбинации существующих молекул име…

Один из способов ускорения алгоритмов обучения с подкреплением - изменение функции награды (Reward shaping). В данном направлении хорошо проработана теория: ещё в 1999 году был разработан метод потенциалов, дающий необходимое и достаточное условие на то, что новая функция награды никак не изменит оптимальную политику для среды. У этого подхода есть серьёзный недостаток - в задачах, возникающих на практике, поиск функций-потенциалов по сложности эквивалентен самому обучению агента. Поэтому активно развивается направление обучения с подкреплением, связанное с аппроксимацией функций-потенциалов. На семинаре мы рассмотрим алгоритм аппроксимации потенциалов с помощью графовых свёрточных сетей (G…

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

Yandex Research — это исследовательская группа внутри Яндекса, которая занимается фундаментальными проблемами в важнейших областях computer science и искусственного интеллекта, таких как компьютерное зрение, Natural Language Processing, речевые технологии, краудсорсинг, поиск и рекомендации. В рамках митапа исследователи из Yandex Research и научной лаборатории Яндекса на Факультете Компьютерных Наук НИУ ВШЭ рассказали об интересных задачах, которыми они занимаются, а ещё о том, как стать частью команды. Участники митапа и программа: • Андрей Малинин. Неопределенность в структурных предсказаниях; • Станислав Морозов. Big GANs Are Watching You: о сегментации объектов без учителя с помощью го…

В этом видео победители соревнования NoFloodWithAI от Сбербанка и МЧС по предсказанию наводнений на реке Амур расскажут о своих решениях, о проблемах подобных задач и нетривиальных подходах к прогнозированию. 03:25 1 место - Дмитрий Раевский

Презентация https://storage.yandexcloud.net/datasouls-ods/ML_trainings_presentations/Dmitry_Raevsky.pdf

Ссылка на репозиторий https://github.com/RaevskyDN/aij2020-amur-noflood-public

Ссылка на описание решения https://opendatascience.slack.com/archives/C2LJA6VP0/p1606908626129600 13:07 2 место - Александр Мамаев

Презентация https://storage.yandexcloud.net/datasouls-ods/ML_trainings_presentations/Alexander_Mamaev.pdf

Ссылка на репозиторий https://github.…

ODS 6th Birthday

Панельная дискуссия, март 2021 Спикеры:

Артур Кузин, Kaggle Grandmaster

Станислав Семенов, Kaggle Grandmaster

Михаил Трофимов, Kaggle Grandmaster

Евгений Нижибицкий, ML Engineer Понравилось это видео? Подключайтесь к прямому эфиру в наш День Рождения 13 марта - будет очень интересно! https://ods.ai/events/birthday6 Вступить в сообщество: https://ods.ai/

Соцсети Дата Фест с актуальными анонсами: https://t.me/datafest

https://vk.com/datafest

В этом видео победители AIJ 2020 расскажут о своих решениях задачи по распознаванию почерка Петра I ✍🏻 Подробное описание задачи и лидерборд на странице соревнования https://ods.ai/tracks/aij2020/competitions/aij-petr 🥇 1 место: команда OCRV

Детальное описание решения в слаке ODS https://opendatascience.slack.com/archives/C2LJA6VP0/p1606079747484300 🥈 2 место: Владислав Крамаренко

Код решения https://storage.yandexcloud.net/datasouls-ods/submissions/e4b4ce84-dcac-4c84-bdf2-1bbd02fcb4ad/6e73c568/OCR-transformer.zip 🥉 3 место: Magic City

Код решения https://github.com/ArefievMC/sberbank_petr/blob/main/final_petr.ipynb Дополнительно:

- Рассказ о задаче от организаторов https://www.youtube.com/…

Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

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Спикер: Асхат Уразбаев, Founder of ScrumTrek, Agile Coach Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

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Спикер: Алексей Смирнов, CEO at Profiscope.io Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

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Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

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Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

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Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

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Спикер: Алексей Григорьев, Lead Data Scientist at OLX Group, Founder at DataTalks.Club Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

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Спикер: Дмитрий Колодезев, Director of Promsoft Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

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Спикеры: Денис Воротынцев, Data Scientist at Unity, Юрий Болконский, Kaggle Grandmaster Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

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Brilliant: http://www.brilliant.org/primer Papers:

