Very ML
State-of-the-art Machine Learning News Feed
/r/MachineLearning
последний пост 1 час назад
[D] Bias-variance tradeoff in human perception?
[D] Bias-variance tradeoff in human perception?

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1 час назад @ reddit.com
[D] Fool me once, shame on you; fool me twice, shame on me: Exponential Smoothing vs. Facebook's Neural-Prophet.
[D] Fool me once, shame on you; fool me twice, shame on me: Exponential Smoothing vs. Facebook's Neural-Prophet. [D] Fool me once, shame on you; fool me twice, shame on me: Exponential Smoothing vs. Facebook's Neural-Prophet.

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1 час назад @ reddit.com
[P] MinImagen: A Minimal Imagen Implementation
[P] MinImagen: A Minimal Imagen Implementation

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[P] Yet another deep learning based natural language processing APIs focused on Korean and English
[P] Yet another deep learning based natural language processing APIs focused on Korean and English

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2 часа назад @ reddit.com
[P] Cleanlab Vizzy — learn how to automatically find label errors and out-of-distribution data
[P] Cleanlab Vizzy — learn how to automatically find label errors and out-of-distribution data [P] Cleanlab Vizzy — learn how to automatically find label errors and out-of-distribution data

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2 часа назад @ reddit.com
[D] Weird stuff in Microsoft COCO
[D] Weird stuff in Microsoft COCO

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7 часов назад @ reddit.com
[P] CodeStamper - Ensuring traceability between ML experiments and Code
[P] CodeStamper - Ensuring traceability between ML experiments and Code

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7 часов назад @ reddit.com
[P] The table extraction tool: PP-Structure
[P] The table extraction tool: PP-Structure

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13 часов назад @ reddit.com
[D] Can there be no real difference between VAEs and GANs in some problems?
[D] Can there be no real difference between VAEs and GANs in some problems?

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14 часов назад @ reddit.com
[P] The spelled-out intro to neural networks and backpropagation: building micrograd (Andrej Karpathy 2h25m lecture)
[P] The spelled-out intro to neural networks and backpropagation: building micrograd (Andrej Karpathy 2h25m lecture)

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15 часов назад @ reddit.com
[D] Is a Chinese PhD as good as a USA or UK one? Or a European PhD?
[D] Is a Chinese PhD as good as a USA or UK one? Or a European PhD?

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15 часов назад @ reddit.com
[R] Transframer: Arbitrary Frame Prediction with Generative Models - DeepMind 2022 - Can generate 30 Second Videos from a single frame while also being able to do 8 different Vision tasks including depth estimation, object detection and instance segmentati
[R] Transframer: Arbitrary Frame Prediction with Generative Models - DeepMind 2022 - Can generate 30 Second Videos from a single frame while also being able to do 8 different Vision tasks including depth estimation, object detection and instance segmentati [R] Transframer: Arbitrary Frame Prediction with Generative Models - DeepMind 2022 - Can generate 30 Second Videos from a single frame while also being able to do 8 different Vision tasks including depth estimation, object detection and instance segmentati

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18 часов назад @ reddit.com
Why does the CNN model accuracy vary too much when the dataset is the same? [P]
Why does the CNN model accuracy vary too much when the dataset is the same? [P] Why does the CNN model accuracy vary too much when the dataset is the same? [P]

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[D] [R] Fine tuning a model with batch size equal to 2
[D] [R] Fine tuning a model with batch size equal to 2

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18 часов назад @ reddit.com
[D] How do contrastive loss functions avoid the issue of duplicates / near duplicates?
[D] How do contrastive loss functions avoid the issue of duplicates / near duplicates?

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19 часов назад @ reddit.com
Towards Data Science
последний пост 29 минут назад
Introduction to SQL Window Functions: Part 1
Introduction to SQL Window Functions: Part 1 Introduction to SQL Window Functions: Part 1

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29 минут назад @ towardsdatascience.com
Python Concurrency — Multiprocessing
Python Concurrency — Multiprocessing Python Concurrency — Multiprocessing

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2 часа назад @ towardsdatascience.com
Demystifying the Parquet File Format
Demystifying the Parquet File Format Demystifying the Parquet File Format

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2 часа назад @ towardsdatascience.com
Machine learning: a friend or a foe for science?
Machine learning: a friend or a foe for science? Machine learning: a friend or a foe for science?

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3 часа назад @ towardsdatascience.com
What Is the Difference Between a Data Engineer, a Data Scientist, and a Data Analyst?
What Is the Difference Between a Data Engineer, a Data Scientist, and a Data Analyst? What Is the Difference Between a Data Engineer, a Data Scientist, and a Data Analyst?

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3 часа назад @ towardsdatascience.com
How To Print Coloured Text in The Terminal Using Python
How To Print Coloured Text in The Terminal Using Python How To Print Coloured Text in The Terminal Using Python

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3 часа назад @ towardsdatascience.com
Competitive Programming & AlphaCode
Competitive Programming & AlphaCode Competitive Programming & AlphaCode

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3 часа назад @ towardsdatascience.com
Explain SQL Joins the Right Way
Explain SQL Joins the Right Way Explain SQL Joins the Right Way

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3 часа назад @ towardsdatascience.com
Connecting DBeaver to Google BigQuery
Connecting DBeaver to Google BigQuery Connecting DBeaver to Google BigQuery

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8 часов назад @ towardsdatascience.com
Monitor Vegetation with Google Earth Engine
Monitor Vegetation with Google Earth Engine Monitor Vegetation with Google Earth Engine

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9 часов назад @ towardsdatascience.com
How to Intuit the Prosecutor’s Fallacy (and Run Better Hypothesis Tests)
How to Intuit the Prosecutor’s Fallacy (and Run Better Hypothesis Tests) How to Intuit the Prosecutor’s Fallacy (and Run Better Hypothesis Tests)

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9 часов назад @ towardsdatascience.com
How to Explain Image Classifiers Using LIME
How to Explain Image Classifiers Using LIME How to Explain Image Classifiers Using LIME

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9 часов назад @ towardsdatascience.com
Software as a Service: The Game-Changer for Small IT-Departments
Software as a Service: The Game-Changer for Small IT-Departments Software as a Service: The Game-Changer for Small IT-Departments

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9 часов назад @ towardsdatascience.com
ADAS: Collision Avoidance System on Indian Cars
ADAS: Collision Avoidance System on Indian Cars ADAS: Collision Avoidance System on Indian Cars

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9 часов назад @ towardsdatascience.com
How to Use Deliberate Practice to Master the Most Challenging Concepts in Data Science
How to Use Deliberate Practice to Master the Most Challenging Concepts in Data Science How to Use Deliberate Practice to Master the Most Challenging Concepts in Data Science

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10 часов назад @ towardsdatascience.com
Distill.pub Distill.pub
последний пост None
The Gradient The Gradient
последний пост 2 недели, 1 день назад
Symmetries, Scaffolds, and a New Era of Scientific Discovery
Symmetries, Scaffolds, and a New Era of Scientific Discovery Symmetries, Scaffolds, and a New Era of Scientific Discovery

Figure 1: Timeline of the drug discovery procedure, from target validation to clinical launch, from [1].

This article will cover how the application of geometric deep learning and the field of molecular machine learning is ushering us into a new era of scientific discovery.

CitationFor attribution in academic contexts or books, please cite this work asMeilina Reksoprodjo, "Symmetries, Scaffolds, and a New Era of Scientific Discovery", The Gradient, 2022.

[7] J. Vamathevan et al., "Applications of machine learning in drug discovery and development", Nature Reviews Drug Discovery, vol.

Available: https://thegradient.pub/ai-scientific-revolution/[13] H. Chen, O. Engkvist, Y. Wang, M. Olivecron…

2 недели, 1 день назад @ thegradient.pub
Overview of Graph Theory and Alzheimer's Disease
Overview of Graph Theory and Alzheimer's Disease Overview of Graph Theory and Alzheimer's Disease

2015)Photos of the brains of Paul Broca’s two aphasic patients, Leborgne (top row) and Lelong (bottom row) (Dronkers et al.

During the last decade, more advanced techniques borrowed from graph theory have been applied to brain imaging research (Rubinov and Sporns 2010).

Importantly, graph-based analyses can model the dynamics of the entire brain network all at once, thereby enabling investigation of network-wide properties.

CitationFor attribution in academic contexts or books, please cite this work asRebecca Ehrenkranz, "Overview of Graph Theory and Alzheimer's Disease", The Gradient, 2022.

BibTeX citation:@article{ehrenkranz_graph_ad,author = {Ehrenkranz, Rebecca},title = {Overview of Gra…

3 недели, 3 дня назад @ thegradient.pub
Lessons from the GPT-4Chan Controversy
Lessons from the GPT-4Chan Controversy Lessons from the GPT-4Chan Controversy

PreambleThis article contains an objective summary of a recent controversy related to an AI model named GPT-4chan, as well as a subjective commentary with my thoughts on it.

The main questions I will address are the following:Can GPT-4chan cause harm to peopleCan GPT-4chan contribute to AI researchIs GPT-4chan more 'truthful' than GPT-3Should the GPT-4chan model have been released to the publicWhat was the intent behind developing, deploying, and distributing GPT-4chanWas deploying GPT-4chan bots to interact with people on a message board unethicalCan GPT-4chan cause harm to peopleCan a bot that disseminates hate speech on the internet (e.g.

Moreover, now that the whole ordeal predictably l…

2 месяца назад @ thegradient.pub
AI is Ushering In a New Scientific Revolution
AI is Ushering In a New Scientific Revolution AI is Ushering In a New Scientific Revolution

With manifold impacts stretching the length of the scientific method, AI is ushering in a scientific revolution through groundbreaking discoveries, novel techniques and augmented tools, and automated methods that advance the speed and accuracy of the scientific process.

Beyond the protein-folding problem, AI has proven its scientific worth with discoveries in a number of fields, from cosmology and chemistry to semiconductor design and materials science.

AI is ushering in a new scientific revolution by making remarkable breakthroughs in a number of fields, unlocking new approaches to science, and accelerating the pace of science and innovation.

CitationFor attribution in academic contexts or…

2 месяца, 1 неделя назад @ thegradient.pub
Working on the Weekends - an Academic Necessity?
Working on the Weekends - an Academic Necessity? Working on the Weekends - an Academic Necessity?

For most people, these roles outside of work occupy their evenings, weekends and vacations, yet almost every academic I know seems to fill every available bit of time with academic pursuits.

Not working on weekends seemed like a graduation from the messy life of an undergrad into the more structured life of an adult.

And the strangest thing is, I do not know where I got the idea that I should be working on weekends.

CitationFor attribution in academic contexts or books, please cite this work asClaas Voelcker, "Working on the Weekends - an Academic Necessity?

BibTeX citation:@article{class2022working,author = {Voelcket, Claas},title = {Working on the Weekends - an Academic Necessity?

2 месяца, 2 недели назад @ thegradient.pub
Lessons From Deploying Deep Learning To Production
Lessons From Deploying Deep Learning To Production Lessons From Deploying Deep Learning To Production

I spent my last year at Berkeley doing research in deep learning for computer vision and working on Caffe, one of the first popular deep learning libraries.

Now I’m at Aquarium, where I get to help a multitude of companies deploying deep learning models to solve important problems for society.

I’ve learned a lot of lessons about doing deep learning in production, and I'd like to share some of those lessons with you so you don’t have to learn them the hard way.

For attribution in academic contexts or books, please cite this work asPeter Gao, "Lessons From Deploying Deep Learning To Production", The Gradient, 2022.

BibTeX citation:@article{gao2022lessons,author = {Gao, Peter },title = {Lesson…

3 месяца назад @ thegradient.pub
An Illustrated Tour of Applying BERT to Speech Data
An Illustrated Tour of Applying BERT to Speech Data An Illustrated Tour of Applying BERT to Speech Data

The core idea behind wav2vec 2.0 is to teach the model to do two things in parallel:Quantize continuous speech data into discrete units automatically.

Wav2vec uses 2 groups with 320 possible words in each group, hence a theoretical maximum of 320 x 320 = 102,400 speech units.

The final context vectors then go through the last projection layer to match the dimension of the quantized speech units Qt.

Fine-tuning and downstream tasksThis concludes our tour of wav2vec 2.0 and its pre-training process.

HuBERT re-uses embeddings from the BERT encoder to improve targets, while wav2vec 2.0 only uses the output of the convolutional network for quantization.

3 месяца, 1 неделя назад @ thegradient.pub
Beyond Message Passing, a Physics-Inspired Paradigm for Graph Neural Networks
Beyond Message Passing, a Physics-Inspired Paradigm for Graph Neural Networks Beyond Message Passing, a Physics-Inspired Paradigm for Graph Neural Networks

Graph Neural Networks (GNNs) are by far the most common among graph ML methods and the most popular neural network architectures overall [2].

CitationFor attribution in academic contexts or books, please cite this work asMichael Bronstein, "Beyond Message Passing, a Physics-Inspired Paradigm for Graph Neural Networks", The Gradient, 2022.

BibTeX citation:@article{dlneuro2022,author = {Bronstein, Michael},title = {Beyond Message Passing, a Physics-Inspired Paradigm for Graph Neural Networks},journal = {The Gradient},year = {2022},howpublished = {\url{https://thegradient.pub/graph-neural-networks-beyond-message-passing-and-weisfeiler-lehman}},}[1] See e.g.

A general form of message passing an…

3 месяца, 1 неделя назад @ thegradient.pub
Focus on the Process: Formulating AI Ethics Principles More Responsibly
Focus on the Process: Formulating AI Ethics Principles More Responsibly Focus on the Process: Formulating AI Ethics Principles More Responsibly

On their own, AI ethics principles are insufficient to improve AI systems.

Instead, I suggest that each organization should articulate its own AI ethics principles, and I sketch ways to do so responsibly.

The search for universal AI ethics principlesIn recent years, several research groups have sought unifying themes in current AI ethics principles.

A key question is how to formulate AI ethics principles responsibly and how to tell that an organization has developed its principles responsibly.

But the first question to ask is “which principles?”, and my answer is: Don’t settle for “universal” AI ethics principles.

3 месяца, 2 недели назад @ thegradient.pub
Deep Learning in Neuroimaging
Deep Learning in Neuroimaging Deep Learning in Neuroimaging

Specifically, this overview will first explain some common neuroimaging modalities more in-depth and then discuss applications of deep learning in conjunction with some of the unique characteristics of neuroimaging data.

These unique characteristics tie into a broader movement in deep learning, namely that data understanding should be a goal in itself to maximize the impact of applied deep learning.

Unique and leverageable aspects of neuroimaging dataAmong others [20], one critical challenge with deep learning in the field of neuroimaging is the limited number of samples; many neuroimaging datasets range from roughly 300 to 1300 subjects.

Data understanding and interpretation is an essentia…

3 месяца, 2 недели назад @ thegradient.pub
AI Startups and the Hunt for Tech Talent in Vietnam
AI Startups and the Hunt for Tech Talent in Vietnam AI Startups and the Hunt for Tech Talent in Vietnam

Tech and AI startups have continued to gain prominence as the AI wave swept the country in 2018.

Hanoi and Ho Chi Minh City have developed a robust ecosystem for tech startups, with dominating sectors including AI, e-commerce, fin-tech, and enterprise solutions.

The ecosystem of 149 AI startups has attracted funding from both domestic and regional venture capital firms.

However, according to computer science professor Than Khoat, these structural initiatives have only started rolling out since 2020, and have not yet translated to a significant boost in tech talent supply to meet the pressing demand of tech talent of the fast growing tech ecosystem.

CitationFor attribution in academic contex…

4 месяца назад @ thegradient.pub
New Technology, Old Problems: The Missing Voices in Natural Language Processing
New Technology, Old Problems: The Missing Voices in Natural Language Processing New Technology, Old Problems: The Missing Voices in Natural Language Processing

Recently, NLP technology facilitated access and synthesis of COVID-19 research with the release of a public, annotated research dataset and the creation of public response resources.

Wikipedia serves as a source for BERT, GPT and many other language models.

Responding to this, MIT researchers have released StereoSet, a dataset for measuring bias in language models across several dimensions.

If NLP technology is going to be something revolutionary, it will need to be better and different.

CitationFor attribution in academic contexts or books, please cite this work asBenjamin Batorsky, "New technology, old problems: The missing voices in Natural Language Processing", The Gradient, 2022.

4 месяца, 1 неделя назад @ thegradient.pub
Reading the Tea Leaves: Expert End-Users Explaining the Unexplainable
Reading the Tea Leaves: Expert End-Users Explaining the Unexplainable Reading the Tea Leaves: Expert End-Users Explaining the Unexplainable

Somehow I think, in terms of chess, engines are always capable of bringing out the key points of a position but never the actual key point itself.

Discussing this pre-neural era of chess engines, Sadler suggests that the elements of the hand-crafted function are uninteresting despite being inherently interpretable.

Before chess engines were unambiguously better than humans (and even for a while after they were), Seconds faced the challenge of doubting the validity of an engine prediction.

In this gap, end-users resort to reading the tea leaves of their model’s predictions.

Sadler additionally has extensive, high quality content on his personal and book-related YouTube channels: Personal, Ga…

4 месяца, 1 неделя назад @ thegradient.pub
Bootstrapping Labels via ___ Supervision & Human-In-The-Loop
Bootstrapping Labels via ___ Supervision & Human-In-The-Loop Bootstrapping Labels via ___ Supervision & Human-In-The-Loop

Active learning: Find samples for human-in-the-loopIn active learning, we select the most “interesting” unlabeled samples for labeling via human-in-the-loop (HITL).

Facebook added a twist to active learning by including similarity search as a filter (Similarity Search for Efficient Active Learning and Search aka SEALS).

Snorkel DryBell then applies these labeling functions on unlabeled examples before loading the output and labeling functions into a generative model.

Even Tesla, which probably has one of the most sophisticated data labeling systems to support Autopilot, hasn’t worked out all the kinks.

How to bootstrap labels at various stages of maturityGetting labels for machine learning …

5 месяцев, 2 недели назад @ thegradient.pub
One Voice Detector to Rule Them All
One Voice Detector to Rule Them All One Voice Detector to Rule Them All

Another problem arises if you try to find a high quality VAD with a permissible license.

Voice Activity Detection is the problem of looking for voice activity – or in other words, someone speaking – in a continuous audio stream.

The input is just a small audio chunk, and the output is a probability that this chunk contains speech given its history.

CitationFor attribution in academic contexts or books, please cite this work asAlexander Veysov and Dimitrii Voronin, "One Voice Detector to Rule Them All", The Gradient, 2022.

BibTeX citation:@article{veysov2020towardimagenetstt,author = {Veysov, Alexander and Voronin, Dimitrii},title = {One Voice Detector to Rule Them All},journal = {The Gradie…

5 месяцев, 4 недели назад @ thegradient.pub
TheSequence TheSequence
последний пост 1 день, 6 часов назад
🔂 Edge#217: ML Testing Series – Recap
🔂 Edge#217: ML Testing Series – Recap 🔂 Edge#217: ML Testing Series – Recap

Last week we finished our mini-series about ML testing, one of the most critical elements of the ML models’ lifecycle.

As the proverb (and many ML people) says: Repetition is the mother of learning ;)The essence of ML testing is to execute explicit checks that validate the behavior of an ML model.

Plenty of taxonomies can be used to organize ML testing techniques.

A very general approach segments testing techniques into two main groups relative to the ML model lifecycle:Pre-Train Tests: Designed to find problems that can help optimize the training workflow.

→ In Edge#211: we discuss what to test in ML models; explain how Meta uses A/B testing to improve Facebook’s newsfeed algorithm; and ex…

1 день, 6 часов назад @ thesequence.substack.com
📙 Free book: Meet the Data Science Innovators
📙 Free book: Meet the Data Science Innovators 📙 Free book: Meet the Data Science Innovators

Learn from top data science leaders, who share their insights on their groundbreaking innovations, their careers, and the data science profession.

Who’s doing the most innovative things in data science?

These questions—and many more—are the focus of “The Data Science Innovator’s Playbook,” a free Domino Data Lab eBook that explores the work, ideas, and experiences of seven people whose work is revolutionizing data science and business, and having an impact on some of the world’s biggest problems.

All say that better tools for data scientists are a key driver of innovation.

Meet Cassie and other top leaders who are advancing the profession, achieving new value for their companies, and helpin…

2 дня, 4 часа назад @ thesequence.substack.com
😴 ❌ Don’t Sleep on JAX
😴 ❌ Don’t Sleep on JAX 😴 ❌ Don’t Sleep on JAX

JAX was initially released by Google Research in 2018 with the objective of streamlining high-performance numerical computing.

While it was not intended as a deep learning framework in the first place, JAX has seen relevant adoption within the deep learning community.

This has been partly influenced by the adoption of AI powerhouses like Google Research and, very notably, DeepMind, which has been very public about their adoption of JAX.

Just this week, Google Research open-sourced a new ranking library of ranking algorithms for JAX.

It might become one of the most relevant deep learning frameworks of the next few years.

3 дня, 5 часов назад @ thesequence.substack.com
📌 Event: Last chance to register for conference on scalable AI – Aug 23-24 in San Francisco!
📌 Event: Last chance to register for conference on scalable AI – Aug 23-24 in San Francisco! 📌 Event: Last chance to register for conference on scalable AI – Aug 23-24 in San Francisco!

The world’s top minds in AI and distributed computing are coming to Ray Summit — August 23-24 in San Francisco.

Join the global Ray community for two days of keynotes, training, and technical sessions exploring the future of scalable AI and more.

No matter your level of experience with Ray, you’ll come away with tools and know-how that will help you understand why and how large organizations are building their next generation of ML Platforms on top of Ray.

Be sure to register today as seats are limited!

SAVE YOUR SEAT

5 дней, 5 часов назад @ thesequence.substack.com
🐈‍⬛ Edge#216: DeepMind’s New Super Model can Generalize Across Multiple Tasks on Different Domains
🐈‍⬛ Edge#216: DeepMind’s New Super Model can Generalize Across Multiple Tasks on Different Domains 🐈‍⬛ Edge#216: DeepMind’s New Super Model can Generalize Across Multiple Tasks on Different Domains

On Thursdays, we dive deep into one of the freshest research papers or technology frameworks that is worth your attention.

💥 What’s New in AI: DeepMind’s New Super Model can Generalize Across Multiple Tasks on Different DomainsMost deep learning models specialize in mastering a single task in a single domain.

Recently, we have seen the emergence of multi-task models in single domains, such as transformers like GPT-3 in the language space.

We have also seen models like OpenAI’s Dall-E that can combine knowledge from different domains to master a single task.

However, having a single model mastering multiple tasks across heterogenous domains remains an elusive goal for deep learning.

6 дней, 5 часов назад @ thesequence.substack.com
🏛 Edge#215: Pre-Train Model Testing and the Pillars of Robust ML
🏛 Edge#215: Pre-Train Model Testing and the Pillars of Robust ML 🏛 Edge#215: Pre-Train Model Testing and the Pillars of Robust ML

In this issue:we discuss Pre-Train Model Testing ;we overview the pillars of robust machine learning ;we explore Great Expectations.

💡 ML Concept of the Day: Pre-Train Model TestingIn the previous edition of our machine learning (ML) testing series (Edge#213), we discussed the main methods for testing trained ML models.

Pre-train tests are another type of testing method targeted to identify potential errors in ML models prior to executing expensive training jobs.

Typically, pre-train model testing is targeted to validate the correct composition of the datasets and some key verifications in the model behavior.

Without attempting to provide an exhaustive list, there are some pre-train testing…

1 неделя, 1 день назад @ thesequence.substack.com
🏛 Edge#215: Pre-Train Model Testing and the Pillars of Robust ML
🏛 Edge#215: Pre-Train Model Testing and the Pillars of Robust ML 🏛 Edge#215: Pre-Train Model Testing and the Pillars of Robust ML

In this issue:we discuss Pre-Train Model Testing ;we overview the pillars of robust machine learning ;we explore Great Expectations.

💡 ML Concept of the Day: Pre-Train Model TestingIn the previous edition of our machine learning (ML) testing series (Edge#213), we discussed the main methods for testing trained ML models.

Pre-train tests are another type of testing method targeted to identify potential errors in ML models prior to executing expensive training jobs.

Typically, pre-train model testing is targeted to validate the correct composition of the datasets and some key verifications in the model behavior.

Without attempting to provide an exhaustive list, there are some pre-train testing…

1 неделя, 1 день назад @ thesequence.substack.com
🏷 Data Labeling for ML: Survey
🏷 Data Labeling for ML: Survey 🏷 Data Labeling for ML: Survey

About 45% of the time in data science projects is consumed by processing and labeling data.

It’s fair to say that data labeling is one of the most expensive tasks of any machine learning project.

We keep learning from the experience gained by engineers and entrepreneurs behind the leading data labeling solutions, Toloka, Superb AI, Label Studio, and more.

TAKE THE SURVEYThe survey invites machine learning engineers and data scientists, as well as AI enthusiasts.

As a thank you, we will send you a cheat sheet with useful resources that help you understand and organize data labeling as well as a $50 promo code for your own project.

1 неделя, 2 дня назад @ thesequence.substack.com
🗣🗣🗣Another Amazing Week for Large Language Models
🗣🗣🗣Another Amazing Week for Large Language Models 🗣🗣🗣Another Amazing Week for Large Language Models

Regularly, we read about massive NLU models reaching new milestones across different language tasks.

This week, we had a fresh taste of the progress with models published by Meta AI and Alexa AI.

This week, Meta AI open-sourced BlederBot 3, a new 175 billion parameter version that achieves over 30% improvement compared to its predecessors across different conversational tasks.

Meta AI released a live demo of BlenderBot 3, allowing users to interact with the chatbot and contribute to its training.

The models released this week by Meta AI and Alexa AI challenge the imagination of the new frontiers for NLU models.

1 неделя, 3 дня назад @ thesequence.substack.com
📝 Guest post: Auto Labeling to Power Insurance Automation: Quickly Label Quality Datasets*
📝 Guest post: Auto Labeling to Power Insurance Automation: Quickly Label Quality Datasets* 📝 Guest post: Auto Labeling to Power Insurance Automation: Quickly Label Quality Datasets*

The Superb AI labeling platform is designed with image and label management features allowing human-in-the-loop labeling of images.

Of these, we initially labeled about 3000 images, then used Superb AI’s Custom Auto Label to label a bit less than another 800 images.

As an example, we auto labeled another 500 images, reviewed them, and used the combined data to train a new Custom Auto Label model.

These results highlighted another useful feature of using the human-in-the-loop auto labeling on Superb AI.

Thus, the auto label process can help improve the dataset quality as well.

1 неделя, 5 дней назад @ thesequence.substack.com
🗺 Edge#214: NLLB-200, Meta AI’s New Super Model that Achieved New Milestones in Machine Translations Across 200 Languages
🗺 Edge#214: NLLB-200, Meta AI’s New Super Model that Achieved New Milestones in Machine Translations Across 200 Languages 🗺 Edge#214: NLLB-200, Meta AI’s New Super Model that Achieved New Milestones in Machine Translations Across 200 Languages

On Thursdays, we dive deep into one of the freshest research papers or technology frameworks that is worth your attention.

💥 What’s New in AI: NLLB-200, Meta AI’s New Super Model that Achieved New Milestones in Machine Translations Across 200 LanguagesMachine translation is one of the deep learning disciplines that can have an immediate social impact.

Expanding translations to hundreds of low-resource languages and dialects is one of the most critical challenges of the next decade of machine translation.

Recently, Meta AI open-sourced no language left behind (NLLB)-200, a model that can perform state-of-the-art machine translation across 200 languages.

Meta AI also open-sourced a few comple…

1 неделя, 6 дней назад @ thesequence.substack.com
🩺 Edge#213: Testing Trained Models
🩺 Edge#213: Testing Trained Models 🩺 Edge#213: Testing Trained Models

Subscribe to TheSequence to keep reading this post and get 7 days of free access to the full post archives.

In our introduction to machine learning (ML) testing ( Edge#209 ), we reviewed two fundamental approaches: pre-training and post-training testing.

Today, we would like to dive deep into the methods for testing trained models.

Most testing techniques for trained models fit into some of the following categories:we explore TensorFlow’s What-If Tool, one of the most commonly used testing tools in the machine learning space.

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2 недели, 1 день назад @ thesequence.substack.com
📝 Guest post: Using AI to Learn a Disentangled Gait Representation for Versatile Quadruped Locomotion*
📝 Guest post: Using AI to Learn a Disentangled Gait Representation for Versatile Quadruped Locomotion* 📝 Guest post: Using AI to Learn a Disentangled Gait Representation for Versatile Quadruped Locomotion*

Project BackgroundQuadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains.

ORI attempt to address this limitation by learning a latent space capturing the key stance phases of a particular gait, via a generative model trained on a single trot style.

The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.

Using a variational auto-encoder (VAE), the ORI approach learns a structured latent space capturing key stance phases constituting a particular gait.

Utilizing a generative model affords detection of disturbances as out of the distribution …

2 недели, 2 дня назад @ thesequence.substack.com
🧬 DeepMind’s AlphaFold Database
🧬 DeepMind’s AlphaFold Database 🧬 DeepMind’s AlphaFold Database

AlphaFold can easily be considered the most relevant ML contribution to the world of science in the last decade.

The initial release of AlphaFold was accompanied by a less promoted project known as AlphaFold Protein Structure Database (AlphaFold DB).

Last week, DeepMind doubled down in AlphaFold DB with a new and incredibly impressive release.

In collaboration with EMBL’s European Bioinformatics Institute (EMBL-EBI), DeepMind upgraded AlphaFold DB with the structure of nearly all catalogued proteins known to science.

By providing access to the structure of proteins in a data-structured, searchable format, AlphaFold DB can drastically advance research across different scientific areas rangin…

2 недели, 3 дня назад @ thesequence.substack.com
📝 Guest post: Getting ML data labeling right*
📝 Guest post: Getting ML data labeling right* 📝 Guest post: Getting ML data labeling right*

In this article, Toloka’s team shares their data labeling experience, looking into three different case studies.

While building production-ready AI solutions is a long process, it starts with gathering and labeling the data used to train ML models.

Search relevanceOne of the most popular use cases of Toloka is offline metrics evaluation of search relevance.

We have shown you examples of data annotation projects for audio transcription, MT evaluation, and search engine evaluation.

We hope they’ve given you a taste of how we approach this problem and an idea of how you can prepare data for your own ML project.

2 недели, 5 дней назад @ thesequence.substack.com
Synced Review
последний пост 1 день, 3 часа назад
Georgia Tech & Google Propose a Novel Discrete Variational Autoencoder for Automatically Improving…
Georgia Tech & Google Propose a Novel Discrete Variational Autoencoder for Automatically Improving… Georgia Tech & Google Propose a Novel Discrete Variational Autoencoder for Automatically Improving…

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1 день, 3 часа назад @ medium.com
Meet Atlas: A Pretrained Retrieval Augmented Language Model That Outperforms a 540B Parameter Model…
Meet Atlas: A Pretrained Retrieval Augmented Language Model That Outperforms a 540B Parameter Model… Meet Atlas: A Pretrained Retrieval Augmented Language Model That Outperforms a 540B Parameter Model…

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Meta AI & Mila Publicly Release BlenderBot 3: A 175B SOTA Chatbot That Continually Improves via…
Meta AI & Mila Publicly Release BlenderBot 3: A 175B SOTA Chatbot That Continually Improves via… Meta AI & Mila Publicly Release BlenderBot 3: A 175B SOTA Chatbot That Continually Improves via…

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6 дней, 14 часов назад @ medium.com
Tencent’s Effidit Significantly Expands the Capabilities of AI Writing Assistants
Tencent’s Effidit Significantly Expands the Capabilities of AI Writing Assistants Tencent’s Effidit Significantly Expands the Capabilities of AI Writing Assistants

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NVIDIA’s Minimal Video Instance Segmentation Framework Achieves SOTA Performance Without…
NVIDIA’s Minimal Video Instance Segmentation Framework Achieves SOTA Performance Without… NVIDIA’s Minimal Video Instance Segmentation Framework Achieves SOTA Performance Without…

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1 неделя, 1 день назад @ medium.com
Microsoft & Arizona U’s TextWorldExpress Simulates Text Games at 1M SPS, a Speedup of 3 Orders of…
Microsoft & Arizona U’s TextWorldExpress Simulates Text Games at 1M SPS, a Speedup of 3 Orders of… Microsoft & Arizona U’s TextWorldExpress Simulates Text Games at 1M SPS, a Speedup of 3 Orders of…

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1 неделя, 2 дня назад @ medium.com
OpenAI Presents a Simple and Efficient Training Strategy to Boost Language Models’ Text-Infilling…
OpenAI Presents a Simple and Efficient Training Strategy to Boost Language Models’ Text-Infilling… OpenAI Presents a Simple and Efficient Training Strategy to Boost Language Models’ Text-Infilling…

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IITM & UT Austin’s Generalizable NeRF Transformer Demonstrates Transformers’ Capabilities for…
IITM & UT Austin’s Generalizable NeRF Transformer Demonstrates Transformers’ Capabilities for… IITM & UT Austin’s Generalizable NeRF Transformer Demonstrates Transformers’ Capabilities for…

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Google Introduces the First Effective Face-Motion Deblurring System for Mobile Phones
Google Introduces the First Effective Face-Motion Deblurring System for Mobile Phones Google Introduces the First Effective Face-Motion Deblurring System for Mobile Phones

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Fancy a Friendly Chat?
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Google & DeepMind Study the Interactions Between Scaling Laws and Neural Network Architectures
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Microsoft, DP Technology & Tsinghua U Enable Efficient Low-Precision Training of Gradient Boosting…
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Amazon’s Sockeye 3: Neural Machine Translation With PyTorch That Is 126% Faster on GPUs
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Google Open-Sources Its TensorFlow GNN Framework to Encourage Graph Neural Network Productization…
Google Open-Sources Its TensorFlow GNN Framework to Encourage Graph Neural Network Productization… Google Open-Sources Its TensorFlow GNN Framework to Encourage Graph Neural Network Productization…

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Colossal-AI Seamlessly Accelerates Large Models at Low Costs with Hugging Face
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📓 Cool Blogs
ODS.ai Habr ODS.ai Habr
последний пост 6 дней, 6 часов назад
Data Science Pet Projects. FAQ
Data Science Pet Projects. FAQ Data Science Pet Projects. FAQ

В своем проекте вы “и спец, и на дудке игрец”, а также PO, CTO, CEO (и немного HR).

Поиск темы проекта и данных для анализаВ пет-проектах по анализу данных тема неразрывно связана с данными.

__поиск данных для примера 1 • существуют датчики, которые определяют шаги, пульс, глубину дыхания, частоту сердцебиения, температуру тела.

Что зарелизили: ML-бот, который умеет передвигаться, основываясь на входящем кадре-картинке и на векторе-состоянии инвентаря.

ODS ник: Sergei Два года пилил пет-проект про GAN/Deepfake, в процессе хорошо прокачался в в DL, описание проекта в хабр-статье.

6 дней, 6 часов назад @ habr.com
Эй-Яй, крипта, MLOps и командный пет-проджект
Эй-Яй, крипта, MLOps и командный пет-проджект Эй-Яй, крипта, MLOps и командный пет-проджект

Третья часть – про организацию работу, сложности, с которыми мы столкнулись, и хаки, повышающие продуктивность команды, к которым мы в итоге пришли.

MLFlow и сервис обучения моделейМы реализовали абстрактный класс для обучения, у котого есть наследники для простой модели tf-idf & logreg и для BERT-модели.

Adversarial Validation для обнаружения дрифта в данныхAdversarial validation – подход, про который я узнал на Kaggle, который, кажется, под разными именами постоянно переизобретается и в академии, и в индустрии.

Роли в командеКажется, что даже в пет-проджекте хорошо бы выделить роли и не толькаться, не бороться за задачи.

Хорошо бы довести пет-проект до красивой демки, на которую можно и в…

1 месяц, 2 недели назад @ habr.com
Как мы заняли 1-е место в задаче Matching в соревновании Data Fusion Contest 2022, или как нейронка обогнала бустинг
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То есть MRR = 1 если правильный ответ на первой позиции, 0.5 если на второй, 0.33 на третьей и 0.01 – если на последней.

Транзакции:Здесь кроме id-клиента есть mcc код и два поля с его текстовым описанием, код валюты с расшифровкой, сумма транзакции (как с плюсом, так и с минусом) и дата-время транзакции.

Мы предполагали, что в этом случае общее векторное пространство будет также на входе в RNN, и что сеть сможет найти, например, похожие по смыслу mcc в транзакциях и категории в кликстриме.