- https://www.researchgate.net/publication/41910312_Altruism_Spite_and_Greenbeards

- https://www.reed.edu/biology/professors/srenn/pages/teaching/2007_syllabus/2007_readings/a13_Keller_1998.pdf For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

- Twitter: @primerlearning Sometimes streaming myself working on these monstrosities:

- Twitch: https://www.twitch.tv/primerjustin Made with Unity

https://github.com/Helpsypoo/PrimerUnity Music by Mathieu Keith. For business inquiries: mathieu.keith@gmail.com Several other inputs into the graphics are from public domain contributions to blendswap.com Plush blo…

Plush blobs: https://store.dftba.com/collections/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning A good place for learning more about how to be less wrong:

https://www.lesswrong.com/ For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

- Twitter: @primerlearning

- Facebook: facebook.com/primerlearning Streaming myself working on these monstrosities:

- Twitch: https://www.twitch.tv/primerjustin Made possible by support through Patreon:

Christian Gruber

Matthijs Ruijgrok

Christopher

Anthony Eufemio

José Hamilton

Zachariah Richard Fournier

Vladimir Duchenchuk

Noah Healy

JMakes

Mike Schmidt

PeepPhysics

Anders Fjeldvær

Ghost G…

Check out Brilliant: http://www.brilliant.org/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning

Store: https://store.dftba.com/collections/primer More on voting theory:

- Interactive by Nicky Case: https://ncase.me/ballot/

- The best single resource I found: https://www.lesswrong.com/posts/D6trAzh6DApKPhbv4/a-voting-theory-primer-for-rationalists Organizations that advocate for voting reform:

- Team Approval: https://electionscience.org/

- Team Instant Runoff: https://www.fairvote.org/ For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

- Twitter: @primerlearning

- Facebook: facebook.com/primerlearning Streaming myself wor…

Sergey Nazarov is the CEO of Chainlink, a decentralized oracle network that provides data to smart contracts.

Please support this podcast by checking out our sponsors:– Wine Access: https://wineaccess.com/lex to get 20% off first order– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– Indeed: https://indeed.com/lex to get $75 credit– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Sergey’s Twitter: https://twitter.com/SergeyNazarovChainlink Website: https://chain.link/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.c…

Jeremi Suri is a historian at UT Austin.

Please support this podcast by checking out our sponsors:– LMNT: https://drinkLMNT.com/lex to get free sample pack– Munk Pack: https://munkpack.com and use code LEX to get 20% off– Belcampo: https://belcampo.com/lex and use code LEX to get 20% off first order– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savingsEPISODE LINKS:Jeremi’s Twitter: https://twitter.com/JeremiSuriJeremi’s Website: http://jeremisuri.netThis is Democracy Podcast: http://jeremisuri.net/archives/1798The Impossible Presidency (book): https://amzn.to/2QKC5JpPODCAST …

Georges St-Pierre is a martial artist.

Please support this podcast by checking out our sponsors:– Allform: https://allform.com/lex to get 20% off– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Theragun: https://theragun.com/lex to get 30 day trial– The Information: https://theinformation.com/lex to get 75% off first monthEPISODE LINKS:GSP’s Twitter: https://twitter.com/GeorgesStPierreGSP’s Instagram: https://www.instagram.com/georgesstpierre/GSP’s Facebook: https://www.facebook.com/georgesstpierreGSP’s Website: https://www.gspofficial.comPODCAST INFO:Podcast website: https://lex…

Michael Malice is an anarchist.

Yaron Brook is an objectivist.

Both are podcasters and authors.

On some podcast players you should be able to click the timestamp to jump to that time.

(4:23:12) – Back to the island

Risto Miikkulainen is a computer scientist at UT Austin.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(06:51) – If we re-ran Earth over 1 million times(10:08) – Would aliens detect humans?

(12:46) – Evolution of intelligent life(16:31) – Fear of death(22:47) – Hyenas(26:12) – Language(29:43) – The magic of programming(35:43) – Neuralink(43:15) – Surprising discoveries by AI(46:49) – How evolutionary computation works(58:12) – Learning to walk(1:01:25) – Robots and a theory of mind(1:10:29) – Neuroevolution(1:20:47) – Tesla Autopilot(1:24:11) – Language and vision(1:29:53) – Aliens communicating with humans(1:35:29) – Would AI …

Robert Breedlove is a decentralized finance entrepreneur, philosopher, and podcaster.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(08:46) – Sovereignty(15:50) – Territorial imperative(20:28) – Property(26:33) – Anarchism(29:11) – Inflation is theft(33:35) – Volatility is truth(38:14) – Taleb and Bitcoin(42:22) – Life is information propagating through flesh(53:21) – Intelligence(57:34) – Space and time(1:04:16) – Pragmatic truth(1:15:14) – Creative destruction(1:19:03) – Capitalism vs Communism(1:31:46) – Jordan Peterson on religion(1:36:03) – Inflation(1:39:54) – What is money?

(2:04:36) – Bitcoin vs other cryptocurrencies(2…

Yannis Pappas is a comedian and podcaster.

Please support this podcast by checking out our sponsors:– Wine Access: https://wineaccess.com/lex to get 20% off first order– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Yannis’s Twitter: https://twitter.com/yannispappasLong Days Podcast: https://www.youtube.com/channel/UCywn6iboO1P8U7fotfllocwStand Up Special: https://www.youtube.com/watch?v=R156F0uXhzkPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8…

Tyler Cowen is an economist, writer, and podcaster.

Please support this podcast by checking out our sponsors:– Linode: https://linode.com/lex to get $100 free credit– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security camera– Public Goods: https://publicgoods.com/lex and use code LEX to get $15 offEPISODE LINKS:Tyler’s Twitter: https://twitter.com/tylercowenTyler’s Blog: https://marginalrevolution.com/Conversations with Tyler (Podcast): https://conversationswithtyler.com/Big Business (Book): https://amzn.to/2OBPbaKTyler’s Wiki: https://en.wikipedia.org/wiki/Tyler_CowenPODCAST INFO…

Nic Carter is a financial researcher, investor, writer, and podcaster on topics of decentralized finance.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(13:05) – Can humans fully understand reality?

(15:34) – The dollar system(21:44) – Bitcoin(23:19) – Opendime(27:22) – Core values of Bitcoin(35:51) – Who is Satoshi Nakamoto?

(41:39) – How Bitcoin works(50:02) – Bitcoin blocksize wars(1:02:27) – Layered scaling of Bitcoin(1:07:25) – Lightning network(1:10:25) – Schnorr/Taproot update to Bitcoin(1:15:17) – Criticisms of Bitcoin(1:25:04) – Bitcoin failure modes(1:33:07) – Bitcoin vs Ethereum(1:37:03) – Vitalik Buterin(1:39:56) – …

Ryan Schiller is the creator of Librex, an anonymous discussion feed for college communities.

Please support this podcast by checking out our sponsors:– Allform: https://allform.com/lex to get 20% off– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– BetterHelp: https://betterhelp.com/lex to get 10% off– Brave: https://brave.com/lexEPISODE LINKS:Librex App: https://librexapp.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episodes: https://youtube.com/lexfridmanYouTube Clips: https://youtube.com/lexclipsSUPPORT & CONNECT:– Chec…

Anthony Pompliano is an entrepreneur, investor, writer, and podcaster on topics of decentralized finance.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(08:54) – Army(16:35) – Iraq(24:14) – Will there always be war?

(31:27) – Bitcoin maximalism(39:56) – Money is a belief system(42:27) – Bitcoin(46:03) – Censorship(50:35) – Bitcoin as main currency(59:27) – Scarcity creates value(1:01:08) – Money is time(1:09:34) – Eric Weinstein vs Bitcoin Community(1:23:23) – Ray Dalio(1:40:31) – Bitclout(1:43:43) – How to get Bitcoin(1:54:28) – Investing(2:05:05) – Volatility(2:18:08) – Philosophy of the meme(2:28:03) – Dogecoin(2:37:33) – NF…

Ronald Sullivan is a law professor at Harvard and previously a lawyer for Harvey Weinstein and Aaron Hernandez.

Please support this podcast by checking out our sponsors:– Brooklinen: https://brooklinen.com and use code LEX to get $25 off + free shipping– Wine Access: https://wineaccess.com/lex to get 20% off first order– Munk Pack: https://munkpack.com and use code LEX to get 20% off– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premiumEPISODE LINKS:Ronald’s Twitter: https://twitter.com/profronsullivanRonald’s Website: https://hls.harvard.edu/faculty/directory/10870/SullivanRonald’s Wikipedia: https://en.wikipedia.org/wiki/Ronald_S._Sullivan_Jr.