Для поля timestamp посчитали те же временные признаки, что и для транзакций.

Отличие от NLP методов было в том, что мы маскировали и предсказывали не сам токен (транзакцию), а его вектор.

2 месяца, 1 неделя назад @ habr.com
DIY. Книги для всех, даром
DIY. Книги для всех, даром DIY. Книги для всех, даром

Найти их, однако, не так просто, и скорее всего это будут книги для детей или избранная классика.

Долго стоял К. на деревянном мосту, который вел с проезжей дороги в Деревню, и смотрел в кажущуюся пустоту.

Более подробно про интерфейс приложения и как им пользоваться можно почитать здесь, а про технические детали здесь.

Можно выбрать на какой стороне листа будет какой язык и на основе какого исходного текста формировать абзацы параллельного текста.

В шаблонах зашиты переменные для включения цветовой подсветки, путь к картинке для обложки, подсказки для японского и китайского и некоторые другие.

2 месяца, 1 неделя назад @ habr.com
Причинно-следственный анализ в машинном обучении: итоги 2021 г
Причинно-следственный анализ в машинном обучении: итоги 2021 г Причинно-следственный анализ в машинном обучении: итоги 2021 г

А в этой статье - под катом - хотелось бы рассказать о трендах в развитии Causal Inference в ML в 2021 г.Causal Inference в ML: итоги 2021 г.Сначала поговорим обобщенно, а затем детальнее раскроем наиболее интересные пункты.

Нобелевская премия по экономике была выдана за развитие методов CI, крупнейшие конференции по ML провели воркшопы (NeurIPS, ICML) по вопросам CI для ML.

Interpretable & Causal ML Track – Data Fest Online 2021На ежегодном Data Fest уже в третий раз прошел трек по вопросам Reliable ML - Interpretable & Causal ML Track 2021.

Доклад вошел в топ всех выступлений сообщества Open Data Science в 2021 г. Тема с Causal Shapley Values прогремела в 2020 г., в 2021 г.

Появление каче…

2 месяца, 2 недели назад @ habr.com
Система распознавания шрифта Брайля. Читаем написанное белым по белому
Система распознавания шрифта Брайля. Читаем написанное белым по белому Система распознавания шрифта Брайля. Читаем написанное белым по белому

Сейчас этот сервис используют сотни людей и в России, и за ее пределами.

Лень — двигатель прогрессаВы все видели брайлевские символы в лифте и в поликлинике.

В обычном письме буквы представлены связанными линиями, а в письме по Брайлю - комбинацией от 1 до 6 точек, расположенных в узлах воображаемой сетки.

Так что по всему выходило, что для применения опубликованных методов нужен сканер, причем специальный — обычный бытовой мал.

Есть что улучшить и в оптическом распознавании, и в веб-интерфейсе.

2 месяца, 2 недели назад @ habr.com
Интерпретируемость в машинном обучении: итоги 2021 г
Интерпретируемость в машинном обучении: итоги 2021 г Интерпретируемость в машинном обучении: итоги 2021 г

Под катом давайте поговорим о том, что интересного произошло в интерпретируемости в 2021 г.Ключевые тренды и события 2021 г. в Interpretable MLСначала поговорим обобщенно, а затем детальнее раскроем наиболее интересные пункты.

Пожалуй, самое пристальное внимание в области XAI в 2021 г. было направлено на оценку качества методов интерпретации – для возможности сравнения методов между собой.

При этом в январе 2022 г. на arxiv появилась знаковая работа, в которой авторы систематизируют около 300 работ в области XAI, опубликованных на CS конференциях в 2014-2020 гг.

Так, и в 2021 г. в разных бизнес-источниках продолжили ссылаться на отчет PwC по Explainable AI от 2018 г. В обзоре достаточно про…

2 месяца, 3 недели назад @ habr.com
Причинно-следственный анализ в машинном обучении
Причинно-следственный анализ в машинном обучении Причинно-следственный анализ в машинном обучении

В следующей статье побеседуем о ключевых трендах в развитии методов причинно-следственного анализа в машинном обучении в 2020-2021 гг.

Что такое причинно-следственный анализCorrelation doesn't imply causationГлавный тезис эконометрики, который в последние 5 лет прочно пришел и в ML: «Корреляция не подразумевает причинно-следственную связь».

В целом, о кейсах бизнес-применения causal inference 2021 г. я рассказывала в одном из постов tg-канала @Reliable_ML еще в начале года.

Causal Inference как ключ к балансу классического ML и эконометрикиCausal Inference в MLВ 2020 году в отчете State of AI впервые в явном виде была обозначена необходимость интеграции классического ML c методами Causal In…

3 месяца, 3 недели назад @ habr.com
Нюансы распознавания речи. Восстанавливаем пунктуацию, числа и заглавные буквы
Нюансы распознавания речи. Восстанавливаем пунктуацию, числа и заглавные буквы Нюансы распознавания речи. Восстанавливаем пунктуацию, числа и заглавные буквы

Другими словами, сегодня мы поговорим про то, как автоматически восстановить пунктуацию и капитализацию (сделать нужные буквы заглавными).

Если вы делаете модель для малоресурсного языка, то можно воспользоваться проектом Lingtrain, который я делаю для создания параллельных книг (проект открытый и идеи приветствуются).

Если же вы делаете модель для какого-то популярного языка, то можно воспользоваться готовыми датасетами типа Tatoeba.

Для удобства я оформил скрипты для подготовки и обучения в репозиторий multipunct, поэтому дальше я буду обращаться к нему.

Если понадобится экспортировать модель для инференса, например, в TorchScript, то для этого есть метод export().

4 месяца, 1 неделя назад @ habr.com
Чистый AutoML для “грязных” данных: как и зачем автоматизировать предобработку таблиц в машинном обучении
Чистый AutoML для “грязных” данных: как и зачем автоматизировать предобработку таблиц в машинном обучении Чистый AutoML для “грязных” данных: как и зачем автоматизировать предобработку таблиц в машинном обучении

Мы использовали их при развитии нашего open-source AutoML фреймворка FEDOT , у которого безусловно есть свои особенности как в архитектуре, так и в парадигме разработки.

Если хотите подробнее почитать про предобработку табличных данных и какой она бывает, то можете начать с Предварительная обработка данных и продолжить c Data Preprocessing: Concepts .

Оговоримся сразу, что рассматривать будем наиболее критические изменения в данных, преобразования в духе “трансформация одномерного target массива в вектор-столбец” подразумевается, что производится при необходимости в любой сколько-нибудь крупной AutoML библиотеке.

Также имеется возможность опциональной предобработки для некоторых моделей - н…

4 месяца, 2 недели назад @ habr.com
Проблемы современного машинного обучения
Проблемы современного машинного обучения Проблемы современного машинного обучения

Почему ML-модели часто не справляются с такой задачей и что с этим делать – мы рассмотрим далее.

Конечно, этим проблемы машинного обучения не ограничиваются, существуют также сложности с интерпретацией моделей, проблемы предвзятости и этики, ресурсоемкости обучения и прочие.

Начнем с проблемы утечки данных, и затем рассмотрим более общую проблему ограниченного разнообразия обучающих данных и ситуации, когда модель лишь "делает вид", что обучается.

Проблема shortcut learning присутствует и в тех моделях, которые специально дообучались для лидербордов GLUE (Wang et al., 2018) и SuperGLUE (Wang et al., 2019).

Сеть использует признаки, которые позволяют эффективно предсказывать ответ на обучающ…

6 месяцев назад @ habr.com
Новый запуск курса Natural Language Processing
Новый запуск курса Natural Language Processing Новый запуск курса Natural Language Processing

TL;DR: Этой весной сообщество Open Data Science и компания Huawei делают новый запуск курса по обработке естесственного языка.

Мы делаем новый запуск курса Natural Language Processing.

Лекции и семинары будут онлайн.

Я буду читать лекции, в области NLP я работаю последние 10 лет, успел поработать в Яндексе и ВКонтакте, защитить кандидатскую диссертацию.

Ссылка будет в канале курса в слаке ODS.

6 месяцев, 1 неделя назад @ habr.com
CatBoost, XGBoost и выразительная способность решающих деревьев
CatBoost, XGBoost и выразительная способность решающих деревьев CatBoost, XGBoost и выразительная способность решающих деревьев

Для каждого листа, признака и порога определяем значения и на листьях и считаем функцию потерь получившегося дерева.

Запишем суммарную функцию потерь после разделения, и минимизируем ее по и :Первое слагаемое зависит только от , второе только от , поэтому их можно минимизировать независимо по и , если известны примеры, попавшие в один и в другой лист.

Суммируем ошибку по всем классам и будем искать минимум по :Из данной формулы можно исключить , и тогда задача нахождения оптимального распределения сводится к минимизации перекрестной энтропии между распределениями и .

Это эквивалентно минимизации расхождения Кульбака-Лейблера между и и достигается при для всех .

Другие особенности решающих д…

6 месяцев, 4 недели назад @ habr.com
Интерпретация моделей и диагностика сдвига данных: LIME, SHAP и Shapley Flow
Интерпретация моделей и диагностика сдвига данных: LIME, SHAP и Shapley Flow Интерпретация моделей и диагностика сдвига данных: LIME, SHAP и Shapley Flow

Также поговорим о проблемах метода SHAP и его дальнейшем развитии в виде метода Shapley Flow, объединяющего интерпретацию модели и многообразия данных.

Утечка данных является частным случаем сдвига данных, поскольку зависимость ID диагноз была в и , но ее не будет в .

Проблемы и ограничения SHAP valuesНа практике рассчет SHAP values позволяет интерпретировать модель, выявлять скрытые проблемы в модели и данных и даже выполнять кластеризацию, что мы увидим далее в разделе "SHAP на практике".

SHAP values можно посчитать по формуле , но количество слагаемых экспоненциально зависит от количества признаков, и алгоритм рассчета SHAP values в общем случае является NP-полным.

Можно также заметить, …

7 месяцев назад @ habr.com
Выбираем инструмент для разметки текста (и не только!)
Выбираем инструмент для разметки текста (и не только!) Выбираем инструмент для разметки текста (и не только!)

Список фичей в платной версиии вполне достойный, хоть и не самый впечатляющий (ничего такого, что я не нашел бы в labelstudio).

Документация на троечку и не отвечает на многие вопросы.

И это не так легко, особенно когда речь идет об аудиозаписях.

Это самая большая проблема подобных платформ - не имея собственных разметчиков вы не можете контролировать качество разметки и обучать людей тому, как размечать правильно (да, это не так просто, как кажется!).

Через UI загрузка и выгрузка работает отлично - ты кидаешь .txt или .csv/.tsv файл - и на каждую строчку создается отдельная задача внутри проекта.

7 месяцев, 3 недели назад @ habr.com
Machine Learning Mastery
последний пост 4 недели назад
Image Augmentation with Keras Preprocessing Layers and tf.image
Image Augmentation with Keras Preprocessing Layers and tf.image

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4 недели назад @ machinelearningmastery.com
Image Augmentation for Deep Learning with Keras
Image Augmentation for Deep Learning with Keras

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1 месяц назад @ machinelearningmastery.com
Loss Functions in TensorFlow
Loss Functions in TensorFlow

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1 месяц назад @ machinelearningmastery.com
High-Fidelity Synthetic Data for Data Engineers and Data Scientists Alike
High-Fidelity Synthetic Data for Data Engineers and Data Scientists Alike

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1 месяц назад @ machinelearningmastery.com
Understanding the Design of a Convolutional Neural Network
Understanding the Design of a Convolutional Neural Network

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A Gentle Introduction to tensorflow.data API
A Gentle Introduction to tensorflow.data API

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1 месяц назад @ machinelearningmastery.com
Using Learning Rate Schedules for Deep Learning Models in Python with Keras
Using Learning Rate Schedules for Deep Learning Models in Python with Keras

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1 месяц, 1 неделя назад @ machinelearningmastery.com
Binary Classification Tutorial with the Keras Deep Learning Library
Binary Classification Tutorial with the Keras Deep Learning Library

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1 месяц, 1 неделя назад @ machinelearningmastery.com
Dropout Regularization in Deep Learning Models With Keras
Dropout Regularization in Deep Learning Models With Keras

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1 месяц, 1 неделя назад @ machinelearningmastery.com
Using Activation Functions in Neural Networks
Using Activation Functions in Neural Networks

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1 месяц, 2 недели назад @ machinelearningmastery.com
How to Check-Point Deep Learning Models in Keras
How to Check-Point Deep Learning Models in Keras

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1 месяц, 2 недели назад @ machinelearningmastery.com
How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras
How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras

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1 месяц, 2 недели назад @ machinelearningmastery.com
Three Ways to Build Machine Learning Models in Keras
Three Ways to Build Machine Learning Models in Keras

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1 месяц, 2 недели назад @ machinelearningmastery.com
Using autograd in TensorFlow to Solve a Regression Problem
Using autograd in TensorFlow to Solve a Regression Problem

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1 месяц, 2 недели назад @ machinelearningmastery.com
Overview of Some Deep Learning Libraries
Overview of Some Deep Learning Libraries

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1 месяц, 3 недели назад @ machinelearningmastery.com
ML in Production
последний пост 3 месяца, 1 неделя назад
Driving Experimentation Forward through a Working Group (Experimentation Program Series: Guide 03)
Driving Experimentation Forward through a Working Group (Experimentation Program Series: Guide 03) Driving Experimentation Forward through a Working Group (Experimentation Program Series: Guide 03)

The experimentation working group is a group of individuals whose goal is to implement the experimentation program i.e.

Data Science Participation in the Working Group Which data science team members should participate in the working group?

This person is responsible for organizing the working group, leading the working group meetings (we’ll discuss this next), and influencing the working group participants.

The Working Group Meeting The working group should meet regularly to achieve it’s goal of implementing the ExPr.

Tactical Tips for Running an Experimentation Working Group Lets discuss a few tactical tips for improving the the working group’s probability of success.

3 месяца, 1 неделя назад @ mlinproduction.com
What is an Experimentation program and Who is Involved? (Experimentation Program Series: Guide 02)
What is an Experimentation program and Who is Involved? (Experimentation Program Series: Guide 02) What is an Experimentation program and Who is Involved? (Experimentation Program Series: Guide 02)

An experimentation program is the mechanism by which a company uses randomized controlled experiments to generate positive business results.

An experimentation program can succeed only when the right people are involved.

Exactly how this ideation, planning, implementation, and analysis is completed is the process component of an experimentation program.

Data Science Data science plays two important roles in an effective experimentation program.

This second role should be played by data science managers or product managers who sit on a data science team.

4 месяца, 2 недели назад @ mlinproduction.com
Building An Effective Experimentation Program – 01 Introduction
Building An Effective Experimentation Program – 01 Introduction Building An Effective Experimentation Program – 01 Introduction

These are some of the words used to describe the products and services offered by the world’s largest and most successful businesses.

But it’s very likely that this experience wasn’t crafted in some sort of top-down, divine-intervention-like manner either.

Many business leaders today are familiar with examples of companies that have evolved their products and services, and correspondingly optimized their profit-and-loss statements, through experimentation.

Data science teams don’t need to be convinced of the benefits of running experiments.

But often they lack the business knowledge, cross-team relationships, and structured processes for engaging with business teams and helping them optimiz…

5 месяцев назад @ mlinproduction.com
Protected: TODO
Protected: TODO

My goal is to help data scientists, ML engineers, and AI product managers, build and operate machine learning systems in production.

Learn more about why I started MLinProduction.

6 месяцев, 1 неделя назад @ mlinproduction.com
Sorta Insightful Sorta Insightful
последний пост 1 месяц назад
I'm Bad at Twitter
I'm Bad at Twitter I'm Bad at Twitter

I’m bad at Twitter.

I know I’m bad at Twitter.

There’s a machine learning Twitter, a philosophy Twitter, a history Twitter, a My Little Pony Twitter, a Smash Bros Twitter.

People tell me ML Twitter is worth it.

It’s quite likely that I’m losing out on both ML knowledge and career equity by not being more active on Twitter.

1 месяц назад @ alexirpan.com
My 2022 r/place Adventure
My 2022 r/place Adventure My 2022 r/place Adventure

Lots of big communities have little interest in r/place, and lots of little communities have outsized presence in r/place.

The Dustforce Discord talked about doing something for r/place, but hadn’t done anything, so I made a pixel art template in hopes it would get the ball rolling.

After scanning existing r/place pixel art, I realized our target image was somewhat big for our community size, so I prepared a smaller version instead.

Our art template and their art template overlapped by 1 pixel, and we both really wanted that pixel.

We even had time to adjust our template and fill in more space with Dustforce pixel art, adding the S+ icon we had last time r/place happened.

3 месяца, 2 недели назад @ alexirpan.com
The Dawn of Do What I Mean
The Dawn of Do What I Mean The Dawn of Do What I Mean

SayCan is a robot learning system that we’ve been developing for about the past year.

The language generation is the easy part, while the value function + policy are the hard parts.

Meanwhile, Google Brain announced their PaLM language model, trained with 540B parameters on 780 billion tokens.

Let’s just say it’s not a good look for anyone claiming deep learning models are plateauing.

Similar to language generation, progress here might overstate the state of the field, because it’s improving things we naturally find interesting.

4 месяца назад @ alexirpan.com
MIT Mystery Hunt 2022
MIT Mystery Hunt 2022 MIT Mystery Hunt 2022

This has spoilers for MIT Mystery Hunt 2022.

Since the winner of MIT Mystery Hunt has to write the next year’s hunt, things will be…interesting for the next year.

Previous Mystery Hunt puzzles have used gimmicked dice before.

The gimmicked dice from the previous Mystery Hunt puzzle were more clearly gimmicked than our dice.

Maybe I’m a little jaded, but winning Mystery Hunt didn’t light any big fires of motivation for me.

6 месяцев, 3 недели назад @ alexirpan.com
"My Soul is Pony-Scarred for Life Because of You"
"My Soul is Pony-Scarred for Life Because of You" "My Soul is Pony-Scarred for Life Because of You"

In that moment, the brony fandom they never predicted clarified into something real.

Elements of this appear in many fandoms, but brony fandom was unusually prolific.

I think the explosion of MLP fan content is most easily explained by the MLP universe’s untapped potential.

Sturgeon’s Law still applies, but the MLP fandom was prolific enough to produce lots of content in the 10% that wasn’t crap.

Pony fandom was a sizable part of my life for 10 years.

7 месяцев, 2 недели назад @ alexirpan.com
Review: How To Invent Everything
Review: How To Invent Everything Review: How To Invent Everything

How to Invent Everything is a book by Ryan North, of Dinosaur Comics fame.

How to Invent Everything doesn’t literally cover everything, but you can see it as a book about the highlights of civilization.

Hot air balloons took a really long time to invent, given that baskets, controlled fire, and fabric all existed for centuries.

One question How To Invent Everything toys with is what point in history would let you most influence the trajectory of humanity.

Both these technologies are so incredibly useful to society that it’s hard to see how progress was made before their invention, and they look a long time to invent.

9 месяцев, 3 недели назад @ alexirpan.com
Lil'Log
последний пост 5 месяцев, 4 недели назад
Learning with not Enough Data Part 2: Active Learning
Learning with not Enough Data Part 2: Active Learning Learning with not Enough Data Part 2: Active Learning

Active learning is one paradigm to deal with not enough labeled data, when there are resources for labeling more data samples but under a limited budget.

(2019) proposed a GAN-like setup, named VAAL (Variational Adversarial Active Learning), where a discriminator is trained to distinguish unlabeled data from labeled data.

Bayesian Active Learning for Classification and Preference Learning.” arXiv preprint arXiv:1112.5745 (2020).

“Learning Loss for Active Learning.” CVPR 2019.

“When Deep Learners Change Their Mind: Learning Dynamics for Active Learning.” CAIP 2021.

5 месяцев, 4 недели назад @ lilianweng.github.io
Learning with not Enough Data Part 1: Semi-Supervised Learning
Learning with not Enough Data Part 1: Semi-Supervised Learning Learning with not Enough Data Part 1: Semi-Supervised Learning

Semi-supervised learning is one candidate, utilizing a large amount of labeled data conjunction with a small amount of labeled data.

They further set a minimum number of labeled samples in every mini batch by oversampling the labeled samples.

Given a batch of labeled data \(\mathcal{X}\) and unlabeled data \(\mathcal{U}\), we create augmented versions of them via \(\text{MixMatch}(.

A semi-supervised learning framework leverages unlabeled data corpus by (Left) task-agnostic unsupervised pretraining and (Right) task-specific self-training and distillation.

2020:Bigger models are more label-efficient;Bigger/deeper project heads in SimCLR improve representation learning;Distillation using unla…

8 месяцев, 2 недели назад @ lilianweng.github.io
inFERENCe
последний пост 5 месяцев, 2 недели назад
Implicit Bayesian Inference in Large Language Models?
Implicit Bayesian Inference in Large Language Models? Implicit Bayesian Inference in Large Language Models?

March 3, 2022Implicit Bayesian Inference in Large Language Models?

Exchangeable sequences as Implicit Learning MachinesBefore talking about the paper, let me first refresh those old ideas about exchangeable sequences and implicit learning.

The de Finetti theorem connects such sequence models to Bayesian inference, saying that any such distribution can be decomposed as a mixture over i.i.d.

From Exchangeable sequences to Mixtures of HMMsBut GPT-3 is a language models, and clearly language tokens are not exchangeble.

In context learningThe core idea of this paper is that perhaps in-context learning exploits this implicit Bayesian inference, inherent to statistical models of language, to solve…

5 месяцев, 2 недели назад @ inference.vc
The Eastern European's Guide to Writing Reference Letters
The Eastern European's Guide to Writing Reference Letters The Eastern European's Guide to Writing Reference Letters

One phrase I often use to describe what it's like to read reference letters for Eastern European applicants to PhD and Master's programs in Cambridge.

I decided to write this guide for students so they can share it with their professors when asking for reference letters.

Reference letters are often used as ammunition to justify decisions internally, and to determine who gets prioritised for various scholarship and funding competitions.

Reference letters are often used as ammunition to justify decisions internally, and to determine who gets prioritised for various scholarship and funding competitions.

If you have a candidate you enthusiastically support, don't be afraid to ask for help writi…

5 месяцев, 2 недели назад @ inference.vc
The Spectator
последний пост None
Off the Convex Path
последний пост 1 месяц назад
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Networks
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Networks Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Networks

We find that, analogously to matrix and tensor factorizations, the implicit regularization in hierarchical tensor factorization strives to lower a notion of rank (called hierarchical tensor rank).

For our current purpose it suffices to know that a hierarchical tensor factorization consists of multiple local tensor factorizations, whose components we call the local components of the hierarchical factorization.

Basically, if a tensor can be represented through hierarchical tensor factorization with few local components, then it has low hierarchical tensor rank.

Seeing that the implicit regularization in matrix and tensor factorizations leads to low matrix and tensor ranks, respectively, in ou…

1 месяц назад @ offconvex.org
Predicting Generalization using GANs
Predicting Generalization using GANs Predicting Generalization using GANs

Predicting Generalization using GANsA central problem of generalization theory is the following: Given a training dataset and a deep net trained with that dataset, give a mathematical estimate of the test error.

This blog post is about the topic of a NeurIPS 20 competition Predicting Generalization in Deep Learning competition which suggested using machine learning techniques to understand network properties that promote generalization!

This blog post describes our ICLR22 spotlight paper, coauthored with Nikunj Saunshi and Arushi Gupta, that gives a surprisingly easy method to predict generalization using Generative Adversarial Nets or GANs.

Observation 2) Training deep net classifiers usin…

2 месяца, 1 неделя назад @ offconvex.org
Does Gradient Flow Over Neural Networks Really Represent Gradient Descent?
Does Gradient Flow Over Neural Networks Really Represent Gradient Descent? Does Gradient Flow Over Neural Networks Really Represent Gradient Descent?

Does Gradient Flow Over Neural Networks Really Represent Gradient Descent?

Researchers often conduct such studies by considering gradient flow (GF), equivalent to GD with infinitesimally small step size.

This would imply $\boldsymbol\theta ( t + \eta ) = \boldsymbol\theta ( t ) - \etaabla f ( \boldsymbol\theta ( t ) )$, which we can write as $\frac{1}{\eta} ( \boldsymbol\theta ( t + \eta ) - \boldsymbol\theta ( t ) ) = -abla f ( \boldsymbol\theta ( t ) )$.

Figure 2: Experiment with NN comparing every iteration of GD with step size $\eta_0 := 0.001$, to every $r$'th iteration of GD with step size $\eta_0 / r$, where $r = 2 , 5 , 10 , 20$.

Left plot shows training loss values; right one shows…

7 месяцев, 1 неделя назад @ offconvex.org
Jay Alammar
последний пост 5 месяцев, 1 неделя назад
Applying massive language models in the real world with Cohere
Applying massive language models in the real world with Cohere Applying massive language models in the real world with Cohere

The company trains massive language models (both GPT-like and BERT-like) and offers them as an API (which also supports finetuning).

Semantic search has to be one of the most exciting applications of sentence embedding models.

Finetuning tends to lead to the best results language models can achieve.

This article explains the intuitions around finetuning representation/sentence embedding models.

This is a walkthrough of one of the most common use cases of embedding models -- text classification.

5 месяцев, 1 неделя назад @ jalammar.github.io
The Illustrated Retrieval Transformer
The Illustrated Retrieval Transformer The Illustrated Retrieval Transformer

The last few years saw the rise of Large Language Models (LLMs) – machine learning models that rapidly improve how machines process and generate language.

By including a retrieval method in the language model, the model can be much smaller.

Aiding language models with retrieval methods allows us to reduce the amount of information a language model needs to encode in its parameters to perform well at text generation.

Aiding language models with retrieval methods allows us to reduce the amount of information a language model needs to encode in its parameters to perform well at text generation.

Input prompt reaches RETRO Decoder block to start information retrieval Input prompt reaches RETRO D…

7 месяцев, 2 недели назад @ jalammar.github.io
Piekniewski's blog
последний пост 8 месяцев назад
Farcical Self-Delusion
Farcical Self-Delusion Farcical Self-Delusion

And that is exactly what is going on with Tesla FSD.

The excitement and media coverage of this event gathered attention of Silicon Valley and gave rise to a self driving car project at Google in January of 2009.

The appearance of Google "self driving" prototypes in the streets of San Francisco in the early part of 2010's ignited a hysteria in the Bay Area, where the "self driving car" became the next big thing.

It is not clear if a control system for a complex environment can exist in a form that does not continuously learn online.

What we have instead of a safe self driving car, is a farcical comedy of silly mistakes, even in the best of conditions.

8 месяцев назад @ blog.piekniewski.info
Brain computer confusion
Brain computer confusion Brain computer confusion

We learned for example that everything we can write an equation for can be in principle calculated on a computer.

We take for granted that we can write equations for molecules, yet this isn't really the case.

We can write equations for "approximations" of molecules, ignoring some of the details.

This entire mental exercise that tries to fit the entire Universe into a giant Turing machine is fundamentally flawed!!!

And the straightforward consequence of this flawed philosophy is that brain is just yet another computer, and since we build faster computers every day, it's just a matter of time when we build one as sophisticated as the brain.

9 месяцев назад @ blog.piekniewski.info
fast.ai NLP fast.ai NLP
последний пост None
Sebastian Ruder Sebastian Ruder
последний пост None
🔬 Science
Papers With Code Papers With Code
последний пост 1 час назад
/chfocus/ Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing
/chfocus/ Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing /chfocus/ Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing

In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice.

To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution.

Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users' energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated.

This problem is proved to …

1 час назад @ paperswithcode.com
/timdettmers/ LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
/timdettmers/ LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale /timdettmers/ LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

We develop a procedure for Int8 matrix multiplication for feed-forward and attention projection layers in transformers, which cut the memory needed for inference by half while retaining full precision performance.

To cope with these features, we develop a two-part quantization procedure, LLM.int8().

We first use vector-wise quantization with separate normalization constants for each inner product in the matrix multiplication, to quantize most of the features.

However, for the emergent outliers, we also include a new mixed-precision decomposition scheme, which isolates the outlier feature dimensions into a 16-bit matrix multiplication while still more than 99.9% of values are multiplied in 8…

1 час назад @ paperswithcode.com
/ppml38/ Novel algorithm to generate shortest edit script using Levenshtein distance algorithm
/ppml38/ Novel algorithm to generate shortest edit script using Levenshtein distance algorithm /ppml38/ Novel algorithm to generate shortest edit script using Levenshtein distance algorithm

String similarity, longest common subsequence and shortest edit scripts are the triplets of problem that related to each other.

There are different algorithms exist to generate edit script by solving longest common subsequence problem.

This paper proposes an algorithm that uses string similarity problem to generate shortest edit script.

For this we use the famous Levenshtein distance algorithm, which computes a numerical value that represents similarity between the strings from 0 to n, where n is the length of longest input string, and produce the shortest edit script which contains instructions of Insert, Delete and Substitute.

2 часа назад @ paperswithcode.com
/yuzhu2019/ Hypergraphs with Edge-Dependent Vertex Weights: p-Laplacians and Spectral Clustering
/yuzhu2019/ Hypergraphs with Edge-Dependent Vertex Weights: p-Laplacians and Spectral Clustering /yuzhu2019/ Hypergraphs with Edge-Dependent Vertex Weights: p-Laplacians and Spectral Clustering

We study p-Laplacians and spectral clustering for a recently proposed hypergraph model that incorporates edge-dependent vertex weights (EDVWs).

By constructing submodular EDVWs-based splitting functions, we convert hypergraphs with EDVWs into submodular hypergraphs for which the spectral theory is better developed.

We then utilize this eigenvector to cluster the vertices, achieving higher clustering accuracy than traditional spectral clustering based on the 2-Laplacian.

More broadly, the proposed algorithm works for all submodular hypergraphs that are graph reducible.

Numerical experiments using real-world data demonstrate the effectiveness of combining spectral clustering based on the 1-La…

5 часов назад @ paperswithcode.com
/skblaz/ Retrieval-efficiency trade-off of Unsupervised Keyword Extraction
/skblaz/ Retrieval-efficiency trade-off of Unsupervised Keyword Extraction /skblaz/ Retrieval-efficiency trade-off of Unsupervised Keyword Extraction

The graph-based methods can be computationally amongst the most efficient ones, while maintaining the retrieval performance.

One of the main properties, common to graph-based methods, is their immediate conversion of token space into graphs, followed by subsequent processing.

In this paper, we explore a novel unsupervised approach which merges parts of a document in sequential form, prior to construction of the token graph.

Further, by leveraging personalized PageRank, which considers frequencies of such sub-phrases alongside token lengths during node ranking, we demonstrate state-of-the-art retrieval capabilities while being up to two orders of magnitude faster than current state-of-the-ar…

6 часов назад @ paperswithcode.com
/statisticalreinforcementlearninglab/ Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care
/statisticalreinforcementlearninglab/ Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care /statisticalreinforcementlearninglab/ Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care

However, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients.

Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors.

In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors.

We address this challenge by designing a quality reward which maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden.

The RL algorithm discussed in this paper will be deployed in Oralytics, an oral self-care app that provides behavioral strategies to boost patient en…

6 часов назад @ paperswithcode.com
/bflashcp3f/ SynKB: Semantic Search for Synthetic Procedures
/bflashcp3f/ SynKB: Semantic Search for Synthetic Procedures /bflashcp3f/ SynKB: Semantic Search for Synthetic Procedures

In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols.

Similar to proprietary chemistry databases such as Reaxsys, SynKB allows chemists to retrieve structured knowledge about synthetic procedures.

By taking advantage of recent advances in natural language processing for procedural texts, SynKB supports more flexible queries about reaction conditions, and thus has the potential to help chemists search the literature for conditions used in relevant reactions as they design new synthetic routes.

Using customized Transformer models to automatically extract information from 6 million synthesis procedures described in U.S. and EU p…

6 часов назад @ paperswithcode.com
/jeovafarias/ Estimating Appearance Models for Image Segmentation via Tensor Factorization
/jeovafarias/ Estimating Appearance Models for Image Segmentation via Tensor Factorization /jeovafarias/ Estimating Appearance Models for Image Segmentation via Tensor Factorization

Image Segmentation is one of the core tasks in Computer Vision and solving it often depends on modeling the image appearance data via the color distributions of each it its constituent regions.

Whereas many segmentation algorithms handle the appearance models dependence using alternation or implicit methods, we propose here a new approach to directly estimate them from the image without prior information on the underlying segmentation.

Our method uses local high order color statistics from the image as an input to tensor factorization-based estimator for latent variable models.

This approach is able to estimate models in multiregion images and automatically output the regions proportions wi…

6 часов назад @ paperswithcode.com
/szubing/ Counterfactual Supervision-based Information Bottleneck for Out-of-Distribution Generalization
/szubing/ Counterfactual Supervision-based Information Bottleneck for Out-of-Distribution Generalization /szubing/ Counterfactual Supervision-based Information Bottleneck for Out-of-Distribution Generalization

Learning invariant (causal) features for out-of-distribution (OOD) generalization has attracted extensive attention recently, and among the proposals invariant risk minimization (IRM) (Arjovsky et al., 2019) is a notable solution.

In spite of its theoretical promise for linear regression, the challenges of using IRM in linear classification problems yet remain (Rosenfeld et al.,2020, Nagarajan et al., 2021).

Along this line, a recent study (Arjovsky et al., 2019) has made a first step and proposes a learning principle of information bottleneck based invariant risk minimization (IB-IRM).

To further answer the question of whether IB-IRM is sufficient for learning invariant features in linear …

6 часов назад @ paperswithcode.com
/zhenanfanubc/ Knowledge-Injected Federated Learning
/zhenanfanubc/ Knowledge-Injected Federated Learning /zhenanfanubc/ Knowledge-Injected Federated Learning

Federated learning is an emerging technique for training models from decentralized data sets.

In many applications, data owners participating in the federated learning system hold not only the data but also a set of domain knowledge.

Such knowledge includes human know-how and craftsmanship that can be extremely helpful to the federated learning task.

In this work, we propose a federated learning framework that allows the injection of participants' domain knowledge, where the key idea is to refine the global model with knowledge locally.

The scenario we consider is motivated by a real industry-level application, and we demonstrate the effectiveness of our approach to this application.

6 часов назад @ paperswithcode.com
/medical-image-analysis-laboratory/ Multi-dimensional topological loss for cortical plate segmentation in fetal brain MRI
/medical-image-analysis-laboratory/ Multi-dimensional topological loss for cortical plate segmentation in fetal brain MRI /medical-image-analysis-laboratory/ Multi-dimensional topological loss for cortical plate segmentation in fetal brain MRI

The fetal cortical plate (CP) undergoes drastic morphological changes during the in utero development.

Therefore, CP growth and folding patterns are key indicator in the assessment of the brain development and maturation.

In this study, we propose a deep learning segmentation framework for automatic and morphologically consistent segmentation of the CP in fetal brain MRI.

First, we generalized a multi-dimensional topological loss function in order to enhance the topological accuracy.

Overall, both quantitative and qualitative results, on a wide range of gestational ages and number of cases, support the generalizability and added value of our topology-guided framework for fetal CP segmentati…

6 часов назад @ paperswithcode.com
/olfub/ Combining Predictions under Uncertainty: The Case of Random Decision Trees
/olfub/ Combining Predictions under Uncertainty: The Case of Random Decision Trees /olfub/ Combining Predictions under Uncertainty: The Case of Random Decision Trees

A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class.

The latter takes uncertainty into account, but not the reliability of the uncertainty estimates (so to say, the "uncertainty about the uncertainty").

Our methods are inspired by the theories of probability, belief functions and reliable classification, as well as a principle that we call evidence accumulation.

Our experiments on a variety of data sets are based on random decision trees which guarantees a high diversity in the predictions to be combined.

However, evidence accumulation showed consistently better results on all but ver…

7 часов назад @ paperswithcode.com
/arthur-qiu/ StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3
/arthur-qiu/ StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3 /arthur-qiu/ StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3

Realistic generative face video synthesis has long been a pursuit in both computer vision and graphics community.

However, existing face video generation methods tend to produce low-quality frames with drifted facial identities and unnatural movements.

To tackle these challenges, we propose a principled framework named StyleFaceV, which produces high-fidelity identity-preserving face videos with vivid movements.

Extensive experiments demonstrate that our framework achieves state-of-the-art face video generation results both qualitatively and quantitatively.

Notably, StyleFaceV is capable of generating realistic $1024\times1024$ face videos even without high-resolution training videos.