Ronald’s NY Times Artic…

Ryan Hall is a martial artist, BJJ black belt, and MMA fighter undefeated in the UFC.

Please support this podcast by checking out our sponsors:– Indeed: https://indeed.com/fridman to get $75 credit– Audible: https://audible.com/lex to get $9.95 a month for 6 months– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– LMNT: https://drinkLMNT.com/lex to get free sample packEPISODE LINKS:Ryan’s Twitter: https://twitter.com/ryanhall5050​Ryan’s Website: http://www.ryanhallmma.com/​Ryan’s School: https://www.5050bjj.com​Ryan’s Online Courses: https://ryanhallonline.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwq…

Silvio Micali is a computer scientist at MIT, Turing award winner, and founder of Algorand.

Please support this podcast by checking out our sponsors:– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– The Information: https://theinformation.com/lex to get 75% off first month– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Silvio’s Twitter: https://twitter.com/silviomicaliAlgorand’s Twitter: https://twitter.com/AlgorandAlgorand’s Website: https://www.algorand.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https:/…

Today, people around the globe—from teachers to small-business owners to finance executives—use Microsoft Excel to make sense of the information that occupies their respective worlds, and whether they realize it or not, in doing so, they’re taking on the role of programmer.

In this episode, Senior Principal Research Manager Andy Gordon, who leads the Calc Intelligence team at Microsoft Research, and Senior Principal Researcher Simon Peyton Jones provide an inside account of the journey Excel has taken as a programming language, including the expansion of data types that has unlocked greater functionality and the release of the LAMBDA function, which makes the Excel formula language Turing-c…

How can we build Visual Question Answering systems for real users?

For this episode, we chatted with Danna Gurari, about her work in building datasets and models towards VQA for people who are blind.

We talked about the …

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They’re Coming for Our JobsAI has, is, and will continue to facilitate the automation of work done by humans.

Other times it may automate a particular part of their role, scaling their effectiveness.

Unless progress in AI inexplicably halts, the tasks done by humans vs. machines will continue to evolve.

Co-Host of Squaring the Strange Podcast, Caricature Artist, and an Academic Editor, Celestia Ward joins us today!

Kyle and Celestia discuss whether or not her jobs as a caricature artist or as an academic editor are under threat from AI automation.

Pandemic Machine Learning PitfallsToday on the show Derek Driggs, a PhD Student at the University of Cambridge.

He comes on to discuss the work Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans.

by: Michael Roberts, Derek Driggs, Matthew Thorpe, Julian Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Aviles-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, AIX-COVNET, James H. F. Rudd, Evis Sala & Carola-Bibiane Schönlieb.

Follow the team at @camimaging

Given a document in English, how can you estimate the ease with which someone will find they can read it? Does it require a college-level of reading comprehension or is it something a much younger student could read and understand? While these questions are useful to ask, they don't admit a simple answer. One option is to use one of the (essentially identical) two Flesch Kincaid Readability Tests. These are simple calculations which provide you with a rough estimate of the reading ease. In this episode, Kyle shares his thoughts on this tool and when it could be appropriate to use as part of your feature engineering pipeline towards a machine learning objective. For empirical validation of t…

Fairness Aware Outlier DetectionToday on the show we have Shubhranshu Shekar, a Ph.

D Student at Carnegie Mellon University, who joins us to talk about his work, FAIROD: Fairness-aware Outlier Detection.

https://shubhranshu-shekhar.github.io/

Life May Be RareToday on the show Dr. Anders Sanburg, Senior Research Fellow at the Future of Humanity Institute at Oxford University, comes on to share his work The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare@anderssandberg

Social NetworksMayank Kejriwal, Research Professor at the University of Southern California and Researcher at the Information Sciences Institute, joins us today to discuss his work and his new book Knowledge, Graphs, Fundamentals, Techniques and Applications by Mayank Kejriwal, Craig A. Knoblock, and Pedro SzekleySocial MediaLinkedInTwitter

The QAnon ConspiracyQAnon is a conspiracy theory born in the underbelly of the internet.

Max Aliapoulios joins us to discuss the paper The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information.

This makes it possible for researchers to study this phenomenon in a way not accessible in previous conspiracy theories of similar popularity.