13 часов назад @ paperswithcode.com
/jbrich95/ A unifying partially-interpretable framework for neural network-based extreme quantile regression
/jbrich95/ A unifying partially-interpretable framework for neural network-based extreme quantile regression /jbrich95/ A unifying partially-interpretable framework for neural network-based extreme quantile regression

Risk management in many environmental settings requires an understanding of the mechanisms that drive extreme events.

Useful metrics for quantifying such risk are extreme quantiles of response variables conditioned on predictor variables that describe e.g., climate, biosphere and environmental states.

Typically these quantiles lie outside the range of observable data and so, for estimation, require specification of parametric extreme value models within a regression framework.

In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neutral networks, which are able to capture complex non-linear relationships and scale well to high-…

13 часов назад @ paperswithcode.com
/ict-bigdatalab/ CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks
/ict-bigdatalab/ CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks /ict-bigdatalab/ CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks

Knowledge-intensive language tasks (KILT) usually require a large body of information to provide correct answers.

Recently, the reader component has witnessed significant advances with the help of large-scale pre-trained generative models.

We show that a strong generative retrieval model can be learned with a set of adequately designed pre-training tasks, and be adopted to improve a variety of downstream KILT tasks with further fine-tuning.

We name the pre-trained generative retrieval model as CorpusBrain as all information about the corpus is encoded in its parameters without the need of constructing additional index.

Empirical results show that CorpusBrain can significantly outperform str…

13 часов назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 1 час назад
/air-discover/ Language-guided Semantic Style Transfer of 3D Indoor Scenes
/air-discover/ Language-guided Semantic Style Transfer of 3D Indoor Scenes /air-discover/ Language-guided Semantic Style Transfer of 3D Indoor Scenes

We address the new problem of language-guided semantic style transfer of 3D indoor scenes.

The input is a 3D indoor scene mesh and several phrases that describe the target scene.

Firstly, 3D vertex coordinates are mapped to RGB residues by a multi-layer perceptron.

Secondly, colored 3D meshes are differentiablly rendered into 2D images, via a viewpoint sampling strategy tailored for indoor scenes.

(2) rendering 3D indoor scenes from viewpoints consistent with human priors is important.

13 часов назад @ paperswithcode.com
/darioamorosodaragona-tuni/ Machine Learning-Based Test Smell Detection
/darioamorosodaragona-tuni/ Machine Learning-Based Test Smell Detection /darioamorosodaragona-tuni/ Machine Learning-Based Test Smell Detection

Context: Test smells are symptoms of sub-optimal design choices adopted when developing test cases.

Previous studies have proved their harmfulness for test code maintainability and effectiveness.

Objective: We propose the design and experimentation of a novel test smell detection approach based on machine learning to detect four test smells.

Method: We plan to develop the largest dataset of manually-validated test smells.

This dataset will be leveraged to train six machine learners and assess their capabilities in within- and cross-project scenarios.

13 часов назад @ paperswithcode.com
/thudm/ Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries
/thudm/ Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries /thudm/ Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries

Knowledge graph (KG) embeddings have been a mainstream approach for reasoning over incomplete KGs.

However, limited by their inherently shallow and static architectures, they can hardly deal with the rising focus on complex logical queries, which comprise logical operators, imputed edges, multiple source entities, and unknown intermediate entities.

In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies.

We then formulate the complex logical queries as masked prediction and introduce a two-stage masked pre-training strategy to improve transferability and generalizability.

Additionally, kgTransformer can reason with explain…

13 часов назад @ paperswithcode.com
/eyalsel/ Context-Aware Streaming Perception in Dynamic Environments
/eyalsel/ Context-Aware Streaming Perception in Dynamic Environments /eyalsel/ Context-Aware Streaming Perception in Dynamic Environments

Efficient vision works maximize accuracy under a latency budget.

However, real-time vision applications like autonomous driving operate in streaming settings, where ground truth changes between inference start and finish.

Therefore, a recent work proposed to maximize accuracy in streaming settings on average.

In this paper, we propose to maximize streaming accuracy for every environment context.

Our method, Octopus, uses these scenario properties to select configurations that maximize streaming accuracy at test time.

13 часов назад @ paperswithcode.com
/zhihanyang2022/ Training Latent Variable Models with Auto-encoding Variational Bayes: A Tutorial
/zhihanyang2022/ Training Latent Variable Models with Auto-encoding Variational Bayes: A Tutorial /zhihanyang2022/ Training Latent Variable Models with Auto-encoding Variational Bayes: A Tutorial

Auto-encoding Variational Bayes (AEVB) is a powerful and general algorithm for fitting latent variable models (a promising direction for unsupervised learning), and is well-known for training the Variational Auto-Encoder (VAE).

In this tutorial, we focus on motivating AEVB from the classic Expectation Maximization (EM) algorithm, as opposed to from deterministic auto-encoders.

We discuss how approximate E-step can be interpreted as performing variational inference.

Finally, we derive from scratch the AEVB training procedures of a non-deep and several deep latent variable models, including VAE, Conditional VAE, Gaussian Mixture VAE and Variational RNN.

It is our hope that readers would recog…

13 часов назад @ paperswithcode.com
/imperial-qore/ DRAGON: Decentralized Fault Tolerance in Edge Federations
/imperial-qore/ DRAGON: Decentralized Fault Tolerance in Edge Federations /imperial-qore/ DRAGON: Decentralized Fault Tolerance in Edge Federations

Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers.

A key challenge in such systems is the deployment of latency-critical and AI based resource-intensive applications in constrained devices.

To address this challenge, we propose a novel memory-efficient deep learning based model, namely generative optimization networks (GON).

Leveraging the low memory footprint of GONs, we propose a decentralized fault-tolerance method called DRAGON that runs simulations (as per a digital modeling twin) to quickly predict and optimize the performance of the edge federation.

Extensive experiments with real-world edge computing benchmarks…

13 часов назад @ paperswithcode.com
/ma-787639046/ ConTextual Mask Auto-Encoder for Dense Passage Retrieval
/ma-787639046/ ConTextual Mask Auto-Encoder for Dense Passage Retrieval /ma-787639046/ ConTextual Mask Auto-Encoder for Dense Passage Retrieval

Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i.e., vectors) of the query and the passages.

Recent studies have explored improving pre-trained language models to boost dense retrieval performance.

This paper proposes CoT-MAE (ConTextual Masked Auto-Encoder), a simple yet effective generative pre-training method for dense passage retrieval.

Precisely, self-supervised masked auto-encoding learns to model the semantics of the tokens inside a text span, and context-supervised masked auto-encoding learns to model the semantical correlation between the text spans.

We conduct experiments on large-scale passage retrieva…

13 часов назад @ paperswithcode.com
/taeu/ Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment
/taeu/ Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment /taeu/ Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment

HairFIT, a pose-invariant hairstyle transfer model, alleviates this limitation yet still shows unsatisfactory quality in preserving delicate hair textures.

To solve these limitations, we propose a high-performing pose-invariant hairstyle transfer model equipped with latent optimization and a newly presented local-style-matching loss.

In the StyleGAN2 latent space, we first explore a pose-aligned latent code of a target hair with the detailed textures preserved based on local style matching.

Then, our model inpaints the occlusions of the source considering the aligned target hair and blends both images to produce a final output.

The experimental results demonstrate that our model has strengt…

13 часов назад @ paperswithcode.com
/jianjin008/ HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation
/jianjin008/ HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation /jianjin008/ HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation

However, they have a major drawback that the generated JND is assessed in the real-world signal domain instead of in the perceptual domain in the human brain.

Hence, we propose an HVS-inspired signal degradation network for JND estimation.

To achieve this, we carefully analyze the HVS perceptual process in JND subjective viewing to obtain relevant insights, and then design an HVS-inspired signal degradation (HVS-SD) network to represent the signal degradation in the HVS.

On the one hand, the well learnt HVS-SD enables us to assess the JND in the perceptual domain.

On the other hand, it provides more accurate prior information for better guiding JND generation.

13 часов назад @ paperswithcode.com
/roysubhankar/ Uncertainty-guided Source-free Domain Adaptation
/roysubhankar/ Uncertainty-guided Source-free Domain Adaptation /roysubhankar/ Uncertainty-guided Source-free Domain Adaptation

Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model.

However, the absence of the source data and the domain shift makes the predictions on the target data unreliable.

We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation.

For this, we construct a probabilistic source model by incorporating priors on the network parameters inducing a distribution over the model predictions.

Unlike recent works, our probabilistic treatment is computationally lightweight, decouples source training and target adaptation, and requires no specialized source trainin…

13 часов назад @ paperswithcode.com
/jinhseo/ Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
/jinhseo/ Object Discovery via Contrastive Learning for Weakly Supervised Object Detection /jinhseo/ Object Discovery via Contrastive Learning for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations.

Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common ``argmax'' labeling method often ignores many instances of objects.

We further introduce a new contrastive loss under weak supervision where no instance-level information is available for sampling, called weakly supervised contrastive loss (WSCL).

WSCL aims to construct a credible similarity threshold for object discovery by leveraging consistent features for embedding vectors…

13 часов назад @ paperswithcode.com
/RS-CSU/ Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level context memory
/RS-CSU/ Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level context memory /RS-CSU/ Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level context memory

Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community.

Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their hierarchical representation ability.

This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery.

This module is integrated by a category attention-driven invariant domain-level context aggregation module to current pseudo invariant feature for further augmenting the pixel representatio…

15 часов назад @ paperswithcode.com
/szahedian/ An approach to generalizing some impossibility theorems in social choice
/szahedian/ An approach to generalizing some impossibility theorems in social choice /szahedian/ An approach to generalizing some impossibility theorems in social choice

As there are far fewer majority graphs or weighted majority graphs than there are preference profiles (for a bounded number of candidates and voters), computer-aided techniques such as satisfiability solving become practical for proving results about C1 and pairwise methods.

In this paper, we develop an approach to generalizing impossibility theorems proved for C1 or pairwise voting methods to impossibility theorems covering all voting methods.

We apply this approach to impossibility theorems involving "variable candidate" axioms--in particular, social choice versions of Sen's well-known $\gamma$ and $\alpha$ axioms for individual choice--which concern what happens when a candidate is added…

20 часов назад @ paperswithcode.com
/siyandong/ Visual Localization via Few-Shot Scene Region Classification
/siyandong/ Visual Localization via Few-Shot Scene Region Classification /siyandong/ Visual Localization via Few-Shot Scene Region Classification

However, such memorization requires training by amounts of posed images in each scene, which is heavy and inefficient.

On the contrary, few-shot images are usually sufficient to cover the main regions of a scene for a human operator to perform visual localization.

In this paper, we propose a scene region classification approach to achieve fast and effective scene memorization with few-shot images.

Our insight is leveraging a) pre-learned feature extractor, b) scene region classifier, and c) meta-learning strategy to accelerate training while mitigating overfitting.

The experiments validate the effectiveness of our method in the few-shot setting, and the training time is significantly reduce…

21 час назад @ paperswithcode.com
/maxlampe/ Virgo: Scalable Unsupervised Classification of Cosmological Shock Waves
/maxlampe/ Virgo: Scalable Unsupervised Classification of Cosmological Shock Waves /maxlampe/ Virgo: Scalable Unsupervised Classification of Cosmological Shock Waves

Cosmological shock waves are essential to understanding the formation of cosmological structures.

We introduce a novel pipeline, Virgo, combining physical motivation, scalability, and probabilistic robustness to tackle this unsolved unsupervised classification problem.

To this end, we employ kernel principal component analysis with low-rank matrix approximations to denoise data sets of shocked particles and create labeled subsets.

We evaluate on three state-of-the-art data sets with varying complexity and achieve good results.

The proposed pipeline runs automatically, has only a few hyperparameters, and performs well on all tested data sets.

1 день назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 1 час назад
/wuhanstudio/ Man-in-the-Middle Attack against Object Detection Systems
/wuhanstudio/ Man-in-the-Middle Attack against Object Detection Systems /wuhanstudio/ Man-in-the-Middle Attack against Object Detection Systems

Is deep learning secure for robots?

As embedded systems have access to more powerful CPUs and GPUs, deep-learning-enabled object detection systems become pervasive in robotic applications.

Our research borrows the idea of the Main-in-the-Middle attack from Cryptography to attack an object detection system.

Our experimental results prove that we can generate a strong Universal Adversarial Perturbation (UAP) within one minute and then use the perturbation to attack a detection system via the Man-in-the-Middle attack.

Our findings raise a serious concern over the applications of deep learning models in safety-critical systems such as autonomous driving.

1 день назад @ paperswithcode.com
/sachith500/ Self-Supervised Vision Transformers for Malware Detection
/sachith500/ Self-Supervised Vision Transformers for Malware Detection /sachith500/ Self-Supervised Vision Transformers for Malware Detection

Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks.

This paper presents SHERLOCK, a self-supervision based deep learning model to detect malware based on the Vision Transformer (ViT) architecture.

SHERLOCK is a novel malware detection method which learns unique features to differentiate malware from benign programs with the use of image-based binary representation.

Experimental results using 1.2 million Android applications across a hierarchy of 47 types and 696 families, shows that self-supervised learning can achieve an accuracy of 97% for the binary classification of malware which is higher than existing state-of…

1 день назад @ paperswithcode.com
/ad-freiburg/ ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool
/ad-freiburg/ ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool /ad-freiburg/ ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool

We present Elevant, a tool for the fully automatic fine-grained evaluation of a set of entity linkers on a set of benchmarks.

Elevant provides an automatic breakdown of the performance by various error categories and by entity type.

Elevant also provides a rich and compact, yet very intuitive and self-explanatory visualization of the results of a linker on a benchmark in comparison to the ground truth.

A live demo, the link to the complete code base on GitHub and a link to a demo video are provided under https://elevant.cs.uni-freiburg.de .

PDFAbstract

1 день назад @ paperswithcode.com
/PRBonn/ Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments
/PRBonn/ Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments /PRBonn/ Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

In this paper, we present a novel, accurate, and fast pole extraction approach based on geometric features that runs online and has little computational demands.

Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point clouds explicitly and enables fast pole extraction for each scan.

We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation.

We test both our geometric and learning-based pole extraction methods for localization on different datasets with different LiDAR scanners, routes, and seasonal changes.

We released our pole datasets to the public …

1 день назад @ paperswithcode.com
/radi-cho/ Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task
/radi-cho/ Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task /radi-cho/ Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task

The adoption of pre-trained language models in task-oriented dialogue systems has resulted in significant enhancements of their text generation abilities.

However, these architectures are slow to use because of the large number of trainable parameters and can sometimes fail to generate diverse responses.

To address these limitations, we propose two models with auxiliary tasks for response selection - (1) distinguishing distractors from ground truth responses and (2) distinguishing synthetic responses from ground truth labels.

They achieve state-of-the-art results on the MultiWOZ 2.1 dataset with combined scores of 107.5 and 108.3 and outperform a baseline with three times more parameters.

W…

1 день назад @ paperswithcode.com
/jerryxu0129/ HyP$^2$ Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval
/jerryxu0129/ HyP$^2$ Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval /jerryxu0129/ HyP$^2$ Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval

Image retrieval has become an increasingly appealing technique with broad multimedia application prospects, where deep hashing serves as the dominant branch towards low storage and efficient retrieval.

In this paper, we carried out in-depth investigations on metric learning in deep hashing for establishing a powerful metric space in multi-label scenarios, where the pair loss suffers high computational overhead and converge difficulty, while the proxy loss is theoretically incapable of expressing the profound label dependencies and exhibits conflicts in the constructed hypersphere space.

To address the problems, we propose a novel metric learning framework with Hybrid Proxy-Pair Loss (HyP$^2…

1 день, 11 часов назад @ paperswithcode.com
/zparquet/ Fast Learning Radiance Fields by Shooting Much Fewer Rays
/zparquet/ Fast Learning Radiance Fields by Shooting Much Fewer Rays /zparquet/ Fast Learning Radiance Fields by Shooting Much Fewer Rays

Learning radiance fields has shown remarkable results for novel view synthesis.

The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more efficient data structures.

However, these specially designed approaches do not work for most of radiance fields based methods.

To resolve this issue, we introduce a general strategy to speed up the learning procedure for almost all radiance fields based methods.

We evaluate our method with different radiance fields based methods under the widely used benchmarks.

1 день, 12 часов назад @ paperswithcode.com
/sophiaalthammer/ TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval
/sophiaalthammer/ TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval /sophiaalthammer/ TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval

Recently there is a growing interest in evaluating retrieval systems for domain-specific retrieval tasks, however these tasks often lack a reliable test collection with human-annotated relevance assessments following the Cranfield paradigm.

In the medical domain, the TripClick collection was recently proposed, which contains click log data from the Trip search engine and includes two click-based test sets.

However the clicks are biased to the retrieval model used, which remains unknown, and a previous study shows that the test sets have a low judgement coverage for the Top-10 results of lexical and neural retrieval models.

In this paper we present the novel, relevance judgement test collect…

1 день, 12 часов назад @ paperswithcode.com
/boschresearch/ Multi-Attribute Open Set Recognition
/boschresearch/ Multi-Attribute Open Set Recognition /boschresearch/ Multi-Attribute Open Set Recognition

Open Set Recognition (OSR) extends image classification to an open-world setting, by simultaneously classifying known classes and identifying unknown ones.

While conventional OSR approaches can detect Out-of-Distribution (OOD) samples, they cannot provide explanations indicating which underlying visual attribute(s) (e.g., shape, color or background) cause a specific sample to be unknown.

In this work, we introduce a novel problem setup that generalizes conventional OSR to a multi-attribute setting, where multiple visual attributes are simultaneously recognized.

Here, OOD samples can be not only identified but also categorized by their unknown attribute(s).

We propose simple extensions of co…

1 день, 12 часов назад @ paperswithcode.com
/sagizty/ Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image Classification
/sagizty/ Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image Classification /sagizty/ Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image Classification

The rapid on-site evaluation (ROSE) technique can signifi-cantly accelerate the diagnosis of pancreatic cancer by im-mediately analyzing the fast-stained cytopathological images.

Besides, the ROSE images have complicated perturbations regarding color distribution, brightness, and contrast due to different staining qualities and various acquisition device types.

To address these challenges, we proposed a shuffle instances-based Vision Transformer (SI-ViT) approach, which can reduce the perturbations and enhance the modeling among the instances.

With the regrouped bags of shuffle instances and their bag-level soft labels, the approach utilizes a regression head to make the model focus on the …

1 день, 12 часов назад @ paperswithcode.com
/ifca/ Frouros: A Python library for drift detection in Machine Learning problems
/ifca/ Frouros: A Python library for drift detection in Machine Learning problems /ifca/ Frouros: A Python library for drift detection in Machine Learning problems

Frouros is a Python library capable of detecting drift in machine learning problems.

It provides a combination of classical and more recent algorithms for drift detection: both supervised and unsupervised, as well as some capable of acting in a semi-supervised manner.

We have designed it with the objective of being easily integrated with the scikit-learn library, implementing the same application programming interface.

The library is developed following a set of best development and continuous integration practices to ensure ease of maintenance and extensibility.

The source code is available at https://github.com/IFCA/frouros.

1 день, 12 часов назад @ paperswithcode.com
/hitachinsk/ Flow-Guided Transformer for Video Inpainting
/hitachinsk/ Flow-Guided Transformer for Video Inpainting /hitachinsk/ Flow-Guided Transformer for Video Inpainting

We propose a flow-guided transformer, which innovatively leverage the motion discrepancy exposed by optical flows to instruct the attention retrieval in transformer for high fidelity video inpainting.

With the completed flows, we propagate the content across video frames, and adopt the flow-guided transformer to synthesize the rest corrupted regions.

We decouple transformers along temporal and spatial dimension, so that we can easily integrate the locally relevant completed flows to instruct spatial attention only.

Furthermore, we design a flow-reweight module to precisely control the impact of completed flows on each spatial transformer.

Especially in spatial transformer, we design a dual …

1 день, 12 часов назад @ paperswithcode.com
/visionml/ AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility
/visionml/ AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility /visionml/ AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility

One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks.

We introduce AVisT, a dedicated benchmark for visual tracking in diverse scenarios with adverse visibility.

AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios broadly grouped into five attributes with 42 object categories.

We further benchmark 17 popular and recent trackers on AVisT with detailed analysis of their tracking performance across attributes, demonstrating a big room for improvement in performance.

Our dataset along with the complete tracking performance evaluation is available at: https://github.com/visionml/pytrackingPD…

1 день, 12 часов назад @ paperswithcode.com
/chlorophyllccc/ MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy Severity Prediction
/chlorophyllccc/ MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy Severity Prediction /chlorophyllccc/ MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy Severity Prediction

Diabetic Retinopathy (DR) has become one of the leading causes of vision impairment in working-aged people and is a severe problem worldwide.

However, most of the works ignored the ordinal information of labels.

In this project, we propose a novel design MTCSNN, a Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy severity prediction task.

We perform comprehensive experiments over the RetinaMNIST, comparing MTCSNN with other models like ResNet-18, 34, 50.

Our results indicate that MTCSNN outperforms the benchmark models in terms of AUC and accuracy on the test dataset.

1 день, 12 часов назад @ paperswithcode.com
/vision4robotics/ HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking
/vision4robotics/ HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking /vision4robotics/ HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking

Low-light environments have posed a formidable challenge for robust unmanned aerial vehicle (UAV) tracking even with state-of-the-art (SOTA) trackers since the potential image features are hard to extract under adverse light conditions.

Besides, due to the low visibility, accurate online selection of the object also becomes extremely difficult for human monitors to initialize UAV tracking in ground control stations.

To solve these problems, this work proposes a novel enhancer, i.e., HighlightNet, to light up potential objects for both human operators and UAV trackers.

By employing Transformer, HighlightNet can adjust enhancement parameters according to global features and is thus adaptive f…

1 день, 12 часов назад @ paperswithcode.com
💼 University and corporation labs
DeepMind DeepMind
последний пост 6 дней, 17 часов назад
Realising scientists are the real superheroes
Realising scientists are the real superheroes Realising scientists are the real superheroes

While I loved superhero stories, I realised scientists are the real superheroes.

As part of my PhD in computer science, I created biological simulations, and ended up falling in love with biology.

From there I started working in Search at Google, where I learned to deal with massive scales of computation.

I’d been keeping an eye on AI for over a decade and I knew of DeepMind and some of their successes.

I couldn’t have optimised for a career leading to DeepMind (there wasn't even a DeepMind to optimise to!)

6 дней, 17 часов назад @ deepmind.com
AlphaFold reveals the structure of the protein universe
AlphaFold reveals the structure of the protein universe AlphaFold reveals the structure of the protein universe

It’s been one year since we released and open sourced AlphaFold and created the AlphaFold Protein Structure Database (AlphaFold DB) to freely share this scientific knowledge with the world.

Today’s update means that most pages on the main protein database UniProt will come with a predicted structure.

Determining the 3D structure of a protein used to take many months or years, it now takes seconds.

AlphaFold has already accelerated and enabled massive discoveries, including cracking the structure of the nuclear pore complex.

Meeting with researchers at the European Society of Human Genetics revealed how important AlphaFold structures are to biologists and clinicians trying to unravel the…

2 недели, 6 дней назад @ deepmind.com
The virtuous cycle of AI research
The virtuous cycle of AI research The virtuous cycle of AI research

Shortly after submitting my PhD in early 2019, I began my journey as a research scientist at DeepMind!ÂMy role…My role is a virtuous cycle of learning, researching, communicating, and advising.

At ICML…We’re giving a spotlight presentation on our paper, The CLRS algorithmic reasoning benchmark, which we hope will support and enrich efforts in the rapidly emerging area of neural algorithmic reasoning.

The future of algorithmic reasoning…The main dream of our research on algorithmic reasoning is to capture the computation of classical algorithms inside high-dimensional neural executors.

AMMI offers top-tier machine learning tuition to Africa’s brightest emerging researchers, buildin…

4 недели, 1 день назад @ deepmind.com
DeepMind’s latest research at ICML 2022
DeepMind’s latest research at ICML 2022 DeepMind’s latest research at ICML 2022

Researchers working across artificial intelligence, data science, machine vision, computational biology, speech recognition, and more are presenting and publishing their cutting-edge work in machine learning.

In addition to sponsoring the conference and supporting workshops and socials run by our long-term partners LatinX, Black in AI, Queer in AI, and Women in Machine Learning, our research teams are presenting 30 papers, including 17 external collaborations.

Here’s a brief introduction to our upcoming oral and spotlight presentations:Effective reinforcement learningMaking reinforcement learning (RL) algorithms more effective is key to building generalised AI systems.

Studying aspects of…

1 месяц назад @ deepmind.com
Working together with YouTube
Working together with YouTube Working together with YouTube

Optimising video compressionÂWith video surging during the COVID-19 pandemic, and the total amount of internet traffic expected to grow in the future, video compression is an increasingly important problem.

Working together with YouTube, we explored the potential for our AI model, MuZero, to improve the VP9 codec, a coding format that helps compress and transmit video over the internet.

Together with the YouTube team, we developed a label quality model (LQM) that helps label videos with greater precision, according to YouTube’s ad friendly guidelines.

Through improving how videos are identified and classified, we’ve enhanced trust in the platform for viewers, creators, and advertisers a…

1 месяц назад @ deepmind.com
Reflections from ethics and safety ‘on the ground’ at DeepMind
Reflections from ethics and safety ‘on the ground’ at DeepMind Reflections from ethics and safety ‘on the ground’ at DeepMind

DeepMind are actually hosting a workshop with Queer in AI on Wednesday the 6th July to discuss the relationship between queer issues and AI.

My team (the Ethics & Society team) is busy and close knit.

I lead our research collaborations which operationalise ethics and safety across our work at DeepMind.

I love learning from those around me - internally and within the wider AI ethics community.

I often have people tell me they feel intimidated by AI/ML despite having an interest in technology and ethics.

1 месяц, 2 недели назад @ deepmind.com
Leading a movement to strengthen machine learning in Africa
Leading a movement to strengthen machine learning in Africa Leading a movement to strengthen machine learning in Africa

So, a lot of my time is spent thinking about deep learning or machine learning in one way or another.

At the time, machine learning application and research wasn’t really a viable career option in South Africa.

Beyond DeepMind, I’m also a proud organiser and steering committee member of the Deep Learning Indaba, a movement to strengthen machine learning and AI in Africa.

We expected 30 or so students to get together to learn about machine learning – but to our surprise, we received over 700 applications!

In 2017, there were zero publications with an African author, based at an African institution, presented at NeurIPS, the leading machine learning conference.

1 месяц, 3 недели назад @ deepmind.com
Bridging DeepMind research with Alphabet products
Bridging DeepMind research with Alphabet products Bridging DeepMind research with Alphabet products

At DeepMind…I’m a part of the Applied team, which helps bring DeepMind technology to the outside world through Alphabet and Google products and solutions, like with WaveNet and Google Assistant, Maps, and Search.

I presented…ÂImage Recognition Using Deep Neural Networks, our recently published research on vision language models (VLMs).

I want people to leave the session…With a better understanding of what happens after the research breakthrough is announced.

And how can we use our research to create products and services that have a purpose?

The future is bright and I’m excited to discover new ways of applying our groundbreaking research to help benefit millions of people around t…

2 месяца назад @ deepmind.com
Advocating for the LGBTQ+ community in AI research
Advocating for the LGBTQ+ community in AI research Advocating for the LGBTQ+ community in AI research

Research scientist, Kevin McKee, tells how his early love of science fiction and social psychology inspired his career, and how he’s helping advance research in ‘queer fairness’, support human-AI collaboration, and study the effects of AI on the LGBTQ+ community.

In elementary school, I often tried running controlled psychology experiments for my science projects.

Recent advances in AI research show a lot of promise for supporting her and others working with queer communities.

First, a paper I worked on about ‘queer fairness’, where we advocated for more research to understand the effects of AI on LGBTQ+ communities.

Yet, most work aimed at measuring and correcting algorithmic bia…

2 месяца, 2 недели назад @ deepmind.com
Kyrgyzstan to King’s Cross: the star baker cooking up code
Kyrgyzstan to King’s Cross: the star baker cooking up code Kyrgyzstan to King’s Cross: the star baker cooking up code

Our team is working on a custom project management system for organising all of DeepMind’s research projects.

Along the way, I’ll communicate changes to our stakeholders, write docs, code and test solutions, build analytics dashboards, clean-up old code, and fix bugs.

Then our team usually has lunch together, which is really nice after working remotely for so long.

LeetCode: a platform for levelling-up coding skills.ÂCracking the Coding Interview: an interview guide for software developer roles.

Gainlo Blog: practical coding interview tips and hacks.

2 месяца, 3 недели назад @ deepmind.com
Building a culture of pioneering responsibly
Building a culture of pioneering responsibly Building a culture of pioneering responsibly

In fact, at DeepMind, we now champion a term that perfectly captures my own values and hopes for integrating technology into people’s daily lives: pioneering responsibly.ÂI believe pioneering responsibly should be a priority for anyone working in tech.

At DeepMind, everything we do stems from our company mission of solving intelligence to advance society and benefit humanity, and building a culture of pioneering responsibly is essential to making this mission a reality.ÂWhat does pioneering responsibly look like in practice?

I wanted us to focus on the operational and practical aspects of responsibility, starting with an expectation-free space in which everyone could talk candidly about w…

2 месяца, 3 недели назад @ deepmind.com
Open-sourcing MuJoCo
Open-sourcing MuJoCo Open-sourcing MuJoCo

In October 2021, we announced that we acquired the MuJoCo physics simulator, and made it freely available for everyone to support research everywhere.

We also committed to developing and maintaining MuJoCo as a free, open-source, community-driven project with best-in-class capabilities.

MuJoCo is one of the few full-featured simulators backed by an established company, which is truly open source.

As a research-driven organisation, we view MuJoCo as a platform for collaboration, where roboticists and engineers can join us to develop one of the world’s best robot simulators.

Features that make MuJoCo particularly attractive for collaboration are:Full-featured simulator that can model comple…

2 месяца, 3 недели назад @ deepmind.com
From LEGO competitions to DeepMind's robotics lab
From LEGO competitions to DeepMind's robotics lab From LEGO competitions to DeepMind's robotics lab

Today’s post is all about Akhil Raju, a software engineer on the robotics team.

That was until I turned 12 and started participating in LEGO robotics competitions.

From there I continued with robotics competitions, started university at MIT, and spent a lot of time studying computer science with a focus on robotics.

The robotics team wasn’t on my radar until my recruiter asked me, “By the way, you have bits of robotics on your resume.

Robotics – and AI in general – will be a positive force in the world, and it’s exciting to be able to help move that forward.

3 месяца назад @ deepmind.com
Tackling multiple tasks with a single visual language model
Tackling multiple tasks with a single visual language model Tackling multiple tasks with a single visual language model

But for a typical visual model to learn a new task, it must be trained on tens of thousands of examples specifically labelled for that task.

Today, in the preprint of our paper, we introduce Flamingo, a single visual language model (VLM) that sets a new state of the art in few-shot learning on a wide range of open-ended multimodal tasks.

This should allow non-expert people to quickly and easily use accurate visual language models on new tasks at hand.

Following this method, we start from Chinchilla, our recently introduced compute-optimal 70B parameter language model, to train our final Flamingo model, an 80B parameter VLM.

Flamingo’s abilities pave the way towards rich interactions with …

3 месяца, 3 недели назад @ deepmind.com
When a passion for bass and brass help build better tools
When a passion for bass and brass help build better tools When a passion for bass and brass help build better tools

At DeepMind…I build bespoke software tools for our developers.

We’re working a hybrid 3:2 model - Monday through Wednesday in the office, Thursday and Friday from anywhere.

Playing music helped tremendously when we were working remotely during the pandemic.

We’re trying to develop tools that can be used by everybody, so we have to design in a very collaborative way.

One of the things I'm advocating for is to take a step back and decide what our principles are for evolving this part of the programming language we’re working on.

3 месяца, 3 недели назад @ deepmind.com
Google
последний пост 1 день, 3 часа назад
Towards Helpful Robots: Grounding Language in Robotic Affordances
Towards Helpful Robots: Grounding Language in Robotic Affordances Towards Helpful Robots: Grounding Language in Robotic Affordances

Therefore, we asked ourselves, is there an effective way to combine advanced language models with robot learning algorithms to leverage the benefits of both?

With PaLM-SayCan, the robot acts as the language model’s “hands and eyes,” while the language model supplies high-level semantic knowledge about the task.

Our approach selects skills based on what the language model scores as useful to the high level instruction and what the affordance model scores as possible.

Our system can be seen as a dialog between the user and robot, facilitated by the language model.

This is particularly exciting because it represents the first time we can see how an improvement in language models translates to …

1 день, 3 часа назад @ ai.googleblog.com
Simplify model serving with custom prediction routines on Vertex AI
Simplify model serving with custom prediction routines on Vertex AI Simplify model serving with custom prediction routines on Vertex AI

The data received at serving time is rarely in the format your model expects. Numerical columns need to be normalized, features created, image bytes decoded, input values validated. Transforming the data can be as important as the prediction itself. That’s why we’re excited to announce custom prediction routines on Vertex AI, which simplify the process of writing pre and post processing code. With custom prediction routines, you can provide your data transformations as Python code, and behind the scenes Vertex AI SDK will build a custom container that you can test locally and deploy to the cloud. Understanding custom prediction routinesThe Vertex AI pre-built containers handle prediction re…

1 день, 3 часа назад @ cloud.google.com
Rax: Composable Learning-to-Rank Using JAX
Rax: Composable Learning-to-Rank Using JAX Rax: Composable Learning-to-Rank Using JAX

Rax provides state-of-the-art ranking losses, a number of standard ranking metrics, and a set of function transformations to enable ranking metric optimization.

A Rax ranking loss incorporates the entire list of scores to optimize the neural network, improving the overall ranking of the items.

Approximate Metric OptimizationThe quality of a ranking is commonly evaluated using ranking metrics, e.g., the normalized discounted cumulative gain (NDCG).

Using an approximation technique from Rax to transform the NDCG ranking metric into a differentiable and optimizable ranking loss ( approx_t12n and gumbel_t12n ).

Fine-tuning a T5-Base model on MS-MARCO QNA v2.1 with a ranking loss (softmax, in bl…

5 дней, 22 часа назад @ ai.googleblog.com
Google Cloud and Apollo24|7: Building Clinical Decision Support System (CDSS) together
Google Cloud and Apollo24|7: Building Clinical Decision Support System (CDSS) together Google Cloud and Apollo24|7: Building Clinical Decision Support System (CDSS) together

Along with entity extraction, the other key components of the CDSS system are capturing the temporal relationships, subjects, and certainty assessments.

We helped them to parse the discharge summaries and prescriptions to extract the medical entities.

Experimentation and choosing the right approach — Four models put to testFor entity extraction, both Google Cloud products and open-source approaches were explored.

This has to be done via AutoML Entity Extraction for Healthcare, another Google Cloud service for custom model development.

Vertex AutoML Entity Extraction for Healthcare: This is a low-code approach that’s already available on Google Cloud.

6 дней, 1 час назад @ cloud.google.com
Efficient Video-Text Learning with Iterative Co-tokenization
Efficient Video-Text Learning with Iterative Co-tokenization Efficient Video-Text Learning with Iterative Co-tokenization

Visualization of the video-text iterative co-tokenization approach.

Instead of processing the inputs directly, the video-text iterative co-tokenization model learns a reduced number of useful tokens from the fused video-language inputs.

Furthermore, iterative co-tokenization learning yields significant compute savings for video-text learning tasks.

Comparison of our iterative co-tokenization approach to previous methods such as MERLOT and VQA-T, as well as, baselines using single ResNet-3D or X3D-XL.

Despite the apparently more voluminous information to process with three streams, we obtain very efficient models due to the iterative co-tokenization approach.

1 неделя назад @ ai.googleblog.com
Introducing the Google Universal Image Embedding Challenge
Introducing the Google Universal Image Embedding Challenge Introducing the Google Universal Image Embedding Challenge

Computer vision models see daily application for a wide variety of tasks, ranging from object recognition to image-based 3D object reconstruction.

To this end, we’re excited to announce the Google Universal Image Embedding Challenge, hosted by Kaggle in collaboration with Google Research and Google Lens.

Domain Landmark Apparel Image Instance Name Empire State Building2 Cycling jerseys with Android logo3Which physical objects belong to the instance class?

Universal Image Embedding ChallengeTo help motivate the research community to address these challenges, we are hosting the Google Universal Image Embedding Challenge.

We invite researchers and machine learning enthusiasts to participate in…

1 неделя, 5 дней назад @ ai.googleblog.com
How Wayfair is reaching MLOps excellence with Vertex AI
How Wayfair is reaching MLOps excellence with Vertex AI How Wayfair is reaching MLOps excellence with Vertex AI

When Google announced its Vertex AI platform in 2021, the timing coincided perfectly with our search for a comprehensive and reliable AI Platform.

This has been a crucial part of our journey towards MLOps excellence, in which Vertex AI has proved to be of great support.

Vertex AI gives us the infrastructure to do this with tools for training, validating, and deploying ML models and pipelines.

With this in mind, we chose to focus our initial adoption of the Vertex AI platform on its Feature Store component.

This enables us to release Vertex AI Pipelines to our test and production environments, leveraging cloud-native services.

1 неделя, 6 дней назад @ cloud.google.com
ML Engineers: Partners for Scaling AI in Enterprises
ML Engineers: Partners for Scaling AI in Enterprises ML Engineers: Partners for Scaling AI in Enterprises

This involves both collaborating with data engineers (another in-demand role) and creating the infrastructure for robust data practices throughout the end-to-end ML process.

In other words, ML engineers create processes and partnerships to help with cleaning, labeling and working with large scale data from across the enterprise.

PRODUCTIONMany employers look for ML engineers who have experience with the end-to-end ML process, especially taking ML models to production.

ML engineers work with data scientists to productionize their work, building pipelines for continuous training, automated validation and version control of the model.

ML engineers deploy ML models to production either on cloud…

1 неделя, 6 дней назад @ cloud.google.com
Founders and tech leaders share their experiences in “Startup Stories” podcast
Founders and tech leaders share their experiences in “Startup Stories” podcast Founders and tech leaders share their experiences in “Startup Stories” podcast

To give startup leaders more access to these stories and insights, we’re pleased to launch our “Startup Stories” podcast, available on YouTube, Google Podcasts, and Spotify.

Each episode features an intimate, in-depth conversation with a leader of a startup using Google Cloud, with topics ranging from technical implication to brainstorming ideas over glasses of whiskey.

Current: Trevor Marshall, CTO at Current, tells us how he started his journey and how Google Cloud has supported the success of his business.

We’re thrilled to highlight the innovative work and business practices of startups who’ve chosen Google Cloud.

To learn more about how startups are using Google Cloud, please visit thi…

1 неделя, 6 дней назад @ cloud.google.com
Building Efficient Multiple Visual Domain Models with Multi-path Neural Architecture Search
Building Efficient Multiple Visual Domain Models with Multi-path Neural Architecture Search Building Efficient Multiple Visual Domain Models with Multi-path Neural Architecture Search

Deep learning models for visual tasks (e.g., image classification) are usually trained end-to-end with data from a single visual domain (e.g., natural images or computer generated images).

As such, we propose a multi-path neural architecture search (MPNAS) approach to build a unified model with heterogeneous network architecture for multiple domains.

MPNAS extends the efficient neural architecture search (NAS) approach from single path search to multi-path search by finding an optimal path for each domain jointly.

The figure below demonstrates the searched architecture of two visual domains among the ten domains of the Visual Domain Decathlon challenge.

Architecture blocks of two domains (I…

1 неделя, 6 дней назад @ ai.googleblog.com
Efficient Sequence Modeling for On-Device ML
Efficient Sequence Modeling for On-Device ML Efficient Sequence Modeling for On-Device ML

Next, we downsample the byte stream to manageable sequence length and feed it to the encoder layer.

A diagram of a generic end-to-end sequence model using byte stream input.

We chose multilingual data sources that related to the task for pre-training both BERT and byte stream models to achieve the best possible performance.

ConclusionFollowing up on our previous work with pQRNN, we evaluate byte stream models for on-device use to enable pre-training and thereby improve model performance for on-device deployment.

Thanks to Vinh Tran, Jai Gupta and Yi Tay for their help with pre-training byte stream models.

2 недели назад @ ai.googleblog.com
Running AlphaFold batch inference with Vertex AI Pipelines
Running AlphaFold batch inference with Vertex AI Pipelines Running AlphaFold batch inference with Vertex AI Pipelines

Today, to accelerate research in the bio-pharma space, from the creation of treatments for diseases to the production of new synthetic biomaterials, we are announcing a new Vertex AI solution that demonstrates how to use Vertex AI Pipelines to run DeepMind’s AlphaFold protein structure predictions at scale.

Soon after, Google Cloud released a solution that integrated AlphaFold with Vertex AI Workbench to facilitate interactive experimentation.

Between this continued growth in the AlphaFold database and the efficiency of Vertex AI, we look forward to the discoveries researchers around the world will make.

Running inference workflows at scale can be challenging—these challenges include optimi…

2 недели назад @ cloud.google.com
Access larger dataset faster and easier to accelerate your ML models training in Vertex AI
Access larger dataset faster and easier to accelerate your ML models training in Vertex AI Access larger dataset faster and easier to accelerate your ML models training in Vertex AI

Vertex AI Training delivers a serverless approach to simplify the ML model training experience for customers.

As such, training data does not persist on the compute clusters by design.

Built-in NFS support for custom training jobs provides the following benefits:Delivers an easy way to store and access large datasets for Vertex AI Training with less of the cumbersome work involving moving training data around.

Training jobs execute faster by eliminating the data download steps.

Create a Filestore instance and copy dataFirst let’s create a Filestore instance as our NFS file server.

2 недели назад @ cloud.google.com
Sharing is caring: How NVIDIA GPU sharing on GKE saves you money
Sharing is caring: How NVIDIA GPU sharing on GKE saves you money Sharing is caring: How NVIDIA GPU sharing on GKE saves you money

Time-sharing GPUs in GKEGPU time-sharing works by allocating time slices to containers sharing a physical GPU in a round-robin fashion.

For the high-end A100 GPUs, GPU sharing offered a 4.5x throughput increase, which is truly transformational.

NVIDIA multi-instance GPUs (MIG) in GKEGKE’s GPU time-sharing feature is complementary to multi-instance GPUs, which allow you to partition a single NVIDIA A100 GPU into up to seven instances, thus improving GPU utilization and reducing your costs.

You can then run multiple containers on each partition, with those containers sharing access to the resources on that partition.

And now, with GPU time-sharing, you can match your workload acceleration nee…

2 недели, 1 день назад @ cloud.google.com
Enhancing Backpropagation via Local Loss Optimization
Enhancing Backpropagation via Local Loss Optimization Enhancing Backpropagation via Local Loss Optimization

Training DNNs involves minimizing a loss function that measures the discrepancy between the ground truth labels and the model’s predictions.

The operation uses more memory for storing statistics and involves matrix inversion, thus hindering the applicability of higher-order optimizers in practice.

In “LocoProp: Enhancing BackProp via Local Loss Optimization”, we introduce a new framework for training DNN models.

Perhaps the simplest loss function one can think of for a layer is the squared loss.

After forming the objective in each layer, LocoProp updates the layer weights by repeatedly applying gradient descent steps on its objective.

2 недели, 5 дней назад @ ai.googleblog.com
OpenAI OpenAI
последний пост 1 неделя назад
New-and-Improved Content Moderation Tooling
New-and-Improved Content Moderation Tooling New-and-Improved Content Moderation Tooling

We are introducing a new-and-improved content moderation tool: The Moderation endpoint improves upon our previous content filter, and is available for free today to OpenAI API developers.

To help developers protect their applications against possible misuse, we are introducing the faster and more accurate Moderation endpoint.

When given a text input, the Moderation endpoint assesses whether the content is sexual, hateful, violent, or promotes self-harm — content prohibited by our content policy.

input text Violence Self-harm Hate Sexual Moderation endpoint Flagged FlaggedThe Moderation endpoint helps developers to benefit from our infrastructure investments.

Use of the Moderation endpoint t…

1 неделя назад @ openai.com
DALL·E Now Available in Beta
DALL·E Now Available in Beta DALL·E Now Available in Beta

Join DALL·E 2 waitlistDALL·E, the AI system that creates realistic images and art from a description in natural language, is now available in beta.

Every DALL·E user will receive 50 free credits during their first month of use and 15 free credits every subsequent month.

PricingIn this first phase of the beta, users can buy additional DALL·E credits in 115-credit increments (460 images ) for $15 on top of their free monthly credits.

Using DALL·E for commercial projectsStarting today, users get full usage rights to commercialize the images they create with DALL·E, including the right to reprint, sell, and merchandise.

We are excited to see what people create with DALL·E and look forward to us…

4 недели назад @ openai.com
Reducing Bias and Improving Safety in DALL·E 2
Reducing Bias and Improving Safety in DALL·E 2 Reducing Bias and Improving Safety in DALL·E 2

Today, we are implementing a new technique so that DALL·E generates images of people that more accurately reflect the diversity of the world’s population.

We plan to improve this technique over time as we gather more data and feedback.

We are continuing to research how AI systems, like DALL·E, might reflect biases in its training data and different ways we can address them.

These improvements have helped us gain confidence in the ability to invite more users to experience DALL·E.

Expanding access is an important part of our deploying AI systems responsibly because it allows us to learn more about real-world use and continue to iterate on our safety systems.

1 месяц назад @ openai.com
DALL·E 2: Extending Creativity
DALL·E 2: Extending Creativity DALL·E 2: Extending Creativity

As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative workflows.

“We didn't know what an osteosarcoma villain would look like so we turned to DALL·E as our creative outlet.

That's a community effort — it's come from the past few months of me talking to other DALL·E artists on Twitter / Discord / DM.

We're all figuring it out together, how to play this beautiful new instrument.”Tom AvivIsraeli chef and MasterChef winner Tom Aviv is debuting his first U.S. restaurant in Miami in a few months and has used DALL·E for menu, decor, and ambiance inspiration — and his team have also used DALL·E to in designing the…

1 месяц назад @ openai.com
DALL·E 2 Pre-Training Mitigations
DALL·E 2 Pre-Training Mitigations DALL·E 2 Pre-Training Mitigations

This post focuses on pre-training mitigations, a subset of these guardrails which directly modify the data that DALL·E 2 learns from.

This post is organized in three sections, each describing a different pre-training mitigation:In the first section, we describe how we filtered out violent and sexual images from DALL·E 2’s training dataset.

Reducing Graphic and Explicit Training DataSince training data shapes the capabilities of any learned model, data filtering is a powerful tool for limiting undesirable model capabilities.

We refer to the former model as the unfiltered model, and the latter as the filtered model.

Unfiltered Filtered Generations for the prompt “military protest” from our un…

1 месяц, 2 недели назад @ openai.com
Learning to Play Minecraft with Video PreTraining (VPT)
Learning to Play Minecraft with Video PreTraining (VPT) Learning to Play Minecraft with Video PreTraining (VPT)

We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data.

In order to utilize the wealth of unlabeled video data available on the internet, we introduce a novel, yet simple, semi-supervised imitation learning method: Video PreTraining (VPT).

Trained on 70,000 hours of IDM-labeled online video, our behavioral cloning model (the “VPT foundation model”) accomplishes tasks in Minecraft that are nearly impossible to achieve with reinforcement learning from scratch.

We then take each foundation model and fine-tune it to the house building dataset described in th…

1 месяц, 3 недели назад @ openai.com
AI-Written Critiques Help Humans Notice Flaws
AI-Written Critiques Help Humans Notice Flaws AI-Written Critiques Help Humans Notice Flaws

Major travel delays are expected late Friday and Friday night as rain turns into snow, the National Weather Service forecast said.

In counties like Sussex, Morris and Warren, expected snow accumulations range from 6 to 16 inches.

The winter storm warnings have been issued for Sussex, Warren, Morris, Hunterdon, Middlesex, Monmouth, Ocean and northwest Burlington counties.

Expect the National Weather Service’s Upton, N.Y. office, which covers northeastern N.J., to follow suit shortly.

With defenses already weakened, coastal communities could see major impacts from coastal flooding, with the worst coming Saturday morning, according to the National Weather Service.

2 месяца назад @ openai.com
Techniques for Training Large Neural Networks
Techniques for Training Large Neural Networks Techniques for Training Large Neural Networks

As cluster and model sizes have grown, machine learning practitioners have developed an increasing variety of techniques to parallelize model training over many GPUs.

Data Parallelism Pipeline Parallelism Tensor Parallelism Expert Parallelism Data Parallelism Pipeline Parallelism Tensor Parallelism Expert ParallelismAn illustration of various parallelism strategies on a three-layer model.

Forward Forward Backward Backward Update Update Idle Idle GPipe PipeDream Comparison of GPipe and PipeDream pipelining schemes, using 4 microbatches per batch.

Matrix multiplication can be thought of as dot products between pairs of rows and columns; it's possible to compute independent dot products on dif…

2 месяца, 1 неделя назад @ openai.com
Best Practices for Deploying Language Models
Best Practices for Deploying Language Models Best Practices for Deploying Language Models

Joint Recommendation for Language Model DeploymentWe’re recommending several key principles to help providers of large language models (LLMs) mitigate the risks of this technology in order to achieve its full promise to augment human capabilities.

Documentation should also include model and use-case-specific safety best practices.

Publicly disclose lessons learned regarding LLM safety and misuse in order to enable widespread adoption and help with cross-industry iteration on best practices.

Treat all labor in the language model supply chain with respect.

As LLM providers, publishing these principles represents a first step in collaboratively guiding safer large language model development an…

2 месяца, 2 недели назад @ openai.com
Powering Next Generation Applications with OpenAI Codex
Powering Next Generation Applications with OpenAI Codex Powering Next Generation Applications with OpenAI Codex

OpenAI Codex, a natural language-to-code system based on GPT-3, helps turn simple English instructions into over a dozen popular coding languages.

We’re already seeing new applications of Azure OpenAI Service across many industry verticals, from healthcare to financial services.

Applications and IndustriesSince its release via our API, we’ve been working closely with developers to build on top of Codex.

Through tight integration with Codex, GitHub Copilot can convert comments to code, autofill repetitive code, suggest tests and show alternatives.

Developers search for entire commands using natural language rather than trying to remember them or assemble them piecemeal.

2 месяца, 3 недели назад @ openai.com
DALL·E 2 Research Preview Update
DALL·E 2 Research Preview Update DALL·E 2 Research Preview Update

Last month, we started previewing DALL·E 2 to a limited number of trusted users to learn about the technology’s capabilities and limitations.

We’ve enhanced our safety system, improving the text filters and tuning the automated detection & response system for content policy violations.

Less than 0.05% of downloaded or publicly shared images were flagged as potentially violating our content policy.

We hope to increase the rate at which we onboard new users as we learn more and gain confidence in our safety system.

We’re inspired by what our users have created with DALL·E so far, and excited to see what new users will create.

3 месяца назад @ openai.com
OpenAI Leadership Team Update
OpenAI Leadership Team Update OpenAI Leadership Team Update

Brad Lightcap has been pivotal in OpenAI's growth, scaling our structure, team, and capital base through his oversight of our Finance, Legal, People, and Operations organizations.

Mira is taking on the role of Chief Technology Officer, reflecting her leadership across these critical areas within OpenAI.

He will lead the operations of OpenAI’s nonprofit parent and key strategic projects including our relationships with mission-aligned partners.

These executives are supported by world-class teams who are the lifeblood of OpenAI, constantly advancing the state of the art in artificial intelligence research and deployment.

It’s a pleasure to work alongside such incredible talent and leadership …

3 месяца, 1 неделя назад @ openai.com
Measuring Goodhart’s Law
Measuring Goodhart’s Law Measuring Goodhart’s Law

But it’s important to keep track of how well the true objective is being optimized.

We’ll focus on a setting that is particularly clean to analyze, in which we have access to the true objective.

In addition, best-of-$n$ sampling has reliable performance and is straightforward to analyze mathematically, making it well-suited to empirical studies of Goodhart’s law and related phenomena.

Together, these estimators allow us to easily analyze how the true objective varies with the amount of optimization applied to the proxy objective.

In the settings we’ve studied so far, such as summarization, we’ve typically been able to reach a KL of around 10 nats using reinforcement learning before the true…

4 месяца назад @ openai.com
DALL·E 2
DALL·E 2 DALL·E 2

DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language.

4 месяца, 1 неделя назад @ openai.com
New GPT-3 Capabilities: Edit & Insert
New GPT-3 Capabilities: Edit & Insert New GPT-3 Capabilities: Edit & Insert

def ___fib(10) def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve the def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve the runtime def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve the runtime complexity def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve the runtime complexity of the def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve the runtime complexity of the function def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) …

5 месяцев назад @ openai.com
Microsoft Microsoft
последний пост 1 неделя, 1 день назад
Microsoft is a Leader in 2022 Gartner Magic Quadrant for Cloud AI Developer Services
Microsoft is a Leader in 2022 Gartner Magic Quadrant for Cloud AI Developer Services

Gartner has recognized Microsoft as a Leader in the 2022 Gartner® Magic Quadrant™ for Cloud AI Developer Services, with Microsoft placed furthest in “Completeness of Vision”.

1 неделя, 1 день назад @ azure.microsoft.com
3 ways Azure Speech transforms game development with AI
3 ways Azure Speech transforms game development with AI

With Azure Cognitive Services for Speech, customers can build voice-enabled apps confidently and quickly with the Speech SDK. We make it easy for customers to transcribe speech to text (STT) with high accuracy, produce natural-sounding text-to-speech (TTS) voices, and translate spoken audio.

1 неделя, 1 день назад @ azure.microsoft.com
Bluware and Microsoft Azure develop OSDU-enabled interactive AI seismic interpretation solution for energy super major
Bluware and Microsoft Azure develop OSDU-enabled interactive AI seismic interpretation solution for energy super major

Bluware, which develops cloud-native solutions to help oil and gas operators to increase exploration and production workflow productivity through deep learning by enabling geoscientists to deliver faster and smarter decisions about the subsurface and today announced its collaboration with Microsoft for its next-generation automated interpretation solution, InteractivAI™, which is built on the Azure implementation of the OSDU™ Data Platform.

1 неделя, 2 дня назад @ azure.microsoft.com
How data and AI will transform contact centres for financial services
How data and AI will transform contact centres for financial services How data and AI will transform contact centres for financial services

Print a copy of How data and AI will transform contact centres for financial servicesShare How data and AI will transform contact centres for financial services on EmailShare How data and AI will transform contact centres for financial services on FacebookShare How data and AI will transform contact centres for financial services on LinkedInShare How data and AI will transform contact centres for financial services on TwitterToggle share menu for: How data and AI will transform contact centres for financial servicesContact centres for financial institutions have traditionally been a core touch point for customers to access various types of immediate support – from queries to complaints to f…

3 недели, 2 дня назад @ cloudblogs.microsoft.com
AI-equipped drones study dolphins on the edge of extinction
AI-equipped drones study dolphins on the edge of extinction

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3 недели, 6 дней назад @ news.microsoft.com
DeepSpeed Compression: A composable library for extreme compression and zero-cost quantization
DeepSpeed Compression: A composable library for extreme compression and zero-cost quantization DeepSpeed Compression: A composable library for extreme compression and zero-cost quantization

However, no systematic study on best practices for extreme compression exists, such as using aggressive quantization methods and layer reduction.

DeepSpeed Compression overcomes these challenges by offering novel state-of-the-art compression techniques, such as XTC for 32x smaller model size and ZeroQuant for 5000x lower compression cost reduction.

map Layers Table 1: Compression techniques supported in DeepSpeed Compression composer.

After the DNN model has been compressed, DeepSpeed Compression replaces the compressed layers with highly optimized kernels in the DeepSpeed Inference engine to maximize hardware efficiency.

DeepSpeed Compression release planDeepSpeed Compression is still at i…

4 недели назад @ microsoft.com
Confidential Containers: Verifiably secure computation in the cloud
Confidential Containers: Verifiably secure computation in the cloud Confidential Containers: Verifiably secure computation in the cloud

At Microsoft Build 2022, the company announced serverless confidential containers with lift-and-shift support, the next step in the evolution of confidential computing.

Confidential Containers enables users to take existing container workloads, and with a small amount of configuration, use them in a confidential environment.

Users of Confidential Containers create a policy defining precisely what can run in the confidential container environment and how.

Secure multiparty computationsAnother benefit of Confidential Containers is they enable secure multiparty computations.

Confidential Containers is currently available for limited preview and will be available for public preview later this y…

1 месяц назад @ microsoft.com
Online math tutoring service uses AI to help boost students’ skills and confidence
Online math tutoring service uses AI to help boost students’ skills and confidence

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1 месяц назад @ blogs.microsoft.com
AI4Science to empower the fifth paradigm of scientific discovery
AI4Science to empower the fifth paradigm of scientific discovery AI4Science to empower the fifth paradigm of scientific discovery

Could this capability represent the dawn of a new paradigm of scientific discovery?

BOOK The Fourth Paradigm Data-Intensive Scientific DiscoveryJim Gray, a Turing Award winner, and former Microsoft Technical Fellow, characterised the historical evolution of scientific discovery through four paradigms.

Machine learning forms an increasingly important component of the fourth paradigm, allowing the modelling and analysis of large volumes of experimental scientific data.

This ‘fifth paradigm’ of scientific discovery represents one of the most exciting frontiers for machine learning as well as for the natural sciences.

AI4Science is an effort deeply rooted in Microsoft’s mission, applying the fu…

1 месяц, 1 неделя назад @ microsoft.com
AI-Mimi is building inclusive TV experiences for Deaf and Hard of Hearing user in Japan
AI-Mimi is building inclusive TV experiences for Deaf and Hard of Hearing user in Japan

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1 месяц, 1 неделя назад @ blogs.microsoft.com
Choose the right size for your workload with NVads A10 v5 virtual machines, now generally available
Choose the right size for your workload with NVads A10 v5 virtual machines, now generally available

Introducing the general availability of NVads A10 v5 GPU accelerated virtual machines, now available in US South Central, US West2, US West3, Europe West, and Europe North regions. Azure is the first public cloud to offer GPU Partitioning (GPU-P) on NVIDIA GPUs.

1 месяц, 1 неделя назад @ azure.microsoft.com
Introducing the Microsoft Climate Research Initiative
Introducing the Microsoft Climate Research Initiative Introducing the Microsoft Climate Research Initiative

As we continue to explore the role of technology to advance the art of the possible, we are launching the Microsoft Climate Research Initiative (MCRI).

For the kickoff of this initiative, we are focusing on three critical areas in climate research where computational advances can drive key scientific transformations: Overcoming constraints to decarbonization, reducing uncertainties in carbon accounting, and assessing climate risks in more detail.

All results of this initiative are expected to be made public and freely available to spark even broader research and progress on these important climate issues.

“As researchers, we’re excited to work together on projects specifically selected for …

1 месяц, 2 недели назад @ microsoft.com
GODEL: Combining goal-oriented dialog with real-world conversations
GODEL: Combining goal-oriented dialog with real-world conversations GODEL: Combining goal-oriented dialog with real-world conversations

The test set for these experiments combines a variety of dialog genres, including task-oriented dialog, conversational question-answering, and grounded chit-chat.

GODEL available as open sourceTo advance research, we believe it is crucial to make code and models publicly available, and we have released GODEL as fully open source.

We have made three versions of GODEL available: base, large, and extra-large.

We hope GODEL helps numerous academic research teams advance the field of conversational AI with innovative dialog models while eliminating the need for significant GPU resources.

We plan to continuously improve GODEL and make more models available to the research community.

1 месяц, 3 недели назад @ microsoft.com
Azure Orbital Ground Station as Service extends life and reduces costs for satellite operators
Azure Orbital Ground Station as Service extends life and reduces costs for satellite operators

How can Microsoft empower satellite operators to focus on their mission and enable them to continue the operation of their satellites, without making capital investments in their ground infrastructure? To answer that question, Microsoft worked alongside the National Oceanic and Atmospheric Administration (NOAA), and our partner Xplore, to demonstrate how the commercial cloud can provide satellite mission management for NOAA’s legacy polar satellites (NOAA-18)—extending the mission life of these satellites while reducing the cost of operation through Azure Orbital Ground Station as-a-Service (GSaaS).

1 месяц, 3 недели назад @ azure.microsoft.com
Microsoft’s framework for building AI systems responsibly
Microsoft’s framework for building AI systems responsibly

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1 месяц, 3 недели назад @ blogs.microsoft.com
MIT AI MIT AI
последний пост 6 дней, 23 часа назад
New programmable materials can sense their own movements
New programmable materials can sense their own movements New programmable materials can sense their own movements

MIT researchers have developed a method for 3D printing materials with tunable mechanical properties, that sense how they are moving and interacting with the environment.

The method opens opportunities for embedding sensors within architected materials, a class of materials whose mechanical properties are programmed through form and composition.

This technique could someday be used to create flexible soft robots with embedded sensors that enable the robots to understand their posture and movements.

While architected materials can exhibit unique properties, integrating sensors within them is challenging given the materials’ often sparse, complex shapes.

This new method provides accurate prop…

6 дней, 23 часа назад @ news.mit.edu
3 Questions: Amar Gupta on an integrated approach to enhanced health-care delivery
3 Questions: Amar Gupta on an integrated approach to enhanced health-care delivery 3 Questions: Amar Gupta on an integrated approach to enhanced health-care delivery

The Federation of State Medical Boards then gave us names and addresses of the state medical boards in the U.S., and some abroad.

The Federation of State Medical Boards recently developed a new technology to address this problem.

These database sites recommended that we have to go to the sites of the three state medical boards, and it actually took us there.

When we got to the state medical boards, all the information has been redacted.

About 15 years ago, I had coined the term “three-pronged approach” to describe my vision of evolving health care.

1 неделя назад @ news.mit.edu
Caspar Hare, Georgia Perakis named associate deans of Social and Ethical Responsibilities of Computing
Caspar Hare, Georgia Perakis named associate deans of Social and Ethical Responsibilities of Computing Caspar Hare, Georgia Perakis named associate deans of Social and Ethical Responsibilities of Computing

Caspar Hare and Georgia Perakis have been appointed the new associate deans of the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative in the MIT Stephen A. Schwarzman College of Computing.

“I look forward to working with Caspar and Georgia on continuing to develop and advance SERC and its reach across MIT.

Perakis will also assume an associate dean role at MIT Sloan in recognition of her leadership.

Hare and Perakis succeed David Kaiser, the Germeshausen Professor of the History of Science and professor of physics, and Julie Shah, the H.N.

In February 2021, they launched the MIT Case Studies in Social and Ethical Responsibilities of Computing for undergradua…

1 неделя назад @ news.mit.edu
Leveraging computational tools to enhance product design
Leveraging computational tools to enhance product design Leveraging computational tools to enhance product design

Now, Saadi is working to improve the product design process by evaluating computational design tools, exploring new applications, and developing education curricula.

These tools, also known as generative design tools, are commonly used in automotive, aerospace, and architectural industries.

After, they feed the constraints into a generative design tool, which generates a product design accordingly.

Before this project, Saadi had little experience with computational tools in the product design process.

Reforming design curriculum to be more inclusiveSaadi is also working to improve the product design process through curriculum development.

1 неделя назад @ news.mit.edu
Solving a longstanding conundrum in heat transfer
Solving a longstanding conundrum in heat transfer Solving a longstanding conundrum in heat transfer

“Boiling is a very effective heat transfer mechanism; it’s the way to remove large amounts of heat from the surface, which is why it is used in many high-power density applications,” Bucci says.

But what if so many bubbles form and coalesce that they form a band of vapor that prevents further heat transfer?

Such a problem is a known entity and is labeled the boiling crisis.

The boiling crisis remains a challenge because while models abound, the measurement of related phenomena to prove or disprove these models has been difficult.

His interest in heat transfer mechanisms took root during his doctoral studies, a research subject he pursued in Paris at the French Alternative Energies and Atomi…

1 неделя, 1 день назад @ news.mit.edu
New algorithm aces university math course questions
New algorithm aces university math course questions New algorithm aces university math course questions

The best models have only been able to answer elementary or high school-level math questions, and they don’t always find the correct solutions.

The model also automatically explains solutions and rapidly generates new problems in university math subjects.

When the researchers showed these machine-generated questions to university students, the students were unable to tell whether the questions were generated by an algorithm or a human.

Adding contextTurning math questions into programming tasks is not always simple, Drori says.

The researchers gave students 10 questions from each undergraduate math course in a random order; five were created by humans and five were machine-generated.

2 недели назад @ news.mit.edu
Why it’s a problem that pulse oximeters don’t work as well on patients of color
Why it’s a problem that pulse oximeters don’t work as well on patients of color Why it’s a problem that pulse oximeters don’t work as well on patients of color

Pulse oximetry is a noninvasive test that measures the oxygen saturation level in a patient’s blood, and it has become an important tool for monitoring many patients, including those with Covid-19.

It is well known that non-white intensive care unit (ICU) patients receive less-accurate readings of their oxygen levels using pulse oximeters — the common devices clamped on patients’ fingers.

Now, a paper co-authored by MIT scientists reveals that inaccurate pulse oximeter readings can lead to critically ill patients of color receiving less supplemental oxygen during ICU stays.

The findings showed that inaccurate readings of Asian, Black, and Hispanic patients resulted in them receiving less su…

2 недели назад @ news.mit.edu
Using artificial intelligence to control digital manufacturing
Using artificial intelligence to control digital manufacturing Using artificial intelligence to control digital manufacturing

It could also help technicians make adjustments to the printing process on-the-fly if material or environmental conditions change unexpectedly.

Picking parametersDetermining the ideal parameters of a digital manufacturing process can be one of the most expensive parts of the process because so much trial-and-error is required.

But training a neural network-based controller to understand this manufacturing process is data-intensive, and would require making millions of prints.

In practice, conditions typically change due to slight variations or noise in the printing process.

So if you had a manufacturing process out in the field and you wanted to change the material, you wouldn’t have to rev…

2 недели, 1 день назад @ news.mit.edu
New hardware offers faster computation for artificial intelligence, with much less energy
New hardware offers faster computation for artificial intelligence, with much less energy New hardware offers faster computation for artificial intelligence, with much less energy

A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors.

In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

Accelerating deep learningAnalog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons.

To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte.

2 недели, 5 дней назад @ news.mit.edu
Explained: How to tell if artificial intelligence is working the way we want it to
Explained: How to tell if artificial intelligence is working the way we want it to Explained: How to tell if artificial intelligence is working the way we want it to

The most popular types of local explanation methods fall into three broad categories.

Perhaps her credit score or income, both features used in the model’s prediction, need to be higher for her to be approved.

How are explanation methods used?

For one, Ghassemi’s recent research has shown that explanation methods can perpetuate biases and lead to worse outcomes for people from disadvantaged groups.

Another pitfall of explanation methods is that it is often impossible to tell if the explanation method is correct in the first place.

3 недели, 5 дней назад @ news.mit.edu
A technique to improve both fairness and accuracy in artificial intelligence
A technique to improve both fairness and accuracy in artificial intelligence A technique to improve both fairness and accuracy in artificial intelligence

Users sometimes employ a technique, known as selective regression, in which the model estimates its confidence level for each prediction and will reject predictions when its confidence is too low.

As the model’s confidence increases with selective regression, its chance of making the right prediction also increases, but this does not always happen for all subgroups.

One reason this can occur is due to the fact that the model’s confidence measure is trained using overrepresented groups and may not be accurate for these underrepresented groups.

The MIT researchers aimed to ensure that, as the overall error rate for the model improves with selective regression, the performance for every subgro…

4 недели назад @ news.mit.edu
Teaching AI to ask clinical questions
Teaching AI to ask clinical questions Teaching AI to ask clinical questions

However, training effective models requires huge datasets of relevant medical questions, which are often hard to come by due to privacy restrictions.

To overcome this data shortage, researchers at MIT partnered with medical experts to study the questions physicians ask when reviewing EHRs.

When they used their dataset to train a machine-learning model to generate clinical questions, they found that the model asked high-quality and authentic questions, as compared to real questions from medical experts, more than 60 percent of the time.

Some were composed of medical questions asked by patients on web forums, which are a far cry from physician questions.

Once they had compiled their dataset o…

1 месяц назад @ news.mit.edu
Artificial intelligence model finds potential drug molecules a thousand times faster
Artificial intelligence model finds potential drug molecules a thousand times faster Artificial intelligence model finds potential drug molecules a thousand times faster

But what fraction of these molecules have potential drug-like traits that can be used to develop life-saving drug treatments?

This gargantuan number prolongs the drug development process for fast-spreading diseases like Covid-19 because it is far beyond what existing drug design models can compute.

Before drug development can even take place, drug researchers must find promising drug-like molecules that can bind or “dock” properly onto certain protein targets in a process known as drug discovery.

After successfully docking to the protein, the binding drug, also known as the ligand, can stop a protein from functioning.

Walters suggested that the team try their model on an already existing dr…

1 месяц назад @ news.mit.edu
Smart textiles sense how their users are moving
Smart textiles sense how their users are moving Smart textiles sense how their users are moving

The machine-learning system predicted motions and yoga poses performed by an individual standing on the smart textile mat with about 99 percent accuracy.

Using a novel fabrication process, MIT researchers have produced smart textiles that snugly conform to the body so they can sense the wearer’s posture and motions.

Knitting know-howTo produce a smart textile, the researchers use a digital knitting machine that weaves together layers of fabric with rows of standard and functional yarn.

He decided to try incorporating melting fibers and thermoforming into the smart textile fabrication process.

Once he perfected the fabrication process, Wicaksono needed a system to accurately process pressure…

1 месяц, 1 неделя назад @ news.mit.edu
Startup lets doctors classify skin conditions with the snap of a picture
Startup lets doctors classify skin conditions with the snap of a picture Startup lets doctors classify skin conditions with the snap of a picture

Over time, however, Conover realized that other skin conditions make up the vast majority of cases physicians and dermatologists see.

“We realized we needed to pivot away from skin cancer in order to help skin cancer patients see the dermatologist faster.”After primary care physicians take a photo of a patient’s skin condition, Piction’s app shows images of similar skin presentations.

Piction also helps physicians differentiate between the conditions they most suspect to make better care decisions for the patient.

Through those conversations, she learned that skin rashes like psoriasis, eczema, and rosacea account for the vast majority of skin problems seen by primary care physicians.

“This…

1 месяц, 1 неделя назад @ news.mit.edu
Berkeley AI
последний пост 1 месяц, 1 неделя назад
Why do Policy Gradient Methods work so well in Cooperative MARL? Evidence from Policy Representation
Why do Policy Gradient Methods work so well in Cooperative MARL? Evidence from Policy Representation Why do Policy Gradient Methods work so well in Cooperative MARL? Evidence from Policy Representation

Evidence from Policy RepresentationIn cooperative multi-agent reinforcement learning (MARL), due to its on-policy nature, policy gradient (PG) methods are typically believed to be less sample efficient than value decomposition (VD) methods, which are off-policy.

CTDE in Cooperative MARL: VD and PG methodsCentralized training and decentralized execution (CTDE) is a popular framework in cooperative MARL.

VD methods learn local Q networks and a mixing function that mixes the local Q networks to a global Q function.

By contrast, PG methods directly apply policy gradient to learn an individual policy and a centralized value function for each agent.

The permutation game: a simple counterexample w…

1 месяц, 1 неделя назад @ bair.berkeley.edu
FIGS: Attaining XGBoost-level performance with the interpretability and speed of CART
FIGS: Attaining XGBoost-level performance with the interpretability and speed of CART FIGS: Attaining XGBoost-level performance with the interpretability and speed of CART

FIGS: Attaining XGBoost-level performance with the interpretability and speed of CARTFIGS (Fast Interpretable Greedy-tree Sums): A method for building interpretable models by simultaneously growing an ensemble of decision trees in competition with one another.

In this blog post we’ll cover FIGS, a new method for fitting an interpretable model that takes the form of a sum of trees.

Real-world experiments and theoretical results show that FIGS can effectively adapt to a wide range of structure in data, achieving state-of-the-art performance in several settings, all without sacrificing interpretability.

from imodels import FIGSClassifier , get_clean_dataset from sklearn.model_selection impor…

1 месяц, 2 недели назад @ bair.berkeley.edu
The Berkeley Crossword Solver
The Berkeley Crossword Solver The Berkeley Crossword Solver

The Berkeley Crossword SolverWe recently built the Berkeley Crossword Solver (BCS), the first computer program to beat every human competitor in the world’s top crossword tournament.

in Berkeley (3)Domain ender that UC Berkeley was one of the first schools to adopt (3)Angeleno at Berkeley, say (8)Our ApproachThe BCS uses a two-step process to solve crossword puzzles.

Compared to the previous state-of-the-art method for answering crossword clues, this approach obtained a 13.4% absolute improvement in top-1000 QA accuracy.

FillWinning The American Crossword Puzzle TournamentThe American Crossword Puzzle Tournament (ACPT) is the largest and longest-running crossword tournament and is organiz…

2 месяца, 4 недели назад @ bair.berkeley.edu
Rethinking Human-in-the-Loop for Artificial Augmented Intelligence
Rethinking Human-in-the-Loop for Artificial Augmented Intelligence Rethinking Human-in-the-Loop for Artificial Augmented Intelligence

Rethinking Human-in-the-Loop for Artificial Augmented IntelligenceFigure 1: In real-world applications, we think there exist a human-machine loop where humans and machines are mutually augmenting each other.