This episode is also the first in our 2021 Pilot Season in which we are going to test out a few formats for Data Skeptic to see what our next season should be.

In a few weeks, we’re going to ask everyone to vote for their favorite theme for our next season.

Benchmarking Computer Vision on Edge vs CloudKarthick Shankar, Masters Student at Carnegie Mellon University, and Somali Chaterji, Assistant Professor at Purdue University, join us today to discuss the paper JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads.

Social Media

Goodhart’s Law in Reinforcement LearningHal Ashton, a PhD student from the University College of London, joins us today to discuss a recent work Causal Campbell-Goodhart’s law and Reinforcement Learning.

Also mentioned was The Book of Why by Judea Pearl

Video Anomaly DetectionYuqi Ouyang, in his second year of PhD study at the University of Warwick in England, joins us today to discuss his work Video Anomaly Detection by Estimating Likelihood of Representations.

Fault Tolerant Distributed Gradient DescentNirupam Gupta, a Computer Science Post Doctoral Researcher at EDFL University in Switzerland, joins us today to discuss his work Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent.

Conference Details:https://georgetown.zoom.us/meeting/register/tJ0sc-2grDwjEtfnLI0zPnN-GwkDvJdaOxXF

Decentralized Information GatheringMikko Lauri, Post Doctoral researcher at the University of Hamburg, Germany, comes on the show today to discuss the work Information Gathering in Decentralized POMDPs by Policy Graph Improvements.

Follow Mikko: @mikko_lauri

Balaji Arun, a PhD Student in the Systems of Software Research Group at Virginia Tech, joins us today to discuss his research of distributed systems through the paper “Taming the Contention in Consensus-based Distributed Systems.” Works Mentioned “Taming the Contention in Consensus-based Distributed Systems” by Balaji Arun, Sebastiano Peluso, Roberto Palmieri, Giuliano Losa, and Binoy Ravindranhttps://www.ssrg.ece.vt.edu/papers/tdsc20-author-version.pdf “Fast Paxos” by Leslie Lamport https://link.springer.com/article/10.1007/s00446-006-0005-x

Maartje der Hoeve, PhD Student at the University of Amsterdam, joins us today to discuss her research in automated summarization through the paper "What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization."

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!

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…

Konrad Körding joins us to discuss his work in educating the next generation in deep learning and his views on the importance of causality in deep learning research.

In this episode you will learn:• Konrad’s academic b…

In this episode, I tackle another historical topic: the history of data.

Additional materials: www.superdatascience.com/468

Noah Gift joins us to discuss how he believes data science urgency and the end of hierarchies will change the world for the better.

In this episode you will learn:• Catch up with Noah [2:50]• Educational options to pu…

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Konrad Kopczynski joins us to discuss how data, tracking, analytics, and key performance indicators can help your professional and personal development.

In this episode you will learn:• What does Konrad do [3:40]• Too…

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Matt Dancho joins us to discuss his various packages for time series analysis and his courses on the topic through his company Business Science.

In this episode you will learn:• How Matt got into time series library de…

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Sam Hinton joins us to discuss his work since assisting COVID-19 data pipelines, now working in renewable energy and applications of ML and MLOps for the industry.

In this episode you will learn:• Catching up with Sam …

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Vince Petaccio joins us to discuss how he sees data science, ML, and AI making positive impacts in the fight against climate change.

In this episode you will learn:• Where in the world is Vince?

[2:08]• Vince’s intere…

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Harpreet Sahota joins us to discuss his data science mentorship work outside his day job and how you can land your dream job.

In this episode you will learn:• Harpreet’s current life and location [2:25]• Data Communit…

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Horace Wu joins us to discuss his work on Syntheia, a unique product that helps sift through massive amounts of legal data to augment the capacities and function of law firms.

In this episode you will learn:• Horace’s …

April 28, 2021 podcastYour data is worth thousands a year.

Why aren’t you getting your fair share?

There is a company that has a mission: they want you to take back control and get paid for your data.

In this episode I speak about knowledge graphs, data confidentiality and privacy with Mike Audi, CEO of MyTiki.