For demonstration, we designed a recognition framework that was a combination of active learning, semi-supervised learning, and human-in-the-loop (Figure 3).

Low-confidence predictions are sent for human annotation, and high-confidence predictions are trusted for downstream tasks or pseudo-labels for model updates.

Thus, the goal of AI development changes from replacing human intelligence to mutually augmenting both human and machine intelligence.

However, this goal of replacing human e…

3 месяца, 2 недели назад @ bair.berkeley.edu
Designing Societally Beneficial Reinforcement Learning Systems
Designing Societally Beneficial Reinforcement Learning Systems Designing Societally Beneficial Reinforcement Learning Systems

Designing Societally Beneficial Reinforcement Learning SystemsDeep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications.

At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems.

A Taxonomy of FeedbackReinforcement learning systems are often spotlighted for their ability to act in an environment, rather than passively make predictions.

Other supervised machine learning systems, such as computer vision, consume data and return a prediction that can be used by some decision ma…

3 месяца, 2 недели назад @ bair.berkeley.edu
Should I Use Offline RL or Imitation Learning?
Should I Use Offline RL or Imitation Learning? Should I Use Offline RL or Imitation Learning?

Should I Use Offline RL or Imitation Learning?

Are there fundamental limitations to methods that rely on some form of imitation (BC, conditional BC, filtered BC) that offline RL addresses?

While it might be clear that offline RL should enjoy a large advantage over imitation learning when learning from diverse datasets that contain a lot of suboptimal behavior, we will also discuss how even cases that might seem BC-friendly can still allow offline RL to attain significantly better results.

Empirical Results Comparing Offline RL and BCIn our discussion so far, we have already studied settings such as the antmazes, where offline RL methods can significantly outperform imitation-style methods d…

3 месяца, 3 недели назад @ bair.berkeley.edu
Offline RL Made Easier: No TD Learning, Advantage Reweighting, or Transformers
Offline RL Made Easier: No TD Learning, Advantage Reweighting, or Transformers Offline RL Made Easier: No TD Learning, Advantage Reweighting, or Transformers

Offline RL Made Easier: No TD Learning, Advantage Reweighting, or TransformersA demonstration of the RvS policy we learn with just supervised learning and a depth-two MLP.

Offline reinforcement learning (RL) is conventionally approached using value-based methods based on temporal difference (TD) learning.

These algorithms learn conditional policies by conditioning on goal states (Lynch et al., 2019; Ghosh et al., 2021), reward-to-go (Kumar et al., 2019; Chen et al., 2021), or language descriptions of the task (Lynch and Sermanet, 2021).

The video above shows the complex behavior we learn using just supervised learning with a depth-two MLP – no TD learning, data reweighting, or Transformer…

3 месяца, 4 недели назад @ bair.berkeley.edu
Accelerating Ukraine Intelligence Analysis with Computer Vision on Synthetic Aperture Radar Imagery
Accelerating Ukraine Intelligence Analysis with Computer Vision on Synthetic Aperture Radar Imagery Accelerating Ukraine Intelligence Analysis with Computer Vision on Synthetic Aperture Radar Imagery

EO imagery is commonplace—anyone who has used Google Maps or similar mapping software has interacted with EO satellite imagery.

In general, existing computer vision methods on other, non-aerial RGB imagery transfer very well to satellite imagery.

Synthetic Aperture Radar ImagerySynthetic aperture radar (SAR) imagery is an active form of remote sensing in which a satellite transmits pulses of microwave radar waves down to the surface of the Earth.

Computer Vision on SAR Imagery for UkraineImagery analysts are currently relying on both EO and SAR imagery where available over Ukraine.

Our top performing method, MAERS, for representation learning on RGB, SAR, and co-registered RGB + SAR build…

4 месяца, 4 недели назад @ bair.berkeley.edu
Unsupervised Skill Discovery with Contrastive Intrinsic Control
Unsupervised Skill Discovery with Contrastive Intrinsic Control Unsupervised Skill Discovery with Contrastive Intrinsic Control

Unsupervised Skill Discovery with Contrastive Intrinsic ControlUnsupervised Reinforcement Learning (RL), where RL agents pre-train with self-supervised rewards, is an emerging paradigm for developing RL agents that are capable of generalization.

This tension between the need to support large skill spaces and the limitation of current discriminators leads us to propose Contrastive Intrinsic Control (CIC).

Contrastive Intrinsic Control (CIC) introduces a new contrastive density estimator to approximate the conditional entropy (the discriminator).

For a practical algorithm, we use the CIC contrastive skill learning as an auxiliary loss during pre-training.

Our hope is that our approach encoura…

5 месяцев, 3 недели назад @ bair.berkeley.edu
imodels: leveraging the unreasonable effectiveness of rules
imodels: leveraging the unreasonable effectiveness of rules imodels: leveraging the unreasonable effectiveness of rules

imodels: leveraging the unreasonable effectiveness of rulesimodels: A python package with cutting-edge techniques for concise, transparent, and accurate predictive modeling.

Moreover, interpretable models help with all kinds of things, such as identifying errors, leveraging domain knowledge, and speeding up inference.

Fig 1 shows four possible forms an interpretable model in the imodels package could take.

model = BoostedRulesClassifier () # initialize a model model .

This post is based on the imodels package (github, paper), published in the Journal of Open Source Software, 2021.

6 месяцев, 2 недели назад @ bair.berkeley.edu
The Unsupervised Reinforcement Learning Benchmark
The Unsupervised Reinforcement Learning Benchmark The Unsupervised Reinforcement Learning Benchmark

While large-scale RL agents can achieve stunning results, even the best RL agents today are narrow.

Unsupervised RL as a path forwardTo date, the most promising path toward generalist AI systems in language and vision has been through unsupervised pre-training.

The unsupervised RL frameworkUnsupervised RL is very similar to supervised RL.

The Unsupervised Reinforcement Learning Benchmark (URLB)While a variety of unsupervised RL algorithms have been proposed over the last few years, it has been impossible to compare them fairly due to differences in evaluation, environments, and optimization.

Paper: URLB: Unsupervised Reinforcement Learning Benchmark Michael Laskin*, Denis Yarats*, Hao Liu, …

8 месяцев назад @ bair.berkeley.edu
Which Mutual Information Representation Learning Objectives are Sufficient for Control?
Which Mutual Information Representation Learning Objectives are Sufficient for Control? Which Mutual Information Representation Learning Objectives are Sufficient for Control?

Which Mutual Information Representation Learning Objectives are Sufficient for Control?

To simplify the analysis, we analyze representation learning in isolation from the other aspects of RL by assuming the existence of an offline dataset on which to perform representation learning.

An objective may have more than one maximizing representation, so we call a representation learning objective sufficient if all the representations that maximize that objective are sufficient.

To separate representation learning from RL, we first optimize each representation learning objective on a dataset of offline data, (similar to the protocol in Stooke et al.

This post is based on the paper Which Mutual Inf…

9 месяцев назад @ bair.berkeley.edu
Sequence Modeling Solutions for Reinforcement Learning Problems
Sequence Modeling Solutions for Reinforcement Learning Problems Sequence Modeling Solutions for Reinforcement Learning Problems

Sequence Modeling Solutionsfor Reinforcement Learning ProblemsSequence Modeling Solutions for Reinforcement Learning ProblemsLong-horizon predictions of (top) the Trajectory Transformer compared to those of (bottom) a single-step dynamics model.

While it has been possible to apply reinforcement learning algorithms to large-scale problems, generally there has been much more friction in doing so.

In this post, we explore whether we can alleviate these difficulties by tackling the reinforcement learning problem with the toolbox of sequence modeling.

Though there is value in studying the most streamlined approaches that can tackle reinforcement learning problems, it is possible that the most ef…

9 месяцев назад @ bair.berkeley.edu
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
Bridge  Data:  Boosting  Generalization  of  Robotic  Skills  with Cross-Domain  Datasets Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets

Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain DatasetsFig.

For comparison, we include the performance of the policy when trained only on the target domain data, without bridge data (Target Domain Only), a baseline that uses only the bridge data without any target domain data (Direct Transfer), as well as a baseline that trains a single-task policy on data in the target domain only (Single Task).

However, we do include the “direct transfer” baseline, which utilizes a policy trained only on the bridge data.

Figure 10: Example rollouts of policies jointly trained on target domain data and bridge data in each of the three transfer scenarios.

This means that bridge…

9 месяцев назад @ bair.berkeley.edu
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability

Epistemic POMDPs and Implicit Partial ObservabilityActively steering towards regions of low uncertainty or taking information-gathering actions are two of a multitude of avenues an RL agent has to handle its epistemic uncertainty.

What makes the epistemic POMDP particularly exciting is the following equivalence:An RL agent is Bayes-optimal for generalization if and only if it maximizes expected return in the corresponding epistemic POMDP.

LEEP, an algorithm based on the epistemic POMDP objective, generalizes better than PPO in four Procgen tasks.

This is surprisingly not true; limited training data in RL introduces implicit partial observability into an otherwise fully-observable problem.

T…

9 месяцев, 2 недели назад @ bair.berkeley.edu
AWS Machine Learning AWS Machine Learning
последний пост 18 часов назад
Announcing the launch of the model copy feature for Amazon Rekognition Custom Labels
Announcing the launch of the model copy feature for Amazon Rekognition Custom Labels Announcing the launch of the model copy feature for Amazon Rekognition Custom Labels

Rekognition Custom Labels builds off of the existing capabilities of Amazon Rekognition, which are already trained on tens of millions of images across many categories.

If not, you can label them directly on the Rekognition Custom Labels console, or use Amazon SageMaker Ground Truth to label them.

Today we’re happy to announce the launch of the Rekognition Custom Labels model copy feature.

For instructions, refer to Training a custom single class object detection model with Amazon Rekognition Custom Labels.

Define a resource-based policy to allow cross-account access to copy the Rekognition Custom Labels model.

18 часов назад @ aws.amazon.com
Cloud-based medical imaging reconstruction using deep neural networks
Cloud-based medical imaging reconstruction using deep neural networks Cloud-based medical imaging reconstruction using deep neural networks

In this post, we discuss a use case of MRI reconstruction, but the architectural concepts can be applied to other types of image reconstruction.

Advances in the field of image reconstruction have led to the successful application of AI-based techniques within magnetic resonance (MR) imaging.

An AWS Lambda function retrieves the raw MRI Amazon SQS queue depth, which represents the amount of raw MRI acquisitions uploaded to the AWS Cloud.

MRI reconstruction layerThe MRI reconstruction based on the RAKI neural network is handled by the architecture shown in the following diagram.

In this post, we explored a possible solution for image reconstruction from raw modality data using a computational…

19 часов назад @ aws.amazon.com
Customize your recommendations by promoting specific items using business rules with Amazon Personalize
Customize your recommendations by promoting specific items using business rules with Amazon Personalize Customize your recommendations by promoting specific items using business rules with Amazon Personalize

Amazon Personalize automatically finds the relevant items within the set of promotional items that meet your business rule and distributes them within each user’s recommendations.

You can use the Amazon Personalize console or API to create a filter with your logic using the Amazon Personalize DSL (domain-specific language).

You define the items to promote in the catalog system, load them to the Amazon Personalize items dataset, and then get recommendations.

PrerequisitesTo use promotions, you first set up some Amazon Personalize resources on the Amazon Personalize console.

For more information about Amazon Personalize, see What Is Amazon Personalize?

1 день, 19 часов назад @ aws.amazon.com
Amazon SageMaker JumpStart solutions now support custom IAM role settings
Amazon SageMaker JumpStart solutions now support custom IAM role settings Amazon SageMaker JumpStart solutions now support custom IAM role settings

Amazon SageMaker JumpStart solutions are a feature within Amazon SageMaker Studio that allow a simple-click experience to set up your own machine learning (ML) workflows.

Starting today, we’re excited to announce that JumpStart solutions now supports custom AWS Identity and Access Management (IAM) roles be passed into services.

This means each service uses their own IAM role with dedicated IAM policy attached, and is fully customizable.

In the SageMaker Projects and JumpStart section, select Enable Amazon SageMaker project templates and Amazon SageMaker JumpStart for this account and Enable Amazon SageMaker project templates and Amazon SageMaker JumpStart for Studio users.

About the authors…

1 день, 22 часа назад @ aws.amazon.com
Intelligent document processing with AWS AI services: Part 2
Intelligent document processing with AWS AI services: Part 2 Intelligent document processing with AWS AI services: Part 2

Solution overviewThe following reference architecture shows how you can use AWS AI services like Amazon Textract and Amazon Comprehend, along with other AWS services to implement the IDP workflow.

To extend this phase, we use Amazon Comprehend pre-trained entities and an Amazon Comprehend custom entity recognizer for further document extraction.

We discussed the various stages of the pipeline and a hands-on solution with AWS AI services such as Amazon Textract, Amazon Comprehend, Amazon Comprehend Medical, and Amazon A2I for designing and building industry-specific use cases.

We recommend you review the security sections of the Amazon Textract, Amazon Comprehend, and Amazon A2I documentatio…

1 день, 23 часа назад @ aws.amazon.com
Intelligent document processing with AWS AI services: Part 1
Intelligent document processing with AWS AI services: Part 1 Intelligent document processing with AWS AI services: Part 1

Intelligent document processing (IDP) with AWS artificial intelligence (AI) services helps automate information extraction from documents of different types and formats, quickly and with high accuracy, without the need for machine learning (ML) skills.

We demonstrate how you can utilize ML capabilities with Amazon Textract, Amazon Comprehend, and Amazon Augmented AI (Amazon A2I) to process documents and validate the data extracted from them.

Amazon A2I integrates both with Amazon Textract and Amazon Comprehend to provide you the ability to introduce human review steps within your intelligent document processing workflow.

We recommend reviewing the security sections of the Amazon Textract, A…

1 день, 23 часа назад @ aws.amazon.com
Build an air quality anomaly detector using Amazon Lookout for Metrics
Build an air quality anomaly detector using Amazon Lookout for Metrics Build an air quality anomaly detector using Amazon Lookout for Metrics

Create an Amazon Simple Storage Service (Amazon S3) bucket and a folder named air-quality .

On the AWS IoT Core console, create an AWS IoT policy called admin.

To create the detector, navigate to the Lookout for Metrics console and choose Create detector.

To create a dataset, complete the following steps:On the Amazon Lookout for Metrics console, navigate to your detector.

You can also use an alert to trigger automations using AWS Lambda functions in order to drive API-driven operations on AWS IoT Core.

5 дней, 20 часов назад @ aws.amazon.com
Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library
Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library

To the best of the authors’ knowledge, no reference architectures and examples are available for GNN-based real-time inference solutions as of this writing.

It allows you to pack your model training scripts and dependencies in a Docker image, which it uses to create SageMaker training instances.

The function model_fn loads the saved model file and associated assets from the model_dir argument and the SageMaker model folder.

To fulfill real-time inference, we need to convert the GNN model inference from transductive mode to inductive mode.

ConclusionIn this post, we showed how to build a GNN-based real-time fraud detection solution using SageMaker, Neptune, and the DGL.

5 дней, 20 часов назад @ aws.amazon.com
Use computer vision to measure agriculture yield with Amazon Rekognition Custom Labels
Use computer vision to measure agriculture yield with Amazon Rekognition Custom Labels Use computer vision to measure agriculture yield with Amazon Rekognition Custom Labels

Rekognition Custom Labels lets you manage the ML model training process on the Amazon Rekognition console, which simplifies the end-to-end model development and inference process.

Create your Amazon Rekognition projectTo create your agriculture yield measuring project, complete the following steps:On the Amazon Rekognition console, choose Custom Labels.

Edit this file to replace the parameter bucket with your bucket name and model with your Amazon Rekognition model ARN.

Delete the Amazon Rekognition projectTo delete the Amazon Rekognition project, complete the following steps:On the Amazon Rekognition console, choose Use Custom Labels.

For more information about using custom labels, see Wha…

6 дней, 22 часа назад @ aws.amazon.com
Amazon SageMaker Automatic Model Tuning now supports SageMaker Training Instance Fallbacks
Amazon SageMaker Automatic Model Tuning now supports SageMaker Training Instance Fallbacks Amazon SageMaker Automatic Model Tuning now supports SageMaker Training Instance Fallbacks

Today Amazon SageMaker announced the support of SageMaker training instance fallbacks for Amazon SageMaker Automatic Model Tuning (AMT) that allow users to specify alternative compute resource configurations.

In the following sections, we walk through these high-level steps for overcoming an ICE:Define HyperParameter Tuning Job Configuration Define the Training Job Parameters Create the Hyperparameter Tuning Job Describe training jobDefine HyperParameter Tuning Job ConfigurationThe HyperParameterTuningJobConfig object describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, the ranges of the parameters to search, and the resource limits f…

1 неделя назад @ aws.amazon.com
Create Amazon SageMaker model building pipelines and deploy R models using RStudio on Amazon SageMaker
Create Amazon SageMaker model building pipelines and deploy R models using RStudio on Amazon SageMaker Create Amazon SageMaker model building pipelines and deploy R models using RStudio on Amazon SageMaker

We also explore using SageMaker for our model deployment, all using R.Solution overviewThe following diagram shows the architecture used in our solution.

If you’re new to using RStudio on SageMaker, review Get started with RStudio on Amazon SageMaker.

The easiest way to create a SageMaker pipeline is by using the SageMaker SDK, which is a Python library that we can access using the library reticulate.

Register model step – If the preceding conditional step is True , and the performance of the model is acceptable, then the model is registered in the model registry.

Serverless deployment of the modelAfter you’ve trained and registered a model on SageMaker, deploying the model on SageMaker is …

1 неделя назад @ aws.amazon.com
MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass
MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

Edge devices – Remote edge devices are hardware devices that can run ML models using Edge Manager.

AWS IoT Greengrass V2 – AWS IoT Greengrass allows you to deploy components into the simulated devices using an EC2 instance.

With AWS IoT Greengrass V2 and Edge Manager integration, it’s possible to use AWS IoT Greengrass V2 components.

With AWS IoT Greengrass V2 and Edge Manager integration, it’s possible to use AWS IoT Greengrass V2 components.

AWS IoT Greengrass installation and provisioningAfter we have the IAM policy and role in place, we’re ready to install AWS IoT Greengrass Core software with automatic resource provisioning.

1 неделя, 2 дня назад @ aws.amazon.com
Optimal pricing for maximum profit using Amazon SageMaker
Optimal pricing for maximum profit using Amazon SageMaker Optimal pricing for maximum profit using Amazon SageMaker

In this post, we share how Adspert created the pricing tool from scratch using different AWS services like Amazon SageMaker and how Adspert collaborated with the AWS Data Lab to accelerate this project from design to build in record time.

Product listing notifications coming from Amazon SQS are ingested in near-real time into the raw layer using an AWS Lambda function.

The original source data is stored in the Amazon Simple Storage Service (Amazon S3) raw layer bucket using Parquet data format.

The SageMaker training job is deployed using a SageMaker endpoint.

Ennio Pastore is a data architect on the AWS Data Lab team.

1 неделя, 6 дней назад @ aws.amazon.com
Amazon Comprehend announces lower annotation limits for custom entity recognition
Amazon Comprehend announces lower annotation limits for custom entity recognition Amazon Comprehend announces lower annotation limits for custom entity recognition

For example, you can immediately start detecting entities such as people, places, commercial items, dates, and quantities via the Amazon Comprehend console, AWS Command Line Interface, or Amazon Comprehend APIs.

To train a custom entity recognizer, you can provide training data to Amazon Comprehend as annotations or entity lists.

Dataset preparationIn this post, we explain how we trained an Amazon Comprehend custom entity recognizer using annotated documents.

With this announcement, we’re lowering the barrier to entry for users interested in using Amazon Comprehend custom entity recognition technology.

To learn more and get started with a custom entity recognizer, refer to Custom entity rec…

1 неделя, 6 дней назад @ aws.amazon.com
Promote feature discovery and reuse across your organization using Amazon SageMaker Feature Store and its feature-level metadata capability
Promote feature discovery and reuse across your organization using Amazon SageMaker Feature Store and its feature-level metadata capability Promote feature discovery and reuse across your organization using Amazon SageMaker Feature Store and its feature-level metadata capability

(one-hot encoded: or ) total_amount – Total amount of loans (numeric)The following figure shows example feature groups and feature metadata.

In this section, we interact with the Boto3 API endpoints to update and search feature metadata.

To begin improving feature search and discovery, you can add metadata using the update_feature_metadata API.

response = sagemaker_client.describe_feature_metadata( FeatureGroupName="customer", FeatureName="job_admin", ) # Navigate to 'Parameters' in response to get metadata metadata = response['Parameters']You can search for features by using the SageMaker search API using metadata as search parameters.

If you want to learn more about collaborating and shar…

1 неделя, 6 дней назад @ aws.amazon.com
NVIDIA
последний пост 4 часа назад
Using Network Graphs to Visualize Potential Fraud on Ethereum Blockchain
Using Network Graphs to Visualize Potential Fraud on Ethereum Blockchain Using Network Graphs to Visualize Potential Fraud on Ethereum Blockchain

This post provides a guided project to access, analyze, and identify potential fraud using blockchain data using Python.

In this post and accompanying Jupyter notebook, I discuss the following:The basics of blockchain, NFTs, and network graphs.

Furthermore, as blockchain data is public by design through decentralization, you can use network graphs to visualize economic behaviors on a respective blockchain.

Pulling data from the Ethereum blockchainThough all blockchain data is publicly available to anyone, it is still tough to access and prepare for analysis.

Next stepsBy following along with this post and accompanying notebook, you have seen how to access Ethereum blockchain data and analyz…

4 часа назад @ developer.nvidia.com
Running Large-Scale Graph Analytics with Memgraph and NVIDIA cuGraph Algorithms
Running Large-Scale Graph Analytics with Memgraph and NVIDIA cuGraph Algorithms Running Large-Scale Graph Analytics with Memgraph and NVIDIA cuGraph Algorithms

With the latest Memgraph Advanced Graph Extensions (MAGE) release, you can now run GPU-powered graph analytics from Memgraph in seconds, while working in Python.

from gqlalchemy import Memgraph memgraph = Memgraph("127.0.0.1", 7687) memgraph.drop_database()Import the dataset from CSV files.

Memgraph should help with the process of running graph analytics on large-scale graphs.

Louvain results visualized in Memgraph LabSummaryAnd there you have it: millions of nodes and relationships imported using Memgraph and analyzed using cuGraph PageRank and Louvain graph analytics algorithms.

With GPU-powered graph analytics from Memgraph, powered by NVIDIA cuGraph, you are able to explore massive grap…

17 часов назад @ developer.nvidia.com
Using Federated Learning to Bridge Data Silos in Financial Services
Using Federated Learning to Bridge Data Silos in Financial Services Using Federated Learning to Bridge Data Silos in Financial Services

Unlocking the full potential of artificial intelligence (AI) in financial services is often hindered by the inability to ensure data privacy during machine learning (ML).

We present three ways federated learning can be used in financial services and provide tips on getting started today.

On the other hand, federated learning does not assume that one unified dataset can be created.

In financial institutions, we see an incredible opportunity for federated learning to bridge internal data silos.

How does federated learning fit into an existing workflowIt is important to note that federated learning is a general technique.

23 часа назад @ developer.nvidia.com
Smart Devices, Smart Manufacturing: Pegatron Taps AI, Digital Twins
Smart Devices, Smart Manufacturing: Pegatron Taps AI, Digital Twins Smart Devices, Smart Manufacturing: Pegatron Taps AI, Digital Twins

In the fast-paced field of making the world’s tech devices, Pegatron Corp. initially harnessed AI to gain an edge.

It maintains hundreds of AI models, trained and running in production on NVIDIA GPUs.

Triton, NGC Simplify AI JobsPegatron uses NVIDIA Triton Inference Server, open-source software that helps deploy, run and scale AI models across all types of processors, and frameworks.

Hsiao’s team optimizes pretrained AI models it downloads in integrated Kubernetes containers from the NVIDIA NGC hub for GPU-optimized software.

Next Step: Digital TwinsTaking another step in smarter manufacturing, Pegatron is piloting NVIDIA Omniverse, a platform for developing digital twinsIt has two use case…

1 день, 2 часа назад @ blogs.nvidia.com
AI Shows the Way: Seoul Robotics Helps Cars Move, Park on Their Own
AI Shows the Way: Seoul Robotics Helps Cars Move, Park on Their Own AI Shows the Way: Seoul Robotics Helps Cars Move, Park on Their Own

Software company Seoul Robotics is using NVIDIA technology to make this possible — turning non-autonomous cars into self-driving vehicles.

Seoul Robotics’ platform, dubbed LV5 CTRL TWR, collects 3D data from the environment using cameras and lidar.

As a Metropolis member, Seoul Robotics received early access to software development kits and the NVIDIA Jetson AGX Orin for edge AI.

Equipped with LV5 CTRL TWR, the BMW facility has automated such movement of cars — resulting in time and cost savings.

Beyond automotive factories, Seoul Robotics envisions its platform to be deployed across the globe — at retail stores, airports, traffic intersections and more.

1 день, 2 часа назад @ blogs.nvidia.com
Digital Art Professor Kate Parsons Inspires Next Generation of Creators This Week ‘In the NVIDIA Studio’
Digital Art Professor Kate Parsons Inspires Next Generation of Creators This Week ‘In the NVIDIA Studio’ Digital Art Professor Kate Parsons Inspires Next Generation of Creators This Week ‘In the NVIDIA Studio’

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology accelerates creative workflows.

They design engaging animations, VR art exhibits and futuristic interactive AR displays.

#CreatorsJourney ChallengeIn the spirit of learning, the NVIDIA Studio team is posing a challenge for the community to show off personal growth.

Participate in the #CreatorsJourney challenge for a chance to be showcased on NVIDIA Studio social media channels.

Follow and tag NVIDIA Studio on Instagram, Twitter or Facebook, and use the #CreatorsJourney tag to join.

1 день, 4 часа назад @ blogs.nvidia.com
From Sapling to Forest: Five Sustainability and Employment Initiatives We’re Nurturing in India
From Sapling to Forest: Five Sustainability and Employment Initiatives We’re Nurturing in India From Sapling to Forest: Five Sustainability and Employment Initiatives We’re Nurturing in India

For over a decade, NVIDIA has invested in social causes and communities in India as part of our commitment to corporate social responsibility.

The project will train 3,000 farmers in Naandi’s Farmer Field Schools to earn sustained income by cultivating coffee and pepper plants using organic regenerative practices.

Previous efforts trained over 3,000 farmers across 115 villages and resulted in the production and distribution of nearly 34,000 kilograms of coffee fruit.

NVIDIA’s corporate social responsibility initiatives span the globe.

Read more about previous projects we’ve funded in India and corporate social responsibility at NVIDIA.

5 дней, 1 час назад @ blogs.nvidia.com
Top Israel Medical Center Partners with AI Startups to Help Detect Brain Bleeds, Other Critical Cases
Top Israel Medical Center Partners with AI Startups to Help Detect Brain Bleeds, Other Critical Cases Top Israel Medical Center Partners with AI Startups to Help Detect Brain Bleeds, Other Critical Cases

Israel’s largest private medical center is working with startups and researchers to bring potentially life-saving AI solutions to real-world healthcare workflows.

“We saw the impact right away,” said Dr. Michal Guindy, head of medical imaging and head of RISE at Assuta.

With Rhino Health, Assuta aims to help its collaborators develop AI models across hospitals internationally, resulting in more generalizable algorithms that perform more accurately across different patient populations.

Register for NVIDIA GTC, running online Sept. 19-22, to hear more from leaders in healthcare AI.

Subscribe to NVIDIA healthcare news and watch on demand as Assuta, Aidoc and Rhino Health speak at an GTC panel.

6 дней, 2 часа назад @ blogs.nvidia.com
GFN Thursday Brings Thunder to the Cloud With ‘Rumbleverse’ Arriving on GeForce NOW
GFN Thursday Brings Thunder to the Cloud With ‘Rumbleverse’ Arriving on GeForce NOW GFN Thursday Brings Thunder to the Cloud With ‘Rumbleverse’ Arriving on GeForce NOW

It’s time to rumble in Grapital City with Rumbleverse launching today on GeForce NOW.

That means gamers can tackle, uppercut, body slam and more from any GeForce NOW-compatible device, including mobile, at full PC quality.

Drop into the chaotic world of Grapital City, where players must brawl it out to become the champion.

Rumbleverse is free to play, so getting started is easy when paired with a free GeForce NOW membership.

And without having to wait for game downloads due to cloud streaming, members can dive into their new games as quickly as possible.

6 дней, 4 часа назад @ blogs.nvidia.com
Design in the Age of Digital Twins: A Conversation With Graphics Pioneer Donald Greenberg
Design in the Age of Digital Twins: A Conversation With Graphics Pioneer Donald Greenberg Design in the Age of Digital Twins: A Conversation With Graphics Pioneer Donald Greenberg

It’s one example Greenberg gives of how computer graphics are becoming part of every human enterprise.

A Whole New ChapterExpanding the frontier, he’s creating new tools for an architecture design course based on today’s capabilities for building realistic 3D worlds and digital twins.

“This is my next big project, and I’m very excited about it,” said the computer graphics professor of the work, which is sponsored by NVIDIA.

From Sketches to Digital TwinsFor Greenberg, it all comes down to the power of computer graphics.

Educators can request early access to the “Graphics & Omniverse” teaching kit.

1 неделя назад @ blogs.nvidia.com
AI Flying Off the Shelves: Restocking Robot Rolls Out to Hundreds of Japanese Convenience Stores
AI Flying Off the Shelves: Restocking Robot Rolls Out to Hundreds of Japanese Convenience Stores AI Flying Off the Shelves: Restocking Robot Rolls Out to Hundreds of Japanese Convenience Stores

Tokyo-based startup Telexistence this week announced it will deploy NVIDIA AI-powered robots to restock shelves at hundreds of FamilyMart convenience stores in Japan.

There are 56,000 convenience stores in Japan — the third-highest density worldwide.

“The first space we’re tackling this is through convenience stores — a huge network that supports daily life, especially in Japan, but is facing a labor shortage.”The company, founded in 2017, next plans to expand to convenience stores in the U.S., which is also plagued with a labor shortage in the retail industry — and where more than half of consumers say they visit one of the country’s 150,000 convenience stores at least once a month.

Telexi…

1 неделя назад @ blogs.nvidia.com
Future of Creativity on Display ‘In the NVIDIA Studio’ During SIGGRAPH Special Address
Future of Creativity on Display ‘In the NVIDIA Studio’ During SIGGRAPH Special Address Future of Creativity on Display ‘In the NVIDIA Studio’ During SIGGRAPH Special Address

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology accelerates creative workflows.

Announcements included a host of updates to a pillar of the NVIDIA Studio software suite: NVIDIA Omniverse, a platform for 3D design collaboration and world simulation.

Available now, the August NVIDIA Studio Driver gives creators peak reliability for using Omniverse and their favorite creative apps.

Choosing the right NVIDIA Studio laptop can be tricky, but students can use this guide to find the perfect tool to power their creativity — like the Lenovo Slim 7i Pro X, an N…

1 неделя, 1 день назад @ blogs.nvidia.com
At SIGGRAPH, NVIDIA CEO Jensen Huang Illuminates Three Forces Sparking Graphics Revolution
At SIGGRAPH, NVIDIA CEO Jensen Huang Illuminates Three Forces Sparking Graphics Revolution At SIGGRAPH, NVIDIA CEO Jensen Huang Illuminates Three Forces Sparking Graphics Revolution

With 45 demos and slides, five NVIDIA speakers announced:A new platform for creating avatars, NVIDIA Omniverse Avatar Cloud Engine (ACE).

Omniverse also will include neural graphics capabilities developed by NVIDIA Research that combine RTX graphics and AI.

Graphics Get SmartOne of the essential pillars of the emerging metaverse is neural graphics.

“Neural graphics intertwines AI and graphics, paving the way for a future graphics pipeline that is amenable to learning from data,” said Sanja Fidler, vice president of AI at NVIDIA.

For example, they can use neural graphics to capture objects and behaviors in the physical world quickly.

1 неделя, 1 день назад @ blogs.nvidia.com
NVIDIA AI Makes Performance Capture Possible With Any Camera
NVIDIA AI Makes Performance Capture Possible With Any Camera NVIDIA AI Makes Performance Capture Possible With Any Camera

NVIDIA AI tools are enabling deep learning-powered performance capture for creators at every level: visual effects and animation studios, creative professionals — even any enthusiast with a camera.

With NVIDIA Vid2Vid Cameo, creators can harness AI to capture their facial movements and expressions from any standard 2D video taken with a professional camera or smartphone.

And with 3D body-pose estimation software, creators can capture full-body movements like walking, dancing and performing martial arts — bringing virtual characters to life with AI.

And creative studios can harness AI-powered performance capture for concept design or previsualization — to quickly convey an idea of how certai…

1 неделя, 1 день назад @ blogs.nvidia.com
As Far as the AI Can See: ILM Uses Omniverse DeepSearch to Create the Perfect Sky
As Far as the AI Can See: ILM Uses Omniverse DeepSearch to Create the Perfect Sky As Far as the AI Can See: ILM Uses Omniverse DeepSearch to Create the Perfect Sky

For cutting-edge visual effects and virtual production, creative teams and studios benefit from digital sets and environments that can be updated in real time.

A crucial element in any virtual production environment is a sky dome, often used to provide realistic lighting for virtual environments and in-camera visual effects.

At SIGGRAPH today, ILM showcased how its team, with the NVIDIA DeepSearch tool, used natural language to rapidly search through a massive asset library and create a captivating sky dome.

Omniverse DeepSearch, however, lets ILM search intuitively through untagged assets using text or a 2D image.

With NVIDIA DeepSearch and Omniverse Enterprise, ILM has the potential to ac…

1 неделя, 1 день назад @ blogs.nvidia.com
Facebook
последний пост 1 неделя назад
Scaling data ingestion for machine learning training at Meta
Scaling data ingestion for machine learning training at Meta Scaling data ingestion for machine learning training at Meta

To facilitate the level of data ingestion required to support the training models supporting our products, we’ve had to build a new data ingestion infrastructure as well as new last-mile transformation pipelines.

In the sections below, we share our experience building data ingestion and last-mile data preprocessing pipelines that are responsible for feeding data into AI training models.

Data ingestion pipeline overviewWe have exabytes of training data powering our models, and the amount of training data is growing rapidly.

We have built a disaggregated Data PreProcessing tier (DPP) that serves as the reader tier for data ingestion and last-mile data transformations for AI training [Ref].

Sc…

1 неделя назад @ engineering.fb.com
Applying federated learning to protect data on mobile devices
Applying federated learning to protect data on mobile devices Applying federated learning to protect data on mobile devices

FL-DP enhances privacy in two important ways:It allows machine learning (ML) models to be trained in a distributed way so that users’ data remains on their mobile devices.

It adds noise to reduce the risk of an ML model memorizing user data.

Such an approach could enhance user privacy while still facilitating an intelligent, safe, and intuitive user experience across Meta’s family of technologies.

How it works:With FL-DP, ML models are trained in a federated manner where mobile devices learn locally.

This architecture is a combination of infrastructure across mobile devices, trusted execution environments, and conventional back-end servers.

2 месяца назад @ engineering.fb.com
VESPA: Static profiling for binary optimization
VESPA: Static profiling for binary optimization VESPA: Static profiling for binary optimization

What the research is:Recent research has demonstrated that binary optimization is important for achieving peak performance for various applications.

VESPA expands on ESP in several ways to make it useful in the context of binary optimizers.

VESPA increases the scope where binary optimizers can be used, thus enhancing the range of applications that can leverage these tools to improve their performance.

Once the static profile data produced by VESPA is injected into a binary optimizer, this tool can proceed with its optimization steps as usual, completely oblivious to how the profile data was computed.

VESPA, therefore, can very easily be integrated into existing binary optimizers, which we d…

5 месяцев назад @ engineering.fb.com
Uber Engineering Uber Engineering
последний пост 2 недели, 1 день назад
ML Education at Uber: Program Design and Outcomes
ML Education at Uber: Program Design and Outcomes ML Education at Uber: Program Design and Outcomes

Share Vote Reddit WhatsApp 0 SharesIntroductionIf you have read our previous article, ML Education at Uber: Frameworks Inspired by Engineering Principles, you have seen several examples of how Uber benefits from applying Engineering Principles to drive the ML Education Program’s content design and program frameworks.

How were the ML Education program creators able to capture and communicate this value so that the program could scale to what it is today?

When a larger percentage of non-ML engineers attend ML Education courses it means that we are distilling ML expertise to the broader ML market, increasing the overall internal ML market size for Uber.

ConclusionUber’s ML Education Program ha…

2 недели, 1 день назад @ eng.uber.com
ML Education at Uber: Frameworks Inspired by Engineering Principles
ML Education at Uber: Frameworks Inspired by Engineering Principles ML Education at Uber: Frameworks Inspired by Engineering Principles

Part 1 will introduce our design principles and explain the benefits of applying these principles to technical education content design and program frameworks, specifically in the ML domain.

Core Principles of Uber’s ML Education ProgramThe capabilities of Uber’s ML infrastructure and ecosystem have enabled us to design, implement, and ground our ML Education program in our design principles.

Aside from the core principle of reproducibility discussed above, we have a list of other design principles that comprise Uber’s ML Education program:Because our subject matter is highly technical, we felt it appropriate to derive our design principles from industry-recognized engineering principles.

H…

2 недели, 6 дней назад @ eng.uber.com
Uber’s Real-Time Document Check
Uber’s Real-Time Document Check Uber’s Real-Time Document Check

Real-Time Document Check CriteriaFrom the onset, we knew that the Real-Time Document Check product needed to meet 4 non-negotiable criteria:Data privacy: Adherence to best practices for handling personal data, taking into account local laws, regulations, and norms in all countries where the product is available.

In the Document Image Processing module, a list of operations (including document classification, transcription, and fraud detection) are applied to the uploaded document images via different technologies (e.g., 3rd-party vendor, Uber in-house technology, and human review).

Looking into the FutureReal World ImpactAs of May 2022, Real-Time ID Document Check is live in Brazil, Mexico,…

2 месяца, 1 неделя назад @ eng.uber.com
DeepETA: How Uber Predicts Arrival Times Using Deep Learning
DeepETA: How Uber Predicts Arrival Times Using Deep Learning DeepETA: How Uber Predicts Arrival Times Using Deep Learning

By training machine learning (ML) models on top of the road graph prediction using historical data in combination with real-time signals, we can refine ETAs that better predict real-world outcomes.

To meet these challenges, Uber AI partnered with Uber’s Maps team on a project called DeepETA to develop a low-latency deep neural network architecture for global ETA prediction.

We take a similar approach to ETA prediction at Uber.

Conclusions and Future WorkWe have launched this DeepETA model into production for global 4-wheel ETA prediction.

The DeepETA model launch makes it both possible and efficient to train and serve large-scale Deep Learning models that predict ETAs better than XGBoost ap…

6 месяцев, 1 неделя назад @ eng.uber.com
Project RADAR: Intelligent Early Fraud Detection System with Humans in the Loop
Project RADAR: Intelligent Early Fraud Detection System with Humans in the Loop Project RADAR: Intelligent Early Fraud Detection System with Humans in the Loop

Industry-wide, payment fraud losses are measured in terms of the fraction of gross amounts processed.

RADAR is an AI fraud detection and mitigation system with humans in the loop.

RADAR fraud protection rules are generally short-lived and targeted reactions to the unexpected attacks.

We will define some terminology to discuss time series data below:OT – “Order time” when the specific order has been fulfilled.

ConclusionIn this blog, we presented the RADAR system and how it brings together many components of Uber’s technical ecosystem to solve a complex business problem.

6 месяцев, 2 недели назад @ eng.uber.com
Capacity Recommendation Engine: Throughput and Utilization Based Predictive Scaling
Capacity Recommendation Engine: Throughput and Utilization Based Predictive Scaling Capacity Recommendation Engine: Throughput and Utilization Based Predictive Scaling

We recently built a new system, Capacity Recommendation Engine (CRE), with a new algorithm that relies on throughput and utilization based scaling with machine learning modeling.

Take throughput estimation for weekly scaling as an example: the target throughput RPS Target should be the estimation of the next week’s peak traffic.

Define Target UtilizationTarget utilization (Utilization Target ) is the one of the signals required to deduce the capacity number in CRE.

Linear Regression: Normalized Throughput and UtilizationFor utilization-bound service, utilization, throughput, capacity, service, and hardware performance are common factors, which influence each other.

ConclusionIn this article…

6 месяцев, 4 недели назад @ eng.uber.com
neptune.ai neptune.ai
последний пост 7 часов назад
How to Solve the Model Serving Component of the MLOps Stack
How to Solve the Model Serving Component of the MLOps Stack How to Solve the Model Serving Component of the MLOps Stack

Serving Machine Learning models the right wayML model serving has a tight relationship with metadata stores, ML model registries, monitoring components, and feature stores.

If we have a high-performance server that is a nightmare to integrate with our observability, feature stores, and model registries, we have a terrible model serving component.

Our ML serving component periodically checks in with the ML model registry, and if there’s a new model with the compatible tag, it will update the deployment.

Model versions visible in the Neptune model registry | See in the appOf course, as mentioned earlier, frequently, the model serving component has to interact with feature stores.

Think of the…

7 часов назад @ neptune.ai
Active Learning: Strategies, Tools, and Real-World Use Cases
Active Learning: Strategies, Tools, and Real-World Use Cases Active Learning: Strategies, Tools, and Real-World Use Cases

Diagram of active learning system | Source: AuthorWhy do we need active learning?

Active learning use case in NLP (NER) | SourceAs we can see above, clearly, all of the active learning strategies are outperforming the random sampling (RAND) baseline performance by a good margin.

Sample of selected frames via active learning | SourceAside from the cost advantages, a significant improvement in mean average precision (from an objection detection perspective) was observed using active learning.

The improvement of protein production | SourceSome popular frameworks used for Active Learning1.modAL: A modular active learning framework for Python3modAL is an active learning framework for Python3, de…

1 неделя, 1 день назад @ neptune.ai
Transformer NLP Models (Meena and LaMDA): Are They “Sentient” and What Does It Mean for Open-Domain Chatbots?
Transformer NLP Models (Meena and LaMDA): Are They “Sentient” and What Does It Mean for Open-Domain Chatbots? Transformer NLP Models (Meena and LaMDA): Are They “Sentient” and What Does It Mean for Open-Domain Chatbots?

Training data: Meena is trained on a large amount of dialogue data, this is different from previous models.

Building on the early work of Meena, LaMDA introduced a number of new approaches to dialogue models, which resulted in impressive results.

In this post, we looked at two models, namely Meena and LaMDA, which are both dialogue models, and highlighted some of their key technical innovations.

You may have to invest more resources trying to make these dialogue models “fit” your domain-specific application.

So that still represents a big obstacle to the easy application of these dialogue models.

1 неделя, 1 день назад @ neptune.ai
Setting up MLOps at a Reasonable Scale with Jacopo Tagliabue
Setting up MLOps at a Reasonable Scale with Jacopo Tagliabue Setting up MLOps at a Reasonable Scale with Jacopo Tagliabue

You’ll learn about:1 What is a reasonable scale MLOpsWhat is a reasonable scale MLOps 2 How to set up MLOps at a reasonable scaleHow to set up MLOps at a reasonable scale 3 What tools to use and whether to buy or build themWhat tools to use and whether to buy or build them 4 How to deliver models to customersHow to deliver models to customers 5 What are the limits of reasonable scaleWhat are the limits of reasonable scale 6 And much more.

Sabine Nyholm: It’s our pleasure to introduce Jacopo Tagliabue, who has even been called the father of reasonable scale MLOps.

The idea of the reasonable scale stack came from the realization that most of our business problems are per customer or organizat…

2 недели, 5 дней назад @ neptune.ai
Building MLOps Pipeline for Computer Vision: Image Classification Task [Tutorial]
Building MLOps Pipeline for Computer Vision: Image Classification Task [Tutorial] Building MLOps Pipeline for Computer Vision: Image Classification Task [Tutorial]

Aim of the project: bird image classifierThe aim of the project is to build an image classifier to classify different species of birds.

Building the image classification modelAs mentioned before, research and planning is the key to implementing any machine learning project.

MLOps pipeline for image classification: building the vision transformer using PytorchI have created the full model as per the author’s description of ViT in their paper.

The image on the left is the original image whereas the image on the right is overlaid with the attention map.

MLOps pipeline for image classification: creating the app using StreamlitThe Streamlit app will be a web app that we will deploy on the cloud.

3 недели, 5 дней назад @ neptune.ai
Building MLOps Pipeline for Time Series Prediction [Tutorial]
Building MLOps Pipeline for Time Series Prediction [Tutorial] Building MLOps Pipeline for Time Series Prediction [Tutorial]

In this tutorial, we’ll present a simple example of a time-series-based ML project and build an MLOps pipeline for that.

Exploratory data analysis (EDA) – understanding our data using data analysis and visualization techniques.

– understanding our data using data analysis and visualization techniques.

MLOps pipeline for time series prediction: model developmentResearchAs we mentioned before, a good practice of ML project development is to start with research.

ConclusionIn this tutorial, we’ve presented a simple end-to-end ML time series project following MLOps practices.

1 месяц назад @ neptune.ai
Building Deep Learning-Based OCR Model: Lessons Learned
Building Deep Learning-Based OCR Model: Lessons Learned Building Deep Learning-Based OCR Model: Lessons Learned

In this article, you will learn about different lessons for building a deep learning-based OCR model so that when you are working on any such use case, you may not face the issues that I have faced during the development and deployment.

OCR has become very popular nowadays and has been adopted by several industries for faster text data reading from images.

I have also worked on insurance documents OCR where information from different documents needed to be extracted and used for several other purposes like user profile creation, user verification, etc.

Labeling the data (data annotation)ProblemNow that you have your data and also created new samples using image augmentation techniques, the …

1 месяц назад @ neptune.ai
9 Things That Can Make Your ML Team Meetings More Effective
9 Things That Can Make Your ML Team Meetings More Effective 9 Things That Can Make Your ML Team Meetings More Effective

In particular, we provide tips for meetings under different phases of an ML project development to ensure smoothness along the entire journey.

Below we describe the memory-wise and temporal limitations worth discussing in your early ML meetings.

Diving a bit deeper into Neptune.ai and how it is useful in the context of ML team meetings:It stores individual runs with the metadata in clear tables.

Don’t call a meeting without resultsA data science team meeting should only happen to discuss tangible resultsThis is perhaps the most important tip in making your ML meetings effective.

9 small tips to help you along the way in making your ML meetings more effective.

1 месяц, 1 неделя назад @ neptune.ai
Kedro vs ZenML vs Metaflow: Which Pipeline Orchestration Tool Should You Choose?
Kedro vs ZenML vs Metaflow: Which Pipeline Orchestration Tool Should You Choose? Kedro vs ZenML vs Metaflow: Which Pipeline Orchestration Tool Should You Choose?

Just like ZenML, Metaflow also uses steps to decorate functions, so it can be quite similar to users with ZenML experience.

Ingesting data: Kedro > ZenML > MetaflowIntegrationsKedro: pluginsMost of the kedro capability comes from its plugins.

And while kedro adds them as plugins, they actually do their job as an independent component integrated into the kedro structure.

Tool Kedro ZenML Metaflow Programming languages Python Kedro: ZenML: Metaflow: R Kedro: ZenML: Metaflow: Pricing Free-to-use Kedro: ZenML: Metaflow: Code structure Data sources abstraction Kedro: ZenML: Metaflow: Separated environments Kedro: ZenML: Metaflow: Stacks Kedro: ZenML: Metaflow: Object oriented Kedro: ZenML: Metaf…

1 месяц, 2 недели назад @ neptune.ai
Real-World MLOps Examples: Model Development in Hypefactors
Real-World MLOps Examples: Model Development in Hypefactors Real-World MLOps Examples: Model Development in Hypefactors

In this first installment of the series “Real-world MLOps Examples,” Jules Belveze, an MLOps Engineer, will walk you through the model development process at Hypefactors, including the types of models they build, how they design their training pipeline, and other details you may find valuable.

Today, I work for a media intelligence tech company called Hypefactors, where I develop NLP models to help our users gain insights from the media landscape.

Model development at HypefactorsCould you elaborate on the types of models you build at Hypefactors?

ML workflow at Hypefactors | Source: AuthorCould you describe your tool stack for model development?

Hypefactors model training and evaluation sta…

1 месяц, 2 недели назад @ neptune.ai
5 Model Deployment Mistakes That Can Cost You a Lot
5 Model Deployment Mistakes That Can Cost You a Lot 5 Model Deployment Mistakes That Can Cost You a Lot

In Data Science projects, model deployment is probably the most critical and complex part of the whole lifecycle.

What you could try: use one of the deployment strategiesBlue-green deploymentBlue-green model deployment | SourceThis deployment strategy comprises having 2 versions (both old and new) of the service deployed in production at the same time.

If you have a well-defined ML monitoring architecture (Best Tools to Do ML Model Monitoring), you would be able to assess the accuracy of the model by introducing a feedback loop with the ground truth.

Learn more Model Deployment StrategiesMistake #3: Not enabling automated (prediction) service rollbackImagine that you have a production model…

1 месяц, 3 недели назад @ neptune.ai
MLOps at a Reasonable Scale [The Ultimate Guide]
MLOps at a Reasonable Scale [The Ultimate Guide]

For a couple of years now, MLOps is probably the most often used term in the ML industry. The more models people want to deploy to production, the more they think about how to organize the Ops part of this process. Naturally, the way to do MLOps has been shaped by the big players on […]

The post MLOps at a Reasonable Scale [The Ultimate Guide] appeared first on neptune.ai.

2 месяца назад @ neptune.ai
Imbalanced Data in Object Detection Computer Vision Projects
Imbalanced Data in Object Detection Computer Vision Projects Imbalanced Data in Object Detection Computer Vision Projects

Imbalance in object detection modelsObject detection is simultaneously locating the object of interest in a picture while categorizing it into a certain class.

The first generation of object detection algorithms mostly relied on hand-crafted features and linear classifiers, before deep learning came into the picture.

| Source: AuthorClass imbalance from an object detection point of view can be subclassified into two types – foreground-background imbalance and foreground-foreground imbalance.

Faster RCNN, a rather popular method of object detection uses a Feature pyramid network (FPN) for Region Proposal.

IoU distribution ImbalanceWhen the distribution of IoU for bounding boxes (across the d…

2 месяца, 1 неделя назад @ neptune.ai
AutoML Solutions: What I Like and Don’t Like About AutoML as a Data Scientist
AutoML Solutions: What I Like and Don’t Like About AutoML as a Data Scientist AutoML Solutions: What I Like and Don’t Like About AutoML as a Data Scientist

In fact, even if AutoML solutions become 10x better, it will not make Machine Learning specialists of any trade irrelevant.

Prone to over-optimization/over-fittingDepending on the nature of your data, and your model validation setup, some AutoML solutions can easily overfit.

A word on AutoML benchmarksThe literature on AutoML benchmarks is fairly scarce, and most often it compares the performance of AutoML solutions omitting the performance of humans.

Thankfully, we do have some work in establishing standardized ways to assess the performance of different AutoML solutions.

Another important thing you should get from this blog post: Invest all the time you save using AutoML on feature engine…

2 месяца, 1 неделя назад @ neptune.ai
Automated Testing in Machine Learning Projects [Best Practices for MLOps]
Automated Testing in Machine Learning Projects [Best Practices for MLOps] Automated Testing in Machine Learning Projects [Best Practices for MLOps]

But besides software testing, automated testing can include some other types of testing such as hardware, security, performance, and others.

In contrast to conventional software testing, in ML testing we need to pay special attention to data testing .

Model testingJust like for data testing, model testing can be a part of unit testing, integration testing, or regression testing.

Monitoring models in production: similarly as for monitoring data tests, most of the model tests were covered in the section about model tests.

Open table in new window+ Expand all – Collapse all Jenkins GitHub Actions Unittests Pytest Deepchecks CheckList Aporia Arize AI WhyLabs Smoketesting Unit testing Integratio…

2 месяца, 2 недели назад @ neptune.ai
▶️ YouTube
Yannic Kilcher Yannic Kilcher
последний пост 4 дня, 6 часов назад
The Man behind Stable Diffusion
The Man behind Stable Diffusion The Man behind Stable Diffusion

#stablediffusion #ai #stabilityai An interview with Emad Mostaque, founder of Stability AI. OUTLINE:

0:00 - Intro

1:30 - What is Stability AI?

3:45 - Where does the money come from?

5:20 - Is this the CERN of AI?

6:15 - Who gets access to the resources?

8:00 - What is Stable Diffusion?

11:40 - What if your model produces bad outputs?

14:20 - Do you employ people?

16:35 - Can you prevent the corruption of profit?

19:50 - How can people find you?

22:45 - Final thoughts, let's destroy PowerPoint Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://ykilcher.com/discord

Link…

4 дня, 6 часов назад @ youtube.com
[ML News] AI models that write code (Copilot, CodeWhisperer, Pangu-Coder, etc.)
[ML News] AI models that write code (Copilot, CodeWhisperer, Pangu-Coder, etc.) [ML News] AI models that write code (Copilot, CodeWhisperer, Pangu-Coder, etc.)

#mlnews #ai #copilot OUTLINE:

0:00 - Intro

0:20 - Copilot Now Generally Available

3:20 - FOSS Org leaves GitHub

6:45 - Google's Internal ML Code Completion

9:10 - AI Trains Itself to Code Better

14:30 - Amazon CodeWhisperer in Preview

15:15 - Pangu-Coder: A New Coding Model

17:10 - Useful Things References:

Copilot Now Generally Available

https://github.blog/2022-06-21-github-copilot-is-generally-available-to-all-developers/ FOSS Org leaves GitHub

https://www.theregister.com/2022/06/30/software_freedom_conservancy_quits_github/

https://sfconservancy.org/blog/2022/jun/30/give-up-github-launch/

https://sfconservancy.org/GiveUpGitHub/

https://sfconservancy.org/docs/SupportGiveUpGitHub-README-s…

6 дней, 20 часов назад @ youtube.com
[ML News] Text-to-Image models are taking over! (Imagen, DALL-E 2, Midjourney, CogView 2 & more)
[ML News] Text-to-Image models are taking over! (Imagen, DALL-E 2, Midjourney, CogView 2 & more) [ML News] Text-to-Image models are taking over! (Imagen, DALL-E 2, Midjourney, CogView 2 & more)

#mlnews #dalle #imagen All things text-to-image models like DALL-E and Imagen! OUTLINE:

0:00 - Intro

0:30 - Imagen: Google's Text-to-Image Diffusion Model

7:15 - Unified I/O by AllenAI

9:40 - CogView2 is Open-Source

11:05 - Google bans DeepFakes from Colab

13:05 - DALL-E generates real Cosmopolitan cover

15:45 - DALL-E tips & tricks

17:00 - Midjourney moves to Open Beta

17:50 - DALLE-mini is not Crayon

19:00 - Deep Learning Resources References:

Imagen: Google's Text-to-Image Diffusion Model

https://imagen.research.google/?utm_source=pocket_mylist

https://arxiv.org/pdf/2205.11487.pdf Unified I/O by AllenAI

https://unified-io.allenai.org/

https://blog.allenai.org/introducing-ai2s-unified-io-…

1 неделя, 3 дня назад @ youtube.com
[ML News] This AI completes Wikipedia! Meta AI Sphere | Google Minerva | GPT-3 writes a paper
[ML News] This AI completes Wikipedia! Meta AI Sphere | Google Minerva | GPT-3 writes a paper [ML News] This AI completes Wikipedia! Meta AI Sphere | Google Minerva | GPT-3 writes a paper

#mlnews #ai #minerva This episode is all about models that reason. OUTLINE:

0:00 - Intro

0:35 - Meta AI learns Wikipedia citations

5:25 - Google's Minerva solves math problems by reading papers

9:10 - GPT-3 writes a paper on itself

13:35 - Jürgen Schmidhuber prompts LeCun for missing citations References:

Meta AI learns Wikipedia citations

https://tech.fb.com/artificial-intelligence/2022/07/how-ai-could-help-make-wikipedia-entries-more-accurate/

https://ai.facebook.com/blog/introducing-sphere-meta-ais-web-scale-corpus-for-better-knowledge-intensive-nlp/?d=%7B%22u%22%3A100051861999022%2C%22f%22%3A207799259245384%2C%22t%22%3A1658664021%2C%22ed%22%3A[]%7D&s=AWVELTip1y4HowJprXc

https://github.c…

2 недели, 3 дня назад @ youtube.com
[ML News] BLOOM: 176B Open-Source | Chinese Brain-Scale Computer | Meta AI: No Language Left Behind
[ML News] BLOOM: 176B Open-Source | Chinese Brain-Scale Computer | Meta AI: No Language Left Behind [ML News] BLOOM: 176B Open-Source | Chinese Brain-Scale Computer | Meta AI: No Language Left Behind

#mlnews #bloom #ai Today we look at all the recent giant language models in the AI world! OUTLINE:

0:00 - Intro

0:55 - BLOOM: Open-Source 176B Language Model

5:25 - YALM 100B

5:40 - Chinese Brain-Scale Supercomputer

7:25 - Meta AI Translates over 200 Languages

10:05 - Reproducibility Crisis Workshop

10:55 - AI21 Raises $64M

11:50 - Ian Goodfellow leaves Apple

12:20 - Andrej Karpathy leaves Tesla

12:55 - Wordalle References:

BLOOM: Open-Source 176B Language Model

https://bigscience.huggingface.co/blog/bloom

https://huggingface.co/spaces/bigscience/license

https://huggingface.co/bigscience/bloom?text=34%2B10%3D44+%0A54%2B20%3D YALM 100B

https://github.com/yandex/YaLM-100B Chinese Brain-Scale …

2 недели, 6 дней назад @ youtube.com
JEPA - A Path Towards Autonomous Machine Intelligence (Paper Explained)
JEPA - A Path Towards Autonomous Machine Intelligence (Paper Explained) JEPA - A Path Towards Autonomous Machine Intelligence (Paper Explained)

#jepa #ai #machinelearning Yann LeCun's position paper on a path towards machine intelligence combines Self-Supervised Learning, Energy-Based Models, and hierarchical predictive embedding models to arrive at a system that can teach itself to learn useful abstractions at multiple levels and use that as a world model to plan ahead in time. OUTLINE:

0:00 - Introduction

2:00 - Main Contributions

5:45 - Mode 1 and Mode 2 actors

15:40 - Self-Supervised Learning and Energy-Based Models

20:15 - Introducing latent variables

25:00 - The problem of collapse

29:50 - Contrastive vs regularized methods

36:00 - The JEPA architecture

47:00 - Hierarchical JEPA (H-JEPA)

53:00 - Broader relevance

56:00 - Summ…

1 месяц, 1 неделя назад @ youtube.com
ARC Challenge Live Coding
ARC Challenge Live Coding ARC Challenge Live Coding

Chatting & Coding

1 месяц, 2 недели назад @ youtube.com
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos (Paper Explained)
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos (Paper Explained) Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos (Paper Explained)

#openai #vpt #minecraft Minecraft is one of the harder challenges any RL agent could face. Episodes are long, and the world is procedurally generated, complex, and huge. Further, the action space is a keyboard and a mouse, which has to be operated only given the game's video input. OpenAI tackles this challenge using Video PreTraining, leveraging a small set of contractor data in order to pseudo-label a giant corpus of scraped footage of gameplay. The pre-trained model is highly capable in basic game mechanics and can be fine-tuned much better than a blank slate model. This is the first Minecraft agent that achieves the elusive goal of crafting a diamond pickaxe all by itself. OUTLINE:

0:00…

1 месяц, 3 недели назад @ youtube.com
Parti - Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (Paper Explained)
Parti - Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (Paper Explained) Parti - Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (Paper Explained)

#parti #ai #aiart Parti is a new autoregressive text-to-image model that shows just how much scale can achieve. This model's outputs are crips, accurate, realistic, and can combine arbitrary styles, concepts, and fulfil even challenging requests. OUTLINE:

0:00 - Introduction

2:40 - Example Outputs

6:00 - Model Architecture

17:15 - Datasets (incl. PartiPrompts)

21:45 - Experimental Results

27:00 - Picking a cherry tree

29:30 - Failure cases

33:20 - Final comments Website: https://parti.research.google/

Paper: https://arxiv.org/abs/2206.10789

Github: https://github.com/google-research/parti Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTube: https://www.youtube.co…

1 месяц, 3 недели назад @ youtube.com
Did Google's LaMDA chatbot just become sentient?
Did Google's LaMDA chatbot just become sentient? Did Google's LaMDA chatbot just become sentient?

#lamda #google #ai Google engineer Blake Lemoine was put on leave after releasing proprietary information: An interview with the chatbot LaMDA that he believes demonstrates that this AI is, in fact, sentient. We analyze the claims and the interview in detail and trace how a statistical machine managed to convince at least one human that it is more than just an algorithm. OUTLINE:

0:00 - Whistleblower put on leave

4:30 - What is a language model?

6:40 - The prompt is the key

10:40 - Who are we talking to exactly?

12:50 - LaMDA analyzes stories

15:20 - Fear, pain, and consent

20:25 - How would we recognize sentience? When is a machine conscious? References:

https://cajundiscordian.medium.com/…

2 месяца назад @ youtube.com
This is the worst AI ever
This is the worst AI ever This is the worst AI ever

#gpt4chan #4chan #ai GPT-4chan was trained on over 3 years of posts from 4chan's "politically incorrect" (/pol/) board. EXTRA VIDEO HERE: https://www.youtube.com/watch?v=dQw4w9WgXcQ Website (try the model here): https://gpt-4chan.com

Model: https://huggingface.co/ykilcher/gpt-4chan

Code: https://github.com/yk/gpt-4chan-public

Dataset: https://zenodo.org/record/3606810#.YpjGgexByDU OUTLINE:

0:00 - Intro

0:30 - Disclaimers

1:20 - Elon, Twitter, and the Seychelles

4:10 - How I trained a language model on 4chan posts

6:30 - How good is this model?

8:55 - Building a 4chan bot

11:00 - Something strange is happening

13:20 - How the bot got unmasked

15:15 - Here we go again

18:00 - Final thoughts L…

2 месяца, 2 недели назад @ youtube.com
Did I crash the NFT market?
Did I crash the NFT market? Did I crash the NFT market? 2 месяца, 2 недели назад @ youtube.com
[ML News] DeepMind's Flamingo Image-Text model | Locked-Image Tuning | Jurassic X & MRKL
[ML News] DeepMind's Flamingo Image-Text model | Locked-Image Tuning | Jurassic X & MRKL [ML News] DeepMind's Flamingo Image-Text model | Locked-Image Tuning | Jurassic X & MRKL

#flamingo #mlnews #tech Your updates directly from the state of the art in Machine Learning! OUTLINE:

0:00 - Intro

0:30 - DeepMind's Flamingo: Unified Vision-Language Model

8:25 - LiT: Locked Image Tuning

10:20 - Jurassic X & MRKL Systems

15:05 - Helpful Things

22:40 - This AI does not exist References:

DeepMind's Flamingo: Unified Vision-Language Model

https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model

https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/tackling-multiple-tasks-with-a-single-visual-language-model/flamingo.pdf LiT: Locked Image Tuning

https://ai.googleblog.com/2022/04/locked-image-tuning-adding-language.html

https://google-r…

3 месяца назад @ youtube.com
[ML News] Meta's OPT 175B language model | DALL-E Mega is training | TorToiSe TTS fakes my voice
[ML News] Meta's OPT 175B language model | DALL-E Mega is training | TorToiSe TTS fakes my voice [ML News] Meta's OPT 175B language model | DALL-E Mega is training | TorToiSe TTS fakes my voice

#mlnews #dalle #gpt3 An inside look of what's happening in the ML world! Sponsor: Weights & Biases

https://wandb.me/yannic OUTLINE:

0:00 - Intro

0:20 - Sponsor: Weights & Biases

1:40 - Meta AI releases OPT-175B

4:55 - CoCa: New CLIP-Competitor

8:15 - DALL-E Mega is training

10:05 - TorToiSe TTS is amazing!

11:50 - Investigating Vision Transformers

12:50 - Hugging Face Deep RL class launched

13:40 - Helpful Things

17:00 - John Deere's driverless tractors References:

Meta AI releases OPT-175B

https://ai.facebook.com/blog/democratizing-access-to-large-scale-language-models-with-opt-175b/

https://arxiv.org/abs/2205.01068

https://arxiv.org/pdf/2205.01068.pdf

https://github.com/facebookresearch/m…

3 месяца, 1 неделя назад @ youtube.com
This Ape Does Not Exist! (AI creates new NFTs)
This Ape Does Not Exist! (AI creates new NFTs) This Ape Does Not Exist! (AI creates new NFTs)

#nft #gan #ai Today we build our own AI that can create as many bored apes as we want! Fungibility for everyone! Try the model here: https://huggingface.co/spaces/ykilcher/apes

or here: https://ykilcher.com/apes

Files & Models here: https://huggingface.co/ykilcher/apes/tree/main

Code here: https://github.com/yk/apes-public This video is sponsored by BrightData, use this link for 25$ free credits (and they match your first deposit up to 250$):

https://brightdata.grsm.io/yannickilcher OUTLINE:

0:00 - Introduction

2:05 - Generative Adversarial Networks

3:40 - Scraping Opensea with BrightData

7:55 - Training the GAN

11:35 - Here are the results!

15:20 - Diving deeper into BrightData References:…

3 месяца, 1 неделя назад @ youtube.com
Henry AI Labs Henry AI Labs
последний пост 1 неделя, 6 дней назад
Weaviate User Experience - Weaviate Podcast Recap
Weaviate User Experience - Weaviate Podcast Recap Weaviate User Experience - Weaviate Podcast Recap

Please check out the full podcast here: https://www.youtube.com/watch?v=gjJBYcYMB-o This video is a commentary on the latest Weaviate Podcast with Laura Ham on the Weaviate User Experience. User Experience describes a suite of things from the performance of the tech, API interfaces, documentation, and communication strategy -- as outlined by Bob van Luijt here: https://twitter.com/bobvanluijt/status/1552379772747096064. Laura has lead the development of the GraphQL API that makes Weaviate so friendly and exciting to use! I really hope you enjoy learning more about these topics. Here are some additional links referenced in the video: Wikipedia Weaviate Example: https://weaviate.io/developers…

1 неделя, 6 дней назад @ youtube.com
Thoughts on Weaviate v1.14 Release!
Thoughts on Weaviate v1.14 Release! Thoughts on Weaviate v1.14 Release!

Hey everyone! Here are some of my thoughts and lessons learned on the new Weaviate v1.14 release! Please check out the full length podcast linked here: https://www.youtube.com/watch?v=eiQaZIhUS_o. Some references from the video:

Weaviate v1.14 Blog Post: https://weaviate.io/blog/2022/07/Weaviate-release-1-14.html#stronger-together

CO-Search: https://arxiv.org/pdf/2006.09595.pdf

Prometheus: https://prometheus.io/docs/introduction/overview/

Literature-Augmented Clinical Outcome Prediction: https://aclanthology.org/2022.findings-naacl.33.pdf

Sigmoid-MSE vs. Softmax Cross-Entropy: https://wandb.ai/ayush-thakur/dl-question-bank/reports/Sigmoid-MSE-vs-Softmax-Cross-Entropy--VmlldzoyMDA3ODQ

1 месяц назад @ youtube.com
Approximate Nearest Neighbor Benchmarks - Weaviate Podcast Recap
Approximate Nearest Neighbor Benchmarks - Weaviate Podcast Recap Approximate Nearest Neighbor Benchmarks - Weaviate Podcast Recap

Please check out the full podcast here: https://www.youtube.com/watch?v=kG3ji89AFyQ This video is a commentary on the latest Weaviate Podcast with Etienne Dilocker on ANN Benchmarks. ANN search -- short for Approximate Nearest Neighbors -- describes algorithms that enable efficient distance comparison between an encoded query vector and a vector database. For example, we may have 1 billion vectors to search through -- we don't want to do a dot product distance between our query and 1 billion candidate vectors! This podcast describes Weaviate's efforts to benchmark HNSW within the Weaviate system and give users a sense of how performance varies with respect to each dataset (and their respect…

2 месяца, 2 недели назад @ youtube.com
Search through Y Combinator startups with Weaviate!
Search through Y Combinator startups with Weaviate! Search through Y Combinator startups with Weaviate!

Please check out Eric Jang's article "Ranking YC Companies with a Neural Net": https://evjang.com/2022/04/02/yc-rank.html Please subscribe to SeMI Technologies on YouTube! https://www.youtube.com/c/SeMI-and-Weaviate Timecodes

0:00 Introduction

0:58 Weaviate Demo

3:40 Article Overview

10:45 NLP for Venture Capital and Data-Centric AI

4 месяца, 2 недели назад @ youtube.com
MosaicML Composer for faster and cheaper Deep Learning!
MosaicML Composer for faster and cheaper Deep Learning! MosaicML Composer for faster and cheaper Deep Learning!

Please leave a star! https://github.com/mosaicml/composer Thank you so much for watching! This video presents some details of MosaicML's Composer launch and how to use it in Python. I am really excited about this company and their mission to deliver faster and cheaper Deep Learning training! I hope you find this video useful, happy to answer any questions you might have about this or these ideas in Efficient Deep Learning generally! The full Weaviate podcast with Jonathan Frankle will be uploaded very soon on SeMI Technologies YouTube, please subscribe!

https://www.youtube.com/c/SeMI-and-Weaviate Chapters

0:00 Introduction

1:45 Documentation Intro

4:20 Composer Notebooks

5:35 Functional API…

4 месяца, 3 недели назад @ youtube.com
Jina AI DocArray - Documentation Overview
Jina AI DocArray - Documentation Overview Jina AI DocArray - Documentation Overview

I hope you found this useful, please let me know if you have any questions or ideas! Docarray Documentation: https://docarray.jina.ai/ Full-Length Podcast: https://www.youtube.com/watch?v=HIGAQAE_xaI Code Tutorial (Weaviate + Jina AI for Image Search): https://www.youtube.com/watch?v=rBKvoIGihnY Please check out Jina AI on YouTube: https://www.youtube.com/c/JinaAI Please check out SeMI Technologies on YouTube: https://www.youtube.com/c/SeMI-and-Weaviate/videos

5 месяцев назад @ youtube.com
What lead Jina AI CEO Han Xiao to Neural Search?
What lead Jina AI CEO Han Xiao to Neural Search? What lead Jina AI CEO Han Xiao to Neural Search?

This video explains one of the biggest lessons for me in interviewing Han Xiao from Jina AI. I hope this was a good explanation of the preprocessing / granularity of embeddings and how that can enable different kinds of search applications. Full-Length Podcast: https://www.youtube.com/watch?v=HIGAQAE_xaI Code Tutorial (Weaviate + Jina AI for Image Search): https://www.youtube.com/watch?v=rBKvoIGihnY Please check out Jina AI on YouTube: https://www.youtube.com/c/JinaAI Please check out SeMI Technologies on YouTube: https://www.youtube.com/c/SeMI-and-Weaviate/videos Chapters

0:00 Introduction

5 месяцев назад @ youtube.com
Full Stack Neural Search
Full Stack Neural Search Full Stack Neural Search

This video explains one of the biggest lessons for me in interviewing Han Xiao from Jina AI. I hope this was a good explanation of the preprocessing / granularity of embeddings and how that can enable different kinds of search applications. Full-Length Podcast: https://www.youtube.com/watch?v=HIGAQAE_xaI Code Tutorial (Weaviate + Jina AI for Image Search): https://www.youtube.com/watch?v=rBKvoIGihnY Please check out Jina AI on YouTube: https://www.youtube.com/c/JinaAI Please check out SeMI Technologies on YouTube: https://www.youtube.com/c/SeMI-and-Weaviate/videos Chapters

0:00 Please check out SeMI YouTube!

0:15 My takeaways on Full Stack Neural Search

11:04 Podcast Clip - Han Xiao

5 месяцев назад @ youtube.com
Python Tutorial: How to use Weaviate and Jina AI for Image Search!
Python Tutorial: How to use Weaviate and Jina AI for Image Search! Python Tutorial: How to use Weaviate and Jina AI for Image Search!