You can reach them on their website https://mytiki.com/Discord official channelhttps://discord.com/invite/evjYQq48BeTelegramhttps://t.me/mytikiappSignalhttps://signal.group/#CjQKIA66Eq2VHecpcCd-cu-dziozMRSH3EuQdcZJNyMOYNi5EhC0coWtjWzKQ1dDKEjMqhkP

April 19, 2021 podcastIn this episode I have an amazing conversation with Lillian Pearson from data-mania.com This is an action-packed episode on how data professionals can quickly convert their data expertise into high-growth data businesses, all by selecting optimal business models, revenue models, and pricing structures.

If you want to know more or get in touch with Lillian, follow the links below:

April 13, 2021 podcastIs there a gap between life science and data science?

What’s the situation when it comes to interdisciplinary research?

In this episode I am with Laura Harris, Director of Training for the Institute of Cyber-Enabled Research (ICER) at Michigan State University (MSU), and we try to answer some of those questions.

You can contact Laura at training@msu.edu or on LinkedIn

April 11, 2021 podcastIn this episode I am with George Hosu from Cerebralab and we speak about how dangerous it is not to pay for the services you use, and as a consequence how dangerous it is letting an algorithm decide what you like or not.

Our SponsorsThis episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceIf building software is your passion, you’ll love ThoughtW…

April 8, 2021 podcastIn this episode I speak with Ritchie Vink, the author of Polars, a crate that is the fastest dataframe library at date of speaking 🙂 If you want to participate to an amazing Rust open source project, this is your change to collaborate to the official repository in the references.

Our SponsorAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

Referenceshttps://github.com/ritch…

March 31, 2021 podcastDo you want to know the latest in big data analytics frameworks?

Have you ever heard of Apache Arrow?

In this episode I speak with Andy Grove one of the main authors of Apache Arrow and Ballista compute engine.

Andy explains some challenges while he was designing the Arrow and Ballista memory models and he describes some amazing solutions.

It’s a podcast for techies by techies.

March 19, 2021 podcastPandas is the de-facto standard for data loading and manipulation.

Python is the de-facto programming language for such operations.

Rust is the underdog.

In this episode I am showing you why that is no longer the case.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

In this episode I summarize the ways to parallelize on different architectures and operating systems.

Rock-star data scientists must know how concurrency works and when to use it IMHO.

Our SponsorsThis episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in …

March 10, 2021 podcastIn plain English, concurrent and parallel are synonyms.

In this episode I summarize the ways to parallelize on different architectures and operating systems.

Rock-star data scientists must know how concurrency works and when to use it IMHO.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and…

March 2, 2021 podcastThis is one of the most dynamic and fascinating topics: API technologies for machine learning.

In this episode I speak about three must-know technologies to place your model behind an API.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceIf building software is your passion, you’ll love ThoughtWorks Technology Podcast.

It’s a podcast for techies by techies.

Their team of experienced technologists take a deep dive into a tech topic that’s piqued their interest — it could be how machine learning is being used in astrophysics or maybe how to succeed at c…

February 24, 2021 podcastIn this episode I am with George Hosu from Cerebralab and we speak about how dangerous it is not to pay for the services you use, and as a consequence how dangerous it is letting an algorithm decide what you like or not.

Our SponsorsThis episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceIf building software is your passion, you’ll love Thoug…

February 15, 2021 podcastThe financial system is changing.

It is becoming more efficient and integrated with many more services making our life more… digital.

Is the old banking system doomed to fail?

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will…

Our ServicesAmethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve.

AI/ML Fintech Healthcare/RWE Predictive maintenanceWe provide solutions in:

February 1, 2021 podcastIn this podcast I get inspired by Paul Done‘s presentation about The Six Principles for Building Robust Yet Flexible Shared Data Applications, and show how powerful of a language Rust is while still maintaining the flexibility of less strict languages.

Our SponsorsThis episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceAmethix use advanced Art…

🎙 Пилотный выпуск нашего подкаста! В гостях у нас Евгений Кашин – ML-инженер из Snap Inc. С Женей мы успели обсудить кучу вещей: Из Воронежа в Лондон 🇬🇧

Snapchat и маски 😷

Будущие прорывы в DS и AR 🚀

Вопросы от русской GPT-3 🤖 … и многое другое 😉

Домашнее задание: отгадать, какие именно вопросы были сгенерированы нейросетью 🦾😄

Подписывайтесь на нас в Twitter (там будут мемы) и Telegram!

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