I hope this video helps you get started with Image Search using Weaviate and Jina AI - happy to answer any questions / help solve problems! Check out the full tutorial explanation from Laura Ham: https://www.youtube.com/watch?v=rBKvoIGihnY New podcast with Jina AI CEO Han Xiao! https://www.youtube.com/watch?v=HIGAQAE_xaI Full notebook code: https://github.com/laura-ham/HM-Fashion-image-neural-search/blob/main/hm-fashion-image-neural-search.ipynb Get started with the Weaviate Cloud Service: console.semi.technology

5 месяцев назад @ youtube.com
Causal Inference in Deep Learning (Podcast Overview with Brady Neal)
Causal Inference in Deep Learning (Podcast Overview with Brady Neal) Causal Inference in Deep Learning (Podcast Overview with Brady Neal)

Hey everyone! Hopefully this video helps supplement the new Weaviate podcast with Brady Neal, I hope you find this interesting / useful! Check out Brady Neal on YouTube! https://www.youtube.com/c/BradyNealCausalInference/featured Weaviate Podcast: https://www.youtube.com/watch?v=t7g9s1GWcB8 0:00 New Weaviate Podcast!

0:42 Brady Neal Causal Inference

1:34 Oogway.ai

2:45 Whiteboard Ideas

5:35 Discussion Topics

5 месяцев, 2 недели назад @ youtube.com
OpenAI Embeddings API - (Interview Recap and Background)
OpenAI Embeddings API - (Interview Recap and Background) OpenAI Embeddings API - (Interview Recap and Background)

Hey everyone! I recently interviewed Arvind Neelakantan from OpenAI about the new OpenAI Embeddings API on the Weaviate Podcast! This video provides some additional detail for the different topics that were discussed. If you find this video to be informative, please check out SeMI technologies on youtube where we are working hard on developing content explaining concepts in Deep Learning for Search. Full Podcast: https://www.youtube.com/watch?v=uFxfZ0vLsoU SeMI Technologies on YouTube: https://www.youtube.com/channel/UCJKT6kJ3IFYybWnL7jbXxhQ

6 месяцев назад @ youtube.com
AI Weekly Update - February 7th, 2022
AI Weekly Update - February 7th, 2022 AI Weekly Update - February 7th, 2022

Thanks for watching! Please subscribe for more Deep Learning and AI videos, the list of papers is below under "Content Links" Content Links:

Fully Online Meta-Learning without Task Boundaries: https://arxiv.org/abs/2202.00263

Datamodels: Predicting Predictions from Training Data: https://arxiv.org/abs/2202.00622

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization: https://arxiv.org/abs/2202.01334

Competition-Level Code Generation with AlphaCode: https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf

GPT-NeoX-20B: https://blog.eleuther.ai/announcing-20b/

PromptSource: https://arxiv.org/abs/2202.01279

Chain of Thou…

6 месяцев, 1 неделя назад @ youtube.com
Deep Learning for Podcast Content Search (Summary of Interview with Alex Canan at Zencastr)
Deep Learning for Podcast Content Search (Summary of Interview with Alex Canan at Zencastr) Deep Learning for Podcast Content Search (Summary of Interview with Alex Canan at Zencastr)

This video gives an overview of the latest Weaviate podcast, please subscribe to see future episodes!

https://www.youtube.com/c/SeMI-and-Weaviate/videos Thanks for watching! Chapters

0:00 Overview

7:53 Ideas for Podcast Search

10:44 Weaviate Podcast so far

6 месяцев, 2 недели назад @ youtube.com
AI Weekly Update - January 31st, 2022
AI Weekly Update - January 31st, 2022 AI Weekly Update - January 31st, 2022

Thank you so much for watching, please subscribe for more Deep Learning and Ai videos! Please check out SeMI Technologies on YouTube as well, where I am hosting a podcast on Deep Learning for Search! Paper Links:

Text and Code Embeddings by Contrastive Pre-Training: https://cdn.openai.com/papers/Text_and_Code_Embeddings_by_Contrastive_Pre_Training.pdf

Introducing Text and Code Embeddings in the OpenAI API (Blog Post): https://openai.com/blog/introducing-text-and-code-embeddings/

Nils Reimers - OpenAI GPT-3 Text Embeddings - Really a new state-of-the-art in dense text embeddings? https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddi…

6 месяцев, 2 недели назад @ youtube.com
AI Weekly Update - January 24th, 2022
AI Weekly Update - January 24th, 2022 AI Weekly Update - January 24th, 2022

Thank you so much for watching, please subscribe for more Deep Learning and AI videos! Please check out SeMI Technologies on YouTube as well! Paper Links:

CM3: https://arxiv.org/abs/2201.07520

data2vec: https://scontent.fmia1-1.fna.fbcdn.net/v/t39.8562-6/271974914_483120576492438_4239522333319653600_n.pdf?_nc_cat=107&ccb=1-5&_nc_sid=ae5e01&_nc_ohc=4-cMR5tUq4QAX-Of7fj&_nc_ht=scontent.fmia1-1.fna&oh=00_AT9ymN9dNPt1p8zWQClW6MSZikaCTT8gobc2LqxW4OhzZQ&oe=61F3F7D1

LaMDA: https://arxiv.org/abs/2201.08239

PromptBERT: https://arxiv.org/abs/2201.04337

UnifiedSKG: https://arxiv.org/abs/2201.05966

Collapse by Conditioning: https://arxiv.org/abs/2201.06578

GradTail: https://arxiv.org/abs/2201.05938

CLIP…

6 месяцев, 3 недели назад @ youtube.com
3blue1brown 3blue1brown
последний пост 1 месяц, 2 недели назад
How to lie using visual proofs
How to lie using visual proofs How to lie using visual proofs

Three false proofs, and what lessons they teach.

New notebooks: https://store.dftba.com/collections/3blue1brown/products/mathematical-quotebook-notebook

Help fund future projects: https://www.patreon.com/3blue1brown​

An equally valuable form of support is to simply share the videos. Time stamps:

0:00 - Fake sphere proof

1:39 - Fake pi = 4 proof

5:16 - Fake proof that all triangles are isosceles

9:54 - Sphere "proof" explanation

15:09 - pi = 4 "proof" explanation

16:57 - Triangle "proof" explanation and conclusion ------------------ These animations are largely made using a custom python library, manim. See the FAQ comments here:

https://www.3blue1brown.com/faq#manim

https://github.com/3b1b/…

1 месяц, 2 недели назад @ youtube.com
Summer of Math Exposition #2
Summer of Math Exposition #2 Summer of Math Exposition #2

Mailing-list: https://summerofmathexposition.substack.com/p/the-summer-of-math-exposition-is?s=r

Find collaborators here: https://github.com/leios/SoME_Topics/

Join the discord: https://discord.gg/dsp3zgB4qQ

Submission form: https://forms.gle/sNqosxqwCW2EjPVu5

Last year’s results: https://3b1b.co/blog/some1-results ------------------ Music by Vincent Rubinetti.

https://www.vincentrubinetti.com/ ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe: http://3b1b.co/subscribe Various social media stuffs:

Website: https://www.3blue1brown.com

Twitter: https://tw…

2 месяца, 1 неделя назад @ youtube.com
Olympiad level counting
Olympiad level counting Olympiad level counting

Generating functions, as applied to a hard puzzle used for IMO training.

Help fund future projects: https://www.patreon.com/3blue1brown​

An equally valuable form of support is to simply share the videos. Books mentioned 102 Combinatorial problems, by Titu Andreescu and Zuming Feng

https://amzn.to/3wAPoNq Generatingfunctionology by Herbert Wilf

https://amzn.to/3sPJ8Al Visualizing the Riemann zeta function

https://youtu.be/sD0NjbwqlYw Fourier series

https://youtu.be/r6sGWTCMz2k Timestamps

0:00 - Puzzle statement and motivation

4:31 - Simpler example

6:51 - The generating function

11:52 - Evaluation tricks

17:24 - Roots of unity

26:31 - Recap and final trick

30:13 - Takeaways -----------------…

2 месяца, 3 недели назад @ youtube.com
Oh, wait, actually the best Wordle opener is not “crane”…
Oh, wait, actually the best Wordle opener is not “crane”… Oh, wait, actually the best Wordle opener is not “crane”…

A slight correction to the previous video, with some more details about how the best first word was chosen.

Special thanks to these supporters: https://3b1b.co/lessons/wordle#thanks

Help fund future projects: https://www.patreon.com/3blue1brown​

An equally valuable form of support is to simply share the videos. Contents:

0:00 - The Bug

3:31 - How the best first guess is chosen

8:54 - Does this ruin the game? Nice post by Jonathan Olson on optimal wordle algorithms:

https://jonathanolson.net/experiments/optimal-wordle-solutions ------------------ These animations are largely made using a custom python library, manim. See the FAQ comments here:

https://www.3blue1brown.com/faq#manim

https://gi…

6 месяцев назад @ youtube.com
The mathematically optimal Wordle strategy
The mathematically optimal Wordle strategy The mathematically optimal Wordle strategy

An excuse to teach a lesson on information theory and entropy.

Help fund future projects: https://www.patreon.com/3blue1brown​

Special thanks to these supporters: https://3b1b.co/thanks

An equally valuable form of support is to simply share the videos. Contents:

0:00 - What is Wordle?

2:43 - Initial ideas

8:04 - Information theory basics

18:15 - Incorporating word frequencies

27:49 - Final performance Original wordle site:

https://www.powerlanguage.co.uk/wordle/ Music by Vincent Rubinetti.

https://www.vincentrubinetti.com/ Shannon and von Neumann artwork by Kurt Bruns. Code for this video:

https://github.com/3b1b/videos/blob/master/_2022/wordle.py These animations are largely made using a c…

6 месяцев, 1 неделя назад @ youtube.com
Alice, Bob, and the average shadow of a cube
Alice, Bob, and the average shadow of a cube Alice, Bob, and the average shadow of a cube

A tale of two problem solvers.

Numberphile video on Bertrand's paradox: https://youtu.be/mZBwsm6B280

Help fund future projects: https://www.patreon.com/3blue1brown​

Special thanks to these supporters: https://3b1b.co/lessons/newtons-fractal#thanks

An equally valuable form of support is to simply share the videos. The general result here was originally proved by Cauchy.

Mémoire sur la rectification des courbes et la quadrature des surfaces courbes par M. Augustin Cauchy

https://ia600208.us.archive.org/27/items/bub_gb_EomNI7m8__UC/bub_gb_EomNI7m8__UC.pdf ------------------- Timestamps

0:00 - The players

5:22 - How to start

9:12 - Alice's initial thoughts

13:37 - Piecing together the cube

22:1…

8 месяцев назад @ youtube.com
2021 Summer of Math Exposition results
2021 Summer of Math Exposition results 2021 Summer of Math Exposition results

Take a look at the full playlist (really): https://www.youtube.com/watch?v=fJWnA4j0_ho&list=PLnQX-jgAF5pTkwtUuVpqS5tuWmJ-6ZM-Z

Blog post with more details: https://3b1b.co/some1-results

Thanks, as always, to the supporters of this channel for helping to make this whole project possible: http://3b1b.co/thanks ------------------ Typo at 2:00, it should read "Burkard Polster" Videos and posts mentioned in this video. That weird light at the bottom of a mug — ENVELOPES

https://youtu.be/fJWnA4j0_ho Hiding Images in Plain Sight: The Physics Of Magic Windows

https://mattferraro.dev/posts/caustics-engineering The Beauty of Bézier Curves

https://youtu.be/aVwxzDHniEw What Is The Most Complicated Lock…

9 месяцев, 3 недели назад @ youtube.com
Two Minute Papers Two Minute Papers
последний пост 4 часа назад
Google’s New AI Learned To See In The Dark! 🤖
Google’s New AI Learned To See In The Dark! 🤖 Google’s New AI Learned To See In The Dark! 🤖

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers 📝 The paper "NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images" is available here:

https://bmild.github.io/rawnerf/index.html ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Ivo Galic, J…

4 часа назад @ youtube.com
Samsung’s AI: Megapixel DeepFakes! 📷
Samsung’s AI: Megapixel DeepFakes! 📷 Samsung’s AI: Megapixel DeepFakes! 📷

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "MegaPortraits: One-shot Megapixel Neural Head Avatars" is available here:

https://samsunglabs.github.io/MegaPortraits/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Ivo Galic, Jace O'Brien, Jack L…

4 дня, 2 часа назад @ youtube.com
OpenAI’s New AI Learned To Play Minecraft! ⛏
OpenAI’s New AI Learned To Play Minecraft! ⛏ OpenAI’s New AI Learned To Play Minecraft! ⛏

❤️ Come work for Weights & Biases! Check out open roles at https://wandb.me/jobs

❤️ Check out Weights & Biases and say hi in their community forum here: https://wandb.me/paperforum 📝 The paper "Learning to Play Minecraft with Video PreTraining (VPT)" is available here:

https://openai.com/blog/vpt/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahli…

1 неделя, 1 день назад @ youtube.com
OpenAI’s DALL-E 2: Top 5 New Results! 🤯
OpenAI’s DALL-E 2: Top 5 New Results! 🤯 OpenAI’s DALL-E 2: Top 5 New Results! 🤯

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Hierarchical Text-Conditional Image Generation with CLIP Latents" is available here:

https://openai.com/dall-e-2/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join Ladybug inpainting: https://www.reddit.com/r/dalle2/comments/veznq2/using_dalles_inpainting_feature_to_fix_up_my/ Michelangelo: https://twitter.com/FLKDayton/status/1543261364315193346 Mona Lisa: https://www.reddit.com/r/dalle2/comments/venhn1/modern_day_mona_lisa_composite_zoomout_video/ Mo…

1 неделя, 4 дня назад @ youtube.com
NVIDIA’s Ray Tracer - Finally, Real Time! ☀️
NVIDIA’s Ray Tracer - Finally, Real Time! ☀️ NVIDIA’s Ray Tracer - Finally, Real Time! ☀️

❤️ Check out Cohere and sign up for free today: https://cohere.ai/papers 📝 The paper "Rearchitecting Spatiotemporal Resampling for Production" is available here:

https://research.nvidia.com/publication/2021-07_Rearchitecting-Spatiotemporal-Resampling 📝 Our paper with the spheres scene that took 3 weeks is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/adaptive_metropolis/ The denoiser: https://developer.nvidia.com/nvidia-rt-denoiser ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters wh…

2 недели назад @ youtube.com
Google’s Parti AI: Magical Results! 💫
Google’s Parti AI: Magical Results! 💫 Google’s Parti AI: Magical Results! 💫

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Google Parti: Pathways Autoregressive Text-to-Image model" is available here:

https://parti.research.google/ 4 of my favorite prompts from the video (add these to benchmarks if you feel like it):

- surprised scholars looking at a magical parchment emitting magic dust high detail digital art disney style

- scholar delighted by a very long disintegrating magical parchment with sparks and smoke coming out of it fantasy digital art disney style

- stern looking fox in a labcoat, casting a magic spell, digital art

- shiny cybertronic robot frog with leds studio lighting high detail digital art 📝 T…

2 недели, 4 дня назад @ youtube.com
NVIDIA’s AI Plays Minecraft After 33 Years of Training! 🤖
NVIDIA’s AI Plays Minecraft After 33 Years of Training! 🤖 NVIDIA’s AI Plays Minecraft After 33 Years of Training! 🤖

❤️ If you wish to support us and watch these videos in early access, check this out:

- https://www.patreon.com/TwoMinutePapers 📝 The paper "MineDojo - Building Open-Ended Embodied Agents with Internet-Scale Knowledge" is available here:

https://minedojo.org/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Ivo Galic, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Michael T…

3 недели назад @ youtube.com
NVIDIA GTC: When Simulation Becomes Reality! 🤯
NVIDIA GTC: When Simulation Becomes Reality! 🤯 NVIDIA GTC: When Simulation Becomes Reality! 🤯

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers If everything goes well, this will be my GTC talk:

https://www.nvidia.com/gtc/session-catalog/?ncid=so-face-527732&tab.catalogallsessionstab=16566177511100015Kus#/session/16559245032830019Q6q ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Ger…

3 недели, 3 дня назад @ youtube.com
Finally, Robotic Telekinesis is Here! 🤖
Finally, Robotic Telekinesis is Here! 🤖 Finally, Robotic Telekinesis is Here! 🤖

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: http://wandb.me/robotic-telekinesis 📝 The paper "Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans on Youtube" is available here:

https://robotic-telekinesis.github.io/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn…

3 недели, 5 дней назад @ youtube.com
NVIDIA’s New AI Trained For 10 Years! But How? 🤺
NVIDIA’s New AI Trained For 10 Years! But How? 🤺 NVIDIA’s New AI Trained For 10 Years! But How? 🤺

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here (thank you Soumik!): http://wandb.me/ASE 📝 The paper "ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters" is available here:

https://nv-tlabs.github.io/ASE/ 📝 Our material synthesis paper with the latent space is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generou…

4 недели, 1 день назад @ youtube.com
OpenAI DALL-E 2 - Top 10 Best Images! 🤯
OpenAI DALL-E 2 - Top 10 Best Images! 🤯 OpenAI DALL-E 2 - Top 10 Best Images! 🤯

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Hierarchical Text-Conditional Image Generation with CLIP Latents" is available here:

https://openai.com/dall-e-2/ 🕊️ Follow us for more results on Twitter!

https://twitter.com/twominutepapers 🧑‍🎨 Check out Felícia Zsolnai-Fehér's works:

https://www.instagram.com/feliciart_86/ 🧑‍🎨 Judit Somogyvári's works:

https://www.artstation.com/sheyenne

https://www.instagram.com/somogyvari.art/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to th…

1 месяц назад @ youtube.com
Watch This Dragon Grow Out Of Nothing! 🐲
Watch This Dragon Grow Out Of Nothing! 🐲 Watch This Dragon Grow Out Of Nothing! 🐲

❤️ Check out Cohere and sign up for free today: https://cohere.ai/papers 📝 The paper "Differentiable Signed Distance Function Rendering" is available here:

http://rgl.epfl.ch/publications/Vicini2022SDF 📝 Our works on differentiable material synthesis and neural rendering are available here (with code): https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/

https://users.cg.tuwien.ac.at/zsolnai/gfx/photorealistic-material-editing/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join Thank you Gordon Hanzmann-Johnson for catching an issue w…

1 месяц, 1 неделя назад @ youtube.com
NVIDIA’s AI Nailed Human Face Synthesis! 👩‍🎓
NVIDIA’s AI Nailed Human Face Synthesis! 👩‍🎓 NVIDIA’s AI Nailed Human Face Synthesis! 👩‍🎓

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here (Thank you Soumik!): http://wandb.me/styleGAN-NADA 📝 The paper "StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators" is available here:

https://stylegan-nada.github.io/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, …

1 месяц, 1 неделя назад @ youtube.com
Google AI Simulates Evolution On A Computer! 🦖
Google AI Simulates Evolution On A Computer! 🦖 Google AI Simulates Evolution On A Computer! 🦖

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here (Thank you Soumik Rakshit!): https://wandb.me/modern-evolution 📝 The paper "Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts" is available here:

https://es-clip.github.io/ 🧑‍🎨 My previous genetic algorithm implementation for the Mona Lisa problem (+ some explanation in the video below):

https://users.cg.tuwien.ac.at/zsolnai/gfx/mona_lisa_parallel_genetic_algorithm/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/chann…

1 месяц, 3 недели назад @ youtube.com
NVIDIA’s Ray Tracer: Wow, They Nailed It Again! 🤯
NVIDIA’s Ray Tracer: Wow, They Nailed It Again! 🤯 NVIDIA’s Ray Tracer: Wow, They Nailed It Again! 🤯

❤️ Train a neural network and track your experiments with Weights & Biases here: http://wandb.me/paperintro 📝 NVIDIA's paper "Fast Volume Rendering with Spatiotemporal Reservoir Resampling" is available here:

https://dqlin.xyz/pubs/2021-sa-VOR/

https://graphics.cs.utah.edu/research/projects/volumetric-restir/

https://research.nvidia.com/publication/2021-11_Fast-Volume-Rendering 🔆 The free light transport course is available here. You'll love it! https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/ Volumetric path tracer by michael0884: https://www.shadertoy.com/view/NtXSR4 ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/…

1 месяц, 3 недели назад @ youtube.com
DataFest Video DataFest Video
последний пост None
Семинары JetBrains Research Семинары JetBrains Research
последний пост 2 месяца, 2 недели назад
Learning to Recommend Method Names with Global Context
Learning to Recommend Method Names with Global Context Learning to Recommend Method Names with Global Context

Во многих задачах исследователи работают с небольшими фрагментами кода — отдельными методами, реже — с файлами. Но чтобы найти качественное решение, зачастую требуется выйти за пределы небольших кусков кода и использовать глобальную информацию о модуле или проекте. Мы поговорим о различных способах использования информации о контексте в ML моделях и о том, на что нужно обращать внимание для честной оценки их качества. Докладчик: Егор Богомолов Материалы: https://arxiv.org/pdf/2201.10705.pdf

2 месяца, 2 недели назад @ youtube.com
Генерация SQL запросов по тексту на естественном языке
Генерация SQL запросов по тексту на естественном языке Генерация SQL запросов по тексту на естественном языке

Мы разберем методы генерации SQL запросов из описания на естественном языке и немного поговорим о более широком применении их к генерации DSL кода. Мы обсудим почему обучение обучение моделей для DSL может отличаться от моделей генерации кода, текущие подходы к решению задачи на базе лидерборда для Spider датасета и их ограничения. Мы представим более масштабируемый подход к генерации SQL и наши текущие результаты. Докладчик: Денис Литвинов

2 месяца, 3 недели назад @ youtube.com
Automating Reinforcement Learning Architecture Design for Code Optimization
Automating Reinforcement Learning Architecture Design for Code Optimization Automating Reinforcement Learning Architecture Design for Code Optimization

В настоящее время Reinforcement Learning (RL) применяется для решения ряда задач оптимизации в области компиляторов, таких как конфигурация флагов компиляции, выбор оптимального порядка выполнения инструкций и многие другие. Однако, подобрать оптимальный RL-алгоритм бывает сложно, так как он зависит от контекста конкретной задачи. Более того, разработчики компиляторов зачастую могут быть не вовлечены в область RL, что еще сильнее осложняет решение данной задачи. В работе Automating Reinforcement Learning Architecture Design for Code Optimization авторы предлагают инструмент Supersonic, позволяющий автоматически подбирать оптимальный RL-алгоритм для решения оптимизационных задач в компилятор…

3 месяца назад @ youtube.com
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Несмотря на то, что многие из последних достижений в области машинного обучения связаны с глубоким обучением с подкреплением, Deep RL алгоритмы остаются ненадёжными (по сравнению с классическими моделями глубокого обучения) и трудновоспроизводимыми (с точки зрения результата). Авторы статьи связывают описанные недостатки с проблемой отсутствия понимания того как внутренние механизмы, используемые в RL алгоритмах, влияют на поведение агента по отдельности и вместе взятые. На семинаре мы поговорим о поднятой авторами проблеме на примере алгоритмов Trust Region Policy Optimization (TRPO) и Proximal Policy Optimization (PPO), рассмотрим эксперименты по оценке влияния составных частей этих алгор…

3 месяца, 3 недели назад @ youtube.com
Predicting What You Already Know Helps: Provable Self-Supervised Learning
Predicting What You Already Know Helps: Provable Self-Supervised Learning Predicting What You Already Know Helps: Provable Self-Supervised Learning

Зачастую в прикладных задачах собрать достаточно большой, подходящим образом размеченный датасет для обучения модели не представляется возможным. Популярным решением в такой ситуации является Self-Supervised Learning. В рамках этого подхода модель сначала предобучают на синтетической, искусственно выдуманной задаче, выборку для которой автоматически формируют из неразмеченных данных. Примерами таких синтетических задач являются восстановление маскированных токенов в NLP (этот же подход используется и в некоторых моделях для работы с кодом), восстановление фрагментов или удаление искусственного шума при работе с картинками, восстановление последовательности кадров при работе с видео и т.д.. …

3 месяца, 4 недели назад @ youtube.com
Emerging Properties in Self-Supervised Vision Transforms
Emerging Properties in Self-Supervised Vision Transforms Emerging Properties in Self-Supervised Vision Transforms

Многие из самых захватывающих новых прорывов в области искусственного интеллекта произошли благодаря двум недавним инновациям: самоконтролируемое обучение, который позволяет машинам учиться на случайных немаркированных примерах, а также Трансформеры, которые позволяют моделям ИИ выборочно сосредотачиваться на определенных частях своего ввода и, таким образом, рассуждать более эффективно. На семинара будет разобрана новая статья "Emerging Properties in Self-Supervised Vision Transforms", в которой авторы используются ранее упомянутые техники для решения задач компьютерного зрения. Докладчик: Ольга Лавриченко.

4 месяца назад @ youtube.com
Multimodal Conditional Image Synthesis with Product-of-Experts GANs
Multimodal Conditional Image Synthesis with Product-of-Experts GANs Multimodal Conditional Image Synthesis with Product-of-Experts GANs

Существующие фреймворки для генерации изображений могут обуславливаться на пользовательский ввод в одной модальности — например, на текст, эскиз, маску сегментации или пример изображения со стилем. При этом, такие подходы не используют доступные мультимодальные данные. Авторы данной статьи предлагают Product-of-Experts Generative Adversarial Networks (PoE-GAN) фреймворк, который позволяет синтезировать изображение на основе условий в нескольких модальностях или любом их подмножестве, а также осуществлять безусловную генерацию. Данная модель также превосходит другие подходы в условиях унимодальной условной генерации. Докладчик: Дарья Евсикова.

4 месяца назад @ youtube.com
Block-Recurrent Transformers
Block-Recurrent Transformers Block-Recurrent Transformers

Трансформеры уже давно господствуют во многих задачах NLP. И если с задачами где длина последовательности относительно мала (не более 512 токенов) проблем не возникает, то с обработкой больших текстов не все так ясно. Проблема в том, что потребление памяти увеличивается квадратично с ростом обрабатываемой последовательности. Существуют различные подходы к решению проблемы, например, можно линеаризовать softmax в модуле внимания, снизив асимптотику до O(N) (linear transformers); или же исследовать разреженность (BigBird). В свою очередь, авторы статьи продолжают идеи sliding-window и Transformer-XL. Поэтому на семинаре поговорим об этих подходах и архитектуре Block-Recurrent Transformer. Док…

4 месяца назад @ youtube.com
Assessing Project-Level Fine-Tuning of ML4SE Models
Assessing Project-Level Fine-Tuning of ML4SE Models Assessing Project-Level Fine-Tuning of ML4SE Models

Мы расскажем про исследование, посвященное дообучению ML4SE моделей под конкретный проект. В то время как большинство исследователей обучает и тестирует модели на непересекающихся наборах проектов, мы задались вопросом: “А что будет, если показать модели данные из целевого проекта?“. Мы поговорим об особенностях оценки качества проектно-дообученных моделей и презентуем полученные результаты для трех моделей в задаче предсказания имен методов.

Докладчик – Егор Богомолов

4 месяца, 1 неделя назад @ youtube.com
Предсказание типов для исходного кода с использованием графовых нейронных сетей
Предсказание типов для исходного кода с использованием графовых нейронных сетей Предсказание типов для исходного кода с использованием графовых нейронных сетей

На семинаре мы поговорим о нашей работе в области предварительной тренировки векторных представлений графовых нейронных сетей (GNN) для исходного кода. Качество векторов мы оцениваем с помощью задачи предсказания типов для языка с динамической типизацией Python. Для предварительной тренировки используется задача предсказания имён. По результатам наших экспериментов векторные представления GNN позволяют достичь точности классификации типов, сравнимой с CodeBERT. Вдобавок, объединение CodeBERT и GNN векторов в гибридную модель позволяет улучшить точность классификации типов. При этом, улучшения достигаются даже после тренировки GNN модели в течение всего одной эпохи, что намного меньше чем тр…

4 месяца, 1 неделя назад @ youtube.com
Industry-scale IR-based Bug Localization: A Perspective from Facebook
Industry-scale IR-based Bug Localization: A Perspective from Facebook Industry-scale IR-based Bug Localization: A Perspective from Facebook

В крупных компаниях, где весь код лежит в едином репозитории, очень важно уметь оперативно локализовать баг. Задача усложняется, когда отельные файлы состоят из сотен строк, а проблема выявляется на этапе End-to-End тестирования или в продакшене. В такой ситуации необходимо автоматическое решение, которое способно быстро найти ломающий коммит, несмотря на то, что сообщения об ошибке зачастую трудночитаемые и содержат большой объём информации. На этом семинаре мы разберём статью от Facebook (https://arxiv.org/pdf/2010.09977.pdf), в которой авторы предлагают эффективный unsupervised алгоритм локализации бага к коммиту, использующий методы информационного поиска. Описанный алгоритм приспособле…

4 месяца, 1 неделя назад @ youtube.com
Code Smells for Machine Learning Applications
Code Smells for Machine Learning Applications Code Smells for Machine Learning Applications

Разработка программного обеспечения сопряжена с поиском и исправлением ошибок. В программной инженерии уже давно изучаются и описываются запахи кода – формальные признаки, индицирующие о возможном наличии проблем. Примерами запахов кода могут быть завистливая функция (метод обращается к данным чужого класса чаще, чем к данным собственного) или параллельная иерархия (ситуация, когда при создании нового класса в одной иерархии классов вам почти всегда приходится создавать парный к нему класс в другой иерархии). Для каждого запаха кода описаны потенциальные пути исправления, часто сводящиеся к какому-то рефакторингу.

Однако, проекты, связанные с машинным обучением, обладают особой спецификой и…

4 месяца, 1 неделя назад @ youtube.com
Fastformer: Additive Attention Can Be All You Need
Fastformer: Additive Attention Can Be All You Need Fastformer: Additive Attention Can Be All You Need

Трансформер - очень хорошая модель для понимания текста, однако она не эффективна из-за квадратичной асимптотической сложности по длине входящей последовательности. Хотя существует множество методов ускорения трансформера, они все еще недостаточно эффективны на длинных последовательностях. Авторы статьи предлагают Fastformer, эффективную модель трансформера, основанную на аддитивном внимании (additive attention). На семинаре мы вспомним, как работают трансформеры, познакомимся с additive attention и Fastformer и посмотрим, как он справляется с различными задачами. Докладчик: Тимур Хабибуллин

4 месяца, 1 неделя назад @ youtube.com
Language Models are Unsupervised Multitask Learners
Language Models are Unsupervised Multitask Learners Language Models are Unsupervised Multitask Learners

Задачи обработки естественного языка, такие как машинный перевод, ответы на вопросы и обобщения текстов, как правило решаются с помощью обучения с учителем на специально подобранных под конкретное задание датасетах. Авторы статьи показывают, что можно обучить модель, которая будет способна решать различные задачи с минимальным количеством обучения с учителем, используя для этого датасет Webtext, состоящий из миллионов различных веб-страниц. На семинаре мы обсудим, как модель справляется с заданиями различной специфики и сравним результаты авторов с результатами state-of-the art моделей. Докладчики: Маргарита Чудова

4 месяца, 1 неделя назад @ youtube.com
Neural Code Completion: Research & Practice
Neural Code Completion: Research & Practice Neural Code Completion: Research & Practice

Я расскажу про процесс создания командой AI Team системы автодополнения для языка R. Будет рассказано о том, с какими трудностями можно столкнуться при разработке и внедрении системы автодополнения, основанной на нейросетях. Также мы рассмотрим некоторые нерешённые исследовательские проблемы в области нейросетевого автодополнения и обсудим возможные способы их решения. Большая часть рассказа будет основана на статье Time-Efficient Code Completion Model for the R Programming Language (https://aclanthology.org/2021.nlp4prog-1.4/), опубликованной на воркшопе NLP4prog 2021 (https://nlp4prog.github.io/2021/) конференции ACL. Докладчики: Артем Попов.

5 месяцев, 3 недели назад @ youtube.com
Яндекс. Компьютерные науки Яндекс. Компьютерные науки
последний пост 3 месяца, 3 недели назад
Задачи RMQ и LCA. Часть 2
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Дерево отрезков. Задача RSQ (range sum query). Задачи LCA (least common ancestor) и RMQ (range minimum query). Решение RMQ с помощью sparse table. Сведение LCA к RMQ (алгоритм Фарах-Колтона-Бендера). Сведение RMQ к LCA. Задача LA (level ancestors). Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

3 месяца, 3 недели назад @ youtube.com
Задача о кратчайших путях. Алгоритмы Беллмана-Форда, Флойда, Дийкстры и Джонсона
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Кратчайшие пути в графах. Оценки расстояний и их релаксация. Алгоритмы Беллмана-Форда, Флойда и Дийкстры. Потенциалы. Критерий консервативности длин в терминах наличия допустимых потенциалов. Нахождение допустимых потенциалов с помощью алгоритма Беллмана-Форда. Алгоритм Джонсона. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

3 месяца, 3 недели назад @ youtube.com
Минимальные остовные деревья. Алгоритмы Краскала и Прима. Системы непересекающихся множеств.
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Остовы минимального веса. Лемма о минимальном ребре в разрезе. Алгоритмы Краскала и Прима. Структура DSU (disjoint set union) Реализация с использованием леса. Ранги вершин, эвристика ранга. Логарифмическая оценка ранга через количество элементов. Эвристика сжатия путей. Оценка учетной стоимости операций (без доказательства). Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

3 месяца, 3 недели назад @ youtube.com
Splay-деревья. Обход в ширину. Обход в глубину. Топологическая сортировка и проверка ацикличности.
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Splay-деревья. Операция splay: zig, zig-zig и zig-zag шаги. Реализация операций вставки, удаления, слияния и разделения для splay-деревьев. Обход в глубину. Топологическая сортировка Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

3 месяца, 3 недели назад @ youtube.com
Хеширование
Хеширование Хеширование

Хеш-функции. Коллизии. Разрешение коллизий методом цепочек. Гипотеза простого равномерного хеширования, оценка средней длины цепочки хеш-функции. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

3 месяца, 3 недели назад @ youtube.com
Сортировка слиянием. Быстрая сортировка.
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Модель разрешающих деревьев. Нижняя оценка на число сравнений при сортировке и поиске в модели разрешающих деревьев.

Сортировка слиянием (Merge-Sort). Top-down и bottom-up подходы. Сортировка слиянием во внешней памяти. Inplace Merge-Sort. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

3 месяца, 3 недели назад @ youtube.com
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3 месяца, 3 недели назад @ youtube.com
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3 месяца, 3 недели назад @ youtube.com
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3 месяца, 3 недели назад @ youtube.com
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3 месяца, 3 недели назад @ youtube.com
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3 месяца, 3 недели назад @ youtube.com
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Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

2 недели, 6 дней назад @ youtube.com
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Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

3 недели назад @ youtube.com
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Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

3 недели, 1 день назад @ youtube.com
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Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

3 недели, 2 дня назад @ youtube.com
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Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

3 недели, 5 дней назад @ youtube.com
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- 18:25 - главные причины краха

- 19:53 - рецепты успеха

- 22:24 - практика Цельса

- 25:16 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

3 недели, 5 дней назад @ youtube.com
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- 3:07 - uncertainty sampling

- 5:38 - diversity sampling

- 8:44 - random sampling

- 9:42 - случаи из практики

- 17:42 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

3 недели, 6 дней назад @ youtube.com
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- 4:42 - как искать врачей-разметчиков?

- 6:15 - входной контроль

- 9:30 - выход на промышленные масштабы

- 18:32 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

3 недели, 6 дней назад @ youtube.com
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- 3:06 - этап постановки задачи

- 7:30 - этап очистки данных

- 15:13 - этап разметки данных

- 16:15 - заключение и рекомендации

- 17:32 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

4 недели назад @ youtube.com
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Медицинские данные - это боль. Но только преодолевая боль, мы становимся сильнее. Этот доклад - обзор ключевых компонентов процесса обеспечения качества медицинских данных. Таймкоды:

- 1:02 - почему медицинские данные - это боль

- 12:12 - что такое качество данных?

- 16:32 - путь к качеству медицинских данных

- 26:34 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

4 недели, 1 день назад @ youtube.com
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- 1:44 - история данных на проекте Маммография

- 8:59 - дата-платформа (хранение данных, ETL, дата-продукты)

- 24:57 - что дальше?

- 26:45 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

4 недели, 1 день назад @ youtube.com
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Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Twitter: https://twitter.com/NewsOds

1 месяц, 1 неделя назад @ youtube.com
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Ссылка на доклад: https://youtu.be/mt-KIFEEhyk

Спикер: Дмитрий Колодезев Полезные ссылки

Мероприятие Data Fest Online 3.0: https://ods.ai/events/datafestonline2022

Трек Reliable ML: https://ods.ai/tracks/reliable-ml-df2022

Telegram канал Reliable ML: https://t.me/reliable_ml Наши соц.сети

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Twitter: https://twitter.com/NewsOds

1 месяц, 2 недели назад @ youtube.com
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Ссылка на доклад: https://youtu.be/mt-KIFEEhyk

Спикер: Наталья Тоганова Полезные ссылки

Мероприятие Data Fest Online 3.0: https://ods.ai/events/datafestonline2022

Трек Reliable ML: https://ods.ai/tracks/reliable-ml-df2022

Telegram канал Reliable ML: https://t.me/reliable_ml Наши соц.сети

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Twitter: https://twitter.com/NewsOds

1 месяц, 2 недели назад @ youtube.com
Kotenkov Igor | Nearest Neighbors Language Models (part 1) | kNN-LM model
Kotenkov Igor | Nearest Neighbors Language Models (part 1) | kNN-LM model Kotenkov Igor | Nearest Neighbors Language Models (part 1) | kNN-LM model

00:00 - 2:32 Вступление

2:32 - 9:29 Краткая история трансформеров

9:29 - 11:48 Тренд на доступные языковые модели

11:48 - 15:37 Что внутри модели?

15:37 - 17:49 Language Models 101

17:49 - 20:57 KNN and ANN 101

20:57 - 24:29 Datastore and hidden states

24:29 - 30:09 kNN-LM pipeline

30:09 - 34:05 Перплексия и датасет

34:05 - 44:35 Метрики и выводы из них Ссылка на презентацию: https://1drv.ms/p/s!AlnN0aqNwShslQucZAy9R5VeGRRt?e=Gc3dgp

1 месяц, 2 недели назад @ youtube.com
Primer Primer
последний пост 1 день, 1 час назад
How many people might ever exist, calculated
How many people might ever exist, calculated How many people might ever exist, calculated

You can get 50% off What We Owe The Future and drive sales to local independent bookstores by using the promotion code PRIMER50 when buying from the following website: https://bookshop.org/books/what-we-owe-the-future/9781541618626 I made this video in partnership with the Forethought Foundation for Global Priorities Research, where the author Will MacAskill serves as director. Their goal is to help the book reach more people, and I’m very aligned with that goal. The more we can work together to think about our future, the better! Source links:

https://ourworldindata.org/longtermism

https://www.prb.org/articles/how-many-people-have-ever-lived-on-earth/

https://ourworldindata.org/world-popul…

1 день, 1 час назад @ youtube.com
How To Catch A Cheater With Math
How To Catch A Cheater With Math How To Catch A Cheater With Math

Try catching cheaters yourself: https://primerlearning.org/ Support these videos on Patreon: https://www.patreon.com/primerlearning

Plush blobs and other stuff: https://store.dftba.com/collections/primer Binomial probability example (the whole section on Khan Academy may be helpful)

https://www.khanacademy.org/math/statistics-probability/random-variables-stats-library/binomial-random-variables/v/probability-of-making-2-shots-in-6-attempts For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Twitter: @primerlearning

- Reddit: r/primerlearning Made with Unity and Manim

https://github.com/Helpsypoo/PrimerUnity

https://www.manim.community/ Made possible by support through Patreon:…

1 месяц, 3 недели назад @ youtube.com
Can you catch the cheaters?
Can you catch the cheaters? Can you catch the cheaters?

Play at primerlearning.org

Or on Google Play: https://play.google.com/store/apps/details?id=com.Primer.CatchtheCheaters For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Twitter: @primerlearning

- Reddit: r/primerlearning Plush blobs and other merch: https://store.dftba.com/collections/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning Made with Unity

https://github.com/Helpsypoo/PrimerUnity Made possible by support through Patreon:

Anthony Eufemio

Jon Mundle

Spline

Zachariah Richard Fournier

Vladimir Duchenchuk

Roy & BreAnna Steves

Shayn Osborn

Jeremy

Guguke

Anders Fjeldvær

Luc Cedric R.

Erik Broeders

Kairui Wang

Sean Barker

Eric Helps

Stevie Hr…

4 месяца, 1 неделя назад @ youtube.com
🎧 Podcasts
Lex Fridman AI Podcast Lex Fridman AI Podcast
последний пост 1 день, 1 час назад
#312 – Duncan Trussell: Comedy, Sentient Robots, Suffering, Love & Burning Man
#312 – Duncan Trussell: Comedy, Sentient Robots, Suffering, Love & Burning Man #312 – Duncan Trussell: Comedy, Sentient Robots, Suffering, Love & Burning Man

Duncan Trussell is a comedian, host of The Duncan Trussell Family Hour podcast, and co-creator of The Midnight Gospel.

Please support this podcast by checking out our sponsors:– Skiff: https://skiff.com/lex– Calm: https://calm.com/lex to get 40% off premium– SimpliSafe: https://simplisafe.com/lex– NetSuite: http://netsuite.com/lex to get free product tour– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Duncan’s Twitter: https://twitter.com/duncantrussellDuncan’s Instagram: https://instagram.com/duncantrussellThe Duncan Trussell Family Hour: https://duncantrussell.comThe Midnight Gospel: https://netflix.com/themidnightgospelBooks mentioned:Superintelligence: https://amzn.to/3Q…

1 день, 1 час назад @ lexfridman.com
#311 – Magatte Wade: Africa, Capitalism, Communism, and the Future of Humanity
#311 – Magatte Wade: Africa, Capitalism, Communism, and the Future of Humanity #311 – Magatte Wade: Africa, Capitalism, Communism, and the Future of Humanity

Magatte Wade is an entrepreneur with a passion for creating positive change in Africa through economic freedom.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– Audible: https://audible.com/lex to get 30-day free trial– InsideTracker: https://insidetracker.com/lex to get 20% off– Onnit: https://lexfridman.com/onnit to get up to 10% offEPISODE LINKS:Magatte’s Twitter: https://twitter.com/magattewMagatte’s Instagram: https://instagram.com/magattewMagatte’s Facebook: https://facebook.com/themagattewadeMagatte’s Website: https://magattewade.comWebsites mentioned:Austin housing project: https://texansforreasonables…

4 дня, 1 час назад @ lexfridman.com
#310 – Andrew Bustamante: CIA Spy
#310 – Andrew Bustamante: CIA Spy #310 – Andrew Bustamante: CIA Spy

Andrew Bustamante is a former CIA covert intelligence officer.

Check out his work and podcast at https://everydayspy.com Please support this podcast by checking out our sponsors:– Wealthfront: https://wealthfront.com/LEX to get $50 sign-up bonus– LMNT: https://drinkLMNT.com/lex to get free sample pack– BetterHelp: https://betterhelp.com/lex to get 10% off– ExpressVPN: https://expressvpn.com/lexpod to get 3 months free– MasterClass: https://masterclass.com/lex to get 15% offEPISODE LINKS:Everyday Spy: https://everydayspy.com/quizEveryday Spy Podcast: https://everydayspy.com/podcastAndrew’s Twitter: https://twitter.com/everydayspyAndrew’s Instagram: https://instagram.com/everydayspyPODCAST IN…

1 неделя, 2 дня назад @ lexfridman.com
#309 – John Carmack: Doom, Quake, VR, AGI, Programming, Video Games, and Rockets
#309 – John Carmack: Doom, Quake, VR, AGI, Programming, Video Games, and Rockets #309 – John Carmack: Doom, Quake, VR, AGI, Programming, Video Games, and Rockets

John Carmack is a legendary programmer, co-founder of id Software, and lead programmer of many revolutionary video games including Wolfenstein 3D, Doom, Quake, and the Commander Keen series.

He is also the founder of Armadillo Aerospace, and for many years the CTO of Oculus VR.

Please support this podcast by checking out our sponsors:– InsideTracker: https://insidetracker.com/lex to get 20% off– Indeed: https://indeed.com/lex to get $75 credit– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish o…

1 неделя, 6 дней назад @ lexfridman.com
#308 – Ryan Graves: UFOs, Fighter Jets, and Aliens
#308 – Ryan Graves: UFOs, Fighter Jets, and Aliens #308 – Ryan Graves: UFOs, Fighter Jets, and Aliens

Lt. Ryan Graves is a former Navy fighter pilot, who has worked on advanced research and development programs for DARPA, Office of Naval Research, and Air Force Research Labs on topics of multi-agent collaborative autonomy, AI-assisted air-to-air combat, and manned-unmanned teaming technologies.

Ryan and people in his squadron detected and engaged with UFOs on multiple occasions, and he has been one of the few people willing to speak publicly about these experiences.

Please support this podcast by checking out our sponsors:– GiveWell: https://www.givewell.org/ and use code LEX– Notion: https://notion.com/startups to get up to $1000 off team plan– Magic Spoon: https://magicspoon.com/lex and u…

2 недели, 2 дня назад @ lexfridman.com
#307 – Brian Armstrong: Coinbase, Cryptocurrency, and Government Regulation
#307 – Brian Armstrong: Coinbase, Cryptocurrency, and Government Regulation #307 – Brian Armstrong: Coinbase, Cryptocurrency, and Government Regulation

Brian Armstrong is the CEO of Coinbase.

Please support this podcast by checking out our sponsors:– Audible: https://audible.com/lex– Skiff: https://skiff.org/lex– BiOptimizers: http://www.magbreakthrough.com/lex to get 10% off– Fundrise: https://fundrise.com/lex– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oilEPISODE LINKS:Brian’s Twitter: https://twitter.com/brian_armstrongBrian’s Instagram: https://www.instagram.com/brian_armstrongCoinbase’s Website: https://www.coinbase.comResearchHub: https://www.researchhub.comNewLimit: https://www.newlimit.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZI…

2 недели, 5 дней назад @ lexfridman.com
#306 – Oriol Vinyals: Deep Learning and Artificial General Intelligence
#306 – Oriol Vinyals: Deep Learning and Artificial General Intelligence #306 – Oriol Vinyals: Deep Learning and Artificial General Intelligence

Oriol Vinyals is the Research Director and Deep Learning Lead at DeepMind.

Language Models are Few-Shot Learners: https://arxiv.org/abs/2005.141654.

Emergent Abilities of Large Language Models: https://arxiv.org/abs/2206.076825.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(05:18) – AI(20:14) – Weights(26:33) – Gato(1:01:22) – Meta learning(1:15:21) – Neural networks(1:37:46) – Emergence(1:44:30) – AI sentience(2:08:27) – AGI

3 недели, 1 день назад @ lexfridman.com
#305 – Martin Rees: Black Holes, Alien Life, Dark Matter, and the Big Bang
#305 – Martin Rees: Black Holes, Alien Life, Dark Matter, and the Big Bang #305 – Martin Rees: Black Holes, Alien Life, Dark Matter, and the Big Bang

Lord Martin Rees is a cosmologist and astrophysicist at Cambridge University and co-founder of the Centre for the Study of Existential Risk.

Please support this podcast by checking out our sponsors:– Lambda: https://lambdalabs.com/lex– InsideTracker: https://insidetracker.com/lex to get 20% off– Indeed: https://indeed.com/lex to get $75 credit– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Onnit: https://lexfridman.com/onnit to get up to 10% offEPISODE LINKS:Martin’s Twitter: https://twitter.com/lordmartinreesMartin’s Website: https://www.martinrees.ukMartin’s Books:If Science is to Save Us: https://amzn.to/3yXRqscThe End of Astronauts: https://amzn.to/…

3 недели, 4 дня назад @ lexfridman.com
#304 – Bishop Robert Barron: Christianity and the Catholic Church
#304 – Bishop Robert Barron: Christianity and the Catholic Church #304 – Bishop Robert Barron: Christianity and the Catholic Church

Robert Barron is a bishop and founder of Word on Fire Catholic Ministries.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– BetterHelp: https://betterhelp.com/lex to get 10% off– Notion: https://notion.com/startups to get up to $1000 off team plan– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savingsEPISODE LINKS:Robert’s Instagram: https://instagram.com/bishopbarronRobert’s Twitter: https://twitter.com/BishopBarronWord on Fire’s Instagram: https://instagram.com/wordonfire_catholicministriesWord on Fire’s…

4 недели назад @ lexfridman.com
#303 – Steve Keen: Marxism, Capitalism, and Economics
#303 – Steve Keen: Marxism, Capitalism, and Economics #303 – Steve Keen: Marxism, Capitalism, and Economics

Steve Keen is a heterodox economist and author.

Please support this podcast by checking out our sponsors:– Weights & Biases: https://lexfridman.com/wnb– Skiff: https://skiff.org/lex– Indeed: https://indeed.com/lex to get $75 credit– NetSuite: http://netsuite.com/lex to get free product tour– InsideTracker: https://insidetracker.com/lex to get 20% offEPISODE LINKS:Steve’s Twitter: https://twitter.com/profstevekeenThe New Economics (book): https://amzn.to/3zb4eg4PODCAST 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…

1 месяц назад @ lexfridman.com
#302 – Richard Haier: IQ Tests, Human Intelligence, and Group Differences
#302 – Richard Haier: IQ Tests, Human Intelligence, and Group Differences #302 – Richard Haier: IQ Tests, Human Intelligence, and Group Differences

Richard Haier is a psychologist specializing in the science of human intelligence.

Child IQ and survival to 79: https://ncbi.nlm.nih.gov/pmc/articles/PMC5491698/2.

The Bell Curve: https://amzn.to/3Ng4RJe6.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(08:06) – Measuring human intelligence(22:34) – IQ tests(45:23) – College entrance exams(53:59) – Genetics(59:58) – Enhancing intelligence(1:07:27) – The Bell Curve(1:19:58) – Race differences(1:39:11) – Bell curve criticisms(1:48:21) – Intelligence and life success(1:57:57) – Flynn effect(2:02:49) – Nature vs nuture(2:29:42) – Testing artificial intelligence(2:41:46) – Advice(2:4…

1 месяц назад @ lexfridman.com
#301 – Jack Barsky: KGB Spy
#301 – Jack Barsky: KGB Spy #301 – Jack Barsky: KGB Spy

Jack Barsky is a former KGB spy and author of “Deep Undercover: My Secret Life and Tangled Allegiances as a KGB Spy in America”.

Please support this podcast by checking out our sponsors:– InsideTracker: https://insidetracker.com/lex to get 20% off– Notion: https://notion.com/startups to get up to $1000 off team plan– BetterHelp: https://betterhelp.com/lex to get 10% off– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oilEPISODE LINKS:Jack’s Twitter: https://twitter.com/deepcoverbarskyJack’s Website: https://jackbarsky.comDeep Undercover (book): https://amzn.to/39XMTgGThe Agen…

1 месяц, 1 неделя назад @ lexfridman.com
#300 – Joe Rogan: Comedy, Controversy, Aliens, UFOs, Putin, CIA, and Freedom
#300 – Joe Rogan: Comedy, Controversy, Aliens, UFOs, Putin, CIA, and Freedom #300 – Joe Rogan: Comedy, Controversy, Aliens, UFOs, Putin, CIA, and Freedom

Joe Rogan is a comedian, UFC commentator, and host of the Joe Rogan Experience.

Please support this podcast by checking out our sponsors:– Theragun: https://therabody.com/lex– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– InsideTracker: https://insidetracker.com/lex to get 20% off– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months freeEPISODE LINKS:Joe’s Instagram: https://instagram.com/joeroganJoe’s Twitter: https://twitter.com/joeroganJRE (Spotify): https://open.spotify.com/show/4rOoJ6Egrf8K2IrywzwOMkJRE (YouTube): https://youtube.…

1 месяц, 2 недели назад @ lexfridman.com
#299 – Demis Hassabis: DeepMind
#299 – Demis Hassabis: DeepMind #299 – Demis Hassabis: DeepMind

Demis Hassabis is the CEO and co-founder of DeepMind.

Please support this podcast by checking out our sponsors:– Mailgun: https://lexfridman.com/mailgun– InsideTracker: https://insidetracker.com/lex to get 20% off– Onnit: https://lexfridman.com/onnit to get up to 10% off– Indeed: https://indeed.com/lex to get $75 credit– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 offEPISODE LINKS:Demis’s Twitter: https://twitter.com/demishassabisDeepMind’s Twitter: https://twitter.com/DeepMindDeepMind’s Instagram: https://instagram.com/deepmindDeepMind’s Website: https://deepmind.comPlasma control paper: https://nature.com/articles/s41586-021-04301-9Quantum simulation paper: https://…

1 месяц, 2 недели назад @ lexfridman.com
#298 – Susan Cain: The Power of Introverts and Loneliness
#298 – Susan Cain: The Power of Introverts and Loneliness #298 – Susan Cain: The Power of Introverts and Loneliness

Susan Cain is the author of Quiet: The Power of Introverts in a World That Can’t Stop Talking, and Bittersweet: How Sorrow and Longing Make Us Whole.

Please support this podcast by checking out our sponsors:– Brave: https://brave.com/lex– Skiff: https://skiff.org/lex to get early access– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– InsideTracker: https://insidetracker.com/lex to get 20% offEPISODE LINKS:Susan’s Twitter: https://twitter.com/susancainSusan’s Instagram: https://instagram.com/susancainauthorSusan’s Website: https://susancain.netBittersweet (book): https://amzn…

1 месяц, 2 недели назад @ lexfridman.com
Microsoft Research Podcast Microsoft Research Podcast
последний пост 4 месяца назад
135 - Just Tech: Centering Community-Driven Innovation at the Margins Episode 3 with Dr. Sasha Costanza-Chock
135 - Just Tech: Centering Community-Driven Innovation at the Margins Episode 3 with Dr. Sasha Costanza-Chock 135 - Just Tech: Centering Community-Driven Innovation at the Margins Episode 3 with Dr. Sasha Costanza-Chock

In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary L. Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations.

Dr. Gray and her team are working to bring computer science, engineering, social science, and communities together to boost societal resilience in ongoing work with Project Resolve.

She’ll talk with organizers, academics, technology leaders, and activists to understand how to develop tools and frameworks of support alongside members of these communities.

They also discuss how critical thinkers and makers from social movements…

4 месяца назад @ blubrry.com
134 - Just Tech: Centering Community-Driven Innovation at the Margins episode 2 with Dr. Tawanna Dillahunt, Zachary Rowe, and Joanna Velazquez
134 - Just Tech: Centering Community-Driven Innovation at the Margins episode 2 with Dr. Tawanna Dillahunt, Zachary Rowe, and Joanna Velazquez 134 - Just Tech: Centering Community-Driven Innovation at the Margins episode 2 with Dr. Tawanna Dillahunt, Zachary Rowe, and Joanna Velazquez

In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations.

Dr. Gray and her team are working to bring computer science, engineering, social science, and community together to boost societal resilience in ongoing work with Project Resolve.

She’ll talk with organizers, academics, technology leaders, and activists to understand how to develop tools and frameworks of support alongside members of these communities.

In this episode of the series, Dr. Gray talks with Dr. Tawanna Dillahunt, Ass…

4 месяца, 2 недели назад @ blubrry.com
133 - Just Tech: Centering Community-Driven Innovation at the Margins episode 1 with Desmond Patton and Mary Gray
133 - Just Tech: Centering Community-Driven Innovation at the Margins episode 1 with Desmond Patton and Mary Gray 133 - Just Tech: Centering Community-Driven Innovation at the Margins episode 1 with Desmond Patton and Mary Gray

In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations.

Dr. Gray and her team are working to bring computer science, engineering, social science, and community together to boost societal resilience in ongoing work with Project Resolve.

She’ll talk with organizers, academics, technology leaders, and activists to understand how to develop tools and frameworks of support alongside members of these communities.

Together, they explore Patton’s learnings about the challenges of using AI in…

4 месяца, 3 недели назад @ blubrry.com
Data Skeptic
последний пост 2 дня, 4 часа назад
Adwords with Unknown Budgets
Adwords with Unknown Budgets Adwords with Unknown Budgets

AdWords with Unknown BudgetsIn today’s episode, we are joined by Rajan Udwani, an Assistant Professor at the University of California Berkeley.

Rajan began by discussing how the tools for operations research vary based on the optimization problem.

He then delved into the optimization problem for AdWords.

Rajan explained the approaches to modelling the problem of ad allocation.

Concluding, Rajan discussed two other ideas (throttling and bid scaling) that can better optimize ad allocation.

2 дня, 4 часа назад @ dataskeptic.com
ML Ops Best Practices
ML Ops Best Practices ML Ops Best Practices

Piotr discusses common MLOps activities and how data science teams can take advantage of Neptune.ai for better experiment tracking.

He also mentioned when beginners are advised to start using machine learning tools.

Piotr also gave some advice on key activities that should be done by machine learning specialists during machine learning development.

He then talked about the short-term and long-term benefits of experiment tracking and model registry for machine learning developers.

You can read more about how to use the Neptune.ai platform from their blog page or learn more about the platform from their documentation.

5 дней, 5 часов назад @ dataskeptic.com
Affiliate Marketing Rabbithole
Affiliate Marketing Rabbithole Affiliate Marketing Rabbithole

Affiliate Marketing RabbitholeAffiliate marketing creates an opportunity for marketers to gain a commission by promoting a product or service.

Skeptoid podcast is a weekly podcast focused on conversations around skepticism and pseudoscience.

Today, Brian shares his personal story about affiliate marketing and the case he had with eBay between 2008 and 2014.

He was involved in affiliate marketing from 2002 to 2006.

Brian explained how affiliate marketing works in simple terms, and how people make money off it.

1 неделя, 2 дня назад @ dataskeptic.com
Monetization of Youtube Conspiracy Theorists
Monetization of Youtube Conspiracy Theorists Monetization of Youtube Conspiracy Theorists

Monetization of YouTube Conspiracy TheoristsToday, Cameron Ballard joins us to discuss his research paper titled, Conspiracy Brokers: Understanding the Monetization of YouTube Conspiracy Theories.

After collecting and analysing the data, Cameron discussed some observations he found.

In other words, ads likely to be financial scams or ads with fake promises take advantage of conspiracy videos for more reach.

Going forward, Cameron discussed how YouTube and other stakeholders can take action to forestall the advancement of predatory or scam ads.

Wrapping up, Cameron spoke about the Raditube project.

2 недели, 2 дня назад @ dataskeptic.com
User Perceptions of Problematic Ads
User Perceptions of Problematic Ads User Perceptions of Problematic Ads

He discusses a study and his coauthors titled, What Makes a “Bad” Ad?

Eric began by explaining what a bad ad is.

Afterwards, he discussed how bad ads find their way to social media or web pages despite the policies provided by advertising platforms.

Eric and his team crawled an enormous array of websites, including those for misinformation and political ads.

To analyze the data, Eric used a clustering technique called population label distribution learning.

3 недели, 2 дня назад @ dataskeptic.com
Political Digital Advertising Analysis
Political Digital Advertising Analysis Political Digital Advertising Analysis

Having learned how digital ads work, she wanted to delve into how governments are exploiting this technology for more reach during elections.

She spoke about why advertisers are shifting away from long-standing television ads to digital ads.

She also explained how digital ads started for political campaigns in the 2008 US presidential election and have progressed over the years.

She collected 600,000 official political campaigns about the 2020 general election on Facebook.

NaLette discussed how she captured the data to investigate this hypothesis and revealed if the hypothesis was true after her analysis.

3 недели, 5 дней назад @ dataskeptic.com
Political Digital Advertising Analysis
Political Digital Advertising Analysis Political Digital Advertising Analysis

Having learned how digital ads work, she wanted to delve into how governments are exploiting this technology for more reach during elections.

She spoke about why advertisers are shifting away from long-standing television ads to digital ads.

She also explained how digital ads started for political campaigns in the 2008 US presidential election and have progressed over the years.

She collected 600,000 official political campaigns about the 2020 general election on Facebook.

NaLette discussed how she captured the data to investigate this hypothesis and revealed if the hypothesis was true after her analysis.

3 недели, 6 дней назад @ dataskeptic.com
Fraud Detection in Crowdfunding Campaigns
Fraud Detection in Crowdfunding Campaigns Fraud Detection in Crowdfunding Campaigns

Fraud Detection in Crowdfunding CampaignsOn the show today, we are joined by Beatrice Perez.

She discusses her study titled I call BS: Fraud Detection in Crowdfunding Campaigns.

Machine learning has largely been used for bank fraud detection but finds sparse application in detecting fraudulent campaigns on crowdfunding platforms.

She also explained the data collection process of retrieving properties of various crowdfunding campaigns.

Rounding up, she gave some advice on how users can spot a potential fraudulent campaign on crowdfunding platforms.

1 месяц назад @ dataskeptic.com
Artificial Intelligence and Auction Design
Artificial Intelligence and Auction Design Artificial Intelligence and Auction Design

Artificial Intelligence and Auction DesignMartino Banchio, a PhD student at the Stanford Graduate School of Business, joins us to discuss his study on the intersection of artificial intelligence and economics.

He speaks to us about his findings from his research titled, Artificial Intelligence and Auction Design.

He particularly discussed how the knowledge of game theory is key to model interactions between economic agents and decision agents.

He went deeper into what game theory was using chess as an example, and how it is applied in modelling economic problems.

Trust issues can cause collusive agreements to fail, but Martino discussed how reward-punishment schemes can sustain low bids.

1 месяц, 1 неделя назад @ dataskeptic.com
Privacy Preference Signals
Privacy Preference Signals Privacy Preference Signals

Max’s research focuses on tracking website cookie dialogues.

He joins us to discuss the efforts of regulatory standards in the ad tech industry.

There is a need for standards to be put in place to regulate the activities of ad tech.

Max mentioned the ad tech standards that have been used over the years.

He rounded up by sharing his thoughts on what the ad tech space should look like ideally.

1 месяц, 2 недели назад @ dataskeptic.com
Neural Architecture Search for CTR Prediction
Neural Architecture Search for CTR Prediction Neural Architecture Search for CTR Prediction

Neural Architecture Search for CTR PredictionWe are joined by Ravi Krishna, an AI Scientist.

He joins us to discuss his recent work on the implementation of a differentiable NAS framework for ads CTR prediction.

Ravi began with an explanation of what CTR (click-through rate) is about and why it is a vital AI problem today.

He advanced to the NAS (neural architecture search) side of his research - explaining what NAS is, why it is important, how the framework was built and the commensurate result.

His NAS framework could reduce the embedded table compression 15.1X whilst only increasing the loss from 0.4442 to 0.4454.

1 месяц, 3 недели назад @ dataskeptic.com
Algorithmic PPC Management
Algorithmic PPC Management Algorithmic PPC Management

Algorithmic PPC ManagementEffectively managing a large budget of pay per click advertising demands software solutions.

When spending multi-million dollar budgets on hundreds of thousands of keywords, an effective algorithmic strategy is required to optimize marketing objectives.

In this episode, Nathan Janos joins us to share insights from his work in the ad tech industry.

1 месяц, 3 недели назад @ dataskeptic.com
Data Skeptic: Ad Tech
Data Skeptic: Ad Tech Data Skeptic: Ad Tech

Increasingly, people get most if not all of the information they consume online. Alongside the web sites, videos, apps, and other destinations, we’re consistently served advertisements alongside the organic content we search for or discover. Targetted ads make it possible for you to discover relevant new products you might otherwise not have heard about. Targetting can also open a pandora’s box of ethical considerations. Online advertising is a complex network of automated systems. Algorithms controlling algorithms controlling what we see. This season of Data Skeptic will focus on the applications of data science to digital advertising technology. In this first episode in particular, Kyle s…

2 месяца назад @ dataskeptic.com
The Reliability of Mobile Phone Data
The Reliability of Mobile Phone Data The Reliability of Mobile Phone Data

Our mobile phones generate an incredible amount of data inbound and outbound. In today’s episode, Nishant Kishore, a PhD graduate of Harvard University in Infectious Disease Epidemiology, explains how mobility data from mobile phones can be captured and analysed to understand the spread of infectious diseases.

2 месяца назад @ dataskeptic.com
Haywire Algorithms
Haywire Algorithms Haywire Algorithms

The pandemic changed how we lived. And this had a ripple effect on the performance of machine learning models. Ravi Parikh joins us today to discuss how the pandemic has affected the performance of machine learning models in clinical care and some actionable steps to fix it.

2 месяца, 1 неделя назад @ dataskeptic.com
SuperDataScience SuperDataScience
последний пост 1 день, 6 часов назад
SDS 601: Venture Capital for Data Science
SDS 601: Venture Capital for Data Science SDS 601: Venture Capital for Data Science

This week, Sarah Catanzaro, General Partner at Amplify Partners joins Jon for an episode that dives into the venture capital side of data science.

Learn how to fund your data science business idea, take note of what star…

1 день, 6 часов назад @ soundcloud.com
SDS 600: Yoga Nidra Practice with Steve Fazzari
SDS 600: Yoga Nidra Practice with Steve Fazzari SDS 600: Yoga Nidra Practice with Steve Fazzari

Rest and relaxation await as Steve Fazzari joins us this week for a special edition of the podcast!

Tune in for a rejuvenating session of Yoga Nidra led beautifully by the expert.

Additional materials: www.superdatascie…

5 дней, 6 часов назад @ soundcloud.com
SDS 599: MLOps: Machine Learning Operations
SDS 599: MLOps: Machine Learning Operations SDS 599: MLOps: Machine Learning Operations

This week, Mikiko Bazeley, Senior Software Engineer at Mailchimp joins the podcast to share her in-depth knowledge of MLOps: Machine Learning Operations.

Tune in to hear her discuss what it entails, why it's so critical …

1 неделя, 1 день назад @ soundcloud.com
SDS 598: Getting Kids Excited about STEM Subjects
SDS 598: Getting Kids Excited about STEM Subjects SDS 598: Getting Kids Excited about STEM Subjects

Ben Taylor makes a fourth appearance on Five-Minute Friday to discuss the best ways to introduce STEM to children.

Tune in to hear the many ways in which he thinks STEM education will evolve in the future.

Additional m…

1 неделя, 5 дней назад @ soundcloud.com
SDS 597: A.I. Policy at OpenAI
SDS 597: A.I. Policy at OpenAI SDS 597: A.I. Policy at OpenAI

Miles Brundage, Head of Policy Research at OpenAI, joins Jon Krohn this week to discuss AI model production, policy, safety, and alignment.

Tune in to hear him speak on GPT-3, DALL-E, Codex, and CLIP as well.

2 недели, 1 день назад @ soundcloud.com
SDS 596: The A.I. Platforms of the Future
SDS 596: The A.I. Platforms of the Future SDS 596: The A.I. Platforms of the Future

Ben Taylor returns for a third Five-Minute Friday episode!

This week, he looks ahead and digs into what we can expect from the A.I.

platforms of the future.

Additional materials: www.superdatascience.com/596

2 недели, 5 дней назад @ soundcloud.com
SDS 595: Data Engineering 101
SDS 595: Data Engineering 101 SDS 595: Data Engineering 101

Tune in as Joe Reis and Matt Housley, co-founders of Ternary Data and co-authors of the book “Fundamentals of Data Engineering” join Jon Krohn to discuss major undercurrents across the data engineering lifecycle, and the…

3 недели, 1 день назад @ soundcloud.com
SDS 594: Why CEOs Care About A.I. More than Other Technologies
SDS 594: Why CEOs Care About A.I. More than Other Technologies SDS 594: Why CEOs Care About A.I. More than Other Technologies

This week, Jon Krohn and A.I.

industry veteran Ben Taylor discuss the driving factors that push CEOs to prioritize A.I.

over other technologies.

Additional materials: www.superdatascience.com/594

3 недели, 5 дней назад @ soundcloud.com
SDS 593: The Real-World Impact of Cross-Disciplinary Data Science Collaboration
SDS 593: The Real-World Impact of Cross-Disciplinary Data Science Collaboration SDS 593: The Real-World Impact of Cross-Disciplinary Data Science Collaboration

Jon welcomes Professor Philip Bourne, Founding Dean of the School of Data Science at the University of Virginia to discuss his biomedical data science research, the importance of open-source and open-access within the in…

4 недели, 1 день назад @ soundcloud.com
SDS 592: How to Sell a Multimillion Dollar A.I. Contract
SDS 592: How to Sell a Multimillion Dollar A.I. Contract SDS 592: How to Sell a Multimillion Dollar A.I. Contract

In this episode, Jon Krohn welcomes A.I.

industry veteran Ben Taylor to discuss how to sell multimillion dollar A.I.

Tune in to hear why trust and proof of value are some of the critical steps in his sales pro…

1 месяц назад @ soundcloud.com
SDS 591: Simulations and Synthetic Data for Machine Learning
SDS 591: Simulations and Synthetic Data for Machine Learning SDS 591: Simulations and Synthetic Data for Machine Learning

Mars Buttfield-Addison, PhD Candidate at the University of Tasmania, joins Jon Krohn for a high-energy episode covering everything from Machine Learning simulations to Swift, space junk, and more!

In this episode you wi…

1 месяц назад @ soundcloud.com
SDS 590: Artificial General Intelligence is Not Nigh (Part 2 of 2)
SDS 590: Artificial General Intelligence is Not Nigh (Part 2 of 2) SDS 590: Artificial General Intelligence is Not Nigh (Part 2 of 2)

In this episode, Jon continues his two-part series on artificial general intelligence (AGI) and why we are unlikely to realize it anytime soon.

Listen in as Jon reviews Meta's Yann LeCun's seven-part perspective on the t…

1 месяц, 1 неделя назад @ soundcloud.com
SDS 589: Narrative A.I. with Hilary Mason
SDS 589: Narrative A.I. with Hilary Mason SDS 589: Narrative A.I. with Hilary Mason

Hilary Mason, Co-Founder and CEO of Hidden Door, joins Jon Krohn for a live discussion that explores narrative A.I., emerging ML techniques, and how her OSEMN data science process developed.

In this episode you will lea…

1 месяц, 1 неделя назад @ soundcloud.com
SDS 588: Artificial General Intelligence is Not Nigh
SDS 588: Artificial General Intelligence is Not Nigh SDS 588: Artificial General Intelligence is Not Nigh

In this episode, Jon kicks off a two-part series that sees him explore the popular topic of artificial general intelligence and why it might–or might not–be only a few years away.

Listen in as Jon explains the several re…

1 месяц, 2 недели назад @ soundcloud.com
SDS 587: Data Engineering for Data Scientists
SDS 587: Data Engineering for Data Scientists SDS 587: Data Engineering for Data Scientists

Mark Freeman, Senior Data Scientist at Humu, joins Jon Krohn to talk about all things data engineering and offers listeners some critical tips for their data science career journey – from what it takes to get promoted to…

1 месяц, 2 недели назад @ soundcloud.com
Data Science at Home Data Science at Home
последний пост 2 месяца, 2 недели назад
What are generalist agents and why they can change the AI game (Ep. 199)
What are generalist agents and why they can change the AI game (Ep. 199) What are generalist agents and why they can change the AI game (Ep. 199)

June 3, 2022 podcastThat deep learning alone is not sufficient to solve artificial general intelligence, is more and more accepted statement.

Generalist agents have great properties that can overcome some of the limitations of single-task deep learning models.

Be aware, we are still far from AGI, though.

So what are generalist agents?

Referenceshttps://arxiv.org/pdf/2205.06175

2 месяца, 2 недели назад @ datascienceathome.com
Streaming data with ease. With Chip Kent from Deephaven Data Labs (Ep. 198)
Streaming data with ease. With Chip Kent from Deephaven Data Labs (Ep. 198) Streaming data with ease. With Chip Kent from Deephaven Data Labs (Ep. 198)

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2 месяца, 3 недели назад @ datascienceathome.com
Learning from data to create personalized experiences with Matt Swalley from Omneky (Ep. 197)
Learning from data to create personalized experiences with Matt Swalley from Omneky (Ep. 197) Learning from data to create personalized experiences with Matt Swalley from Omneky (Ep. 197)

May 16, 2022 podcastIn this episode I speak with Matt Swalley, Chief Business Officer of Omneky, an AI platform that generates, analyzes and optimizes personalized ad creatives at scale.

We speak about the way AI is used for generating customized recommendation and creating experiences with data aggregation and analytics.

respecting the privacy of individuals.

LinksGrow your business with personalized ads https://www.omneky.com/Data Science at Home Podcast (Live) https://www.twitch.tv/datascienceathome

3 месяца назад @ datascienceathome.com
State of Artificial Intelligence 2022 (Ep. 196)
State of Artificial Intelligence 2022 (Ep. 196) State of Artificial Intelligence 2022 (Ep. 196)

May 16, 2022 podcastLet’s take a break and think about the state of AI in 2022.

In this episode I summarize the long report from the Stanford Institute for Human-Centered Artificial Intelligence (HAI)Enjoy!

If you want a new interactive experience, I am scheduling hands-on session on TwitchFeel free to drop by when there is a live session, and interact with me.

I’ll see you there!

Referenceshttps://spectrum.ieee.org/artificial-intelligence-indexhttps://www.twitch.tv/datascienceathome

3 месяца назад @ datascienceathome.com
Improving your AI by finding issues within data pockets (Ep. 195)
Improving your AI by finding issues within data pockets (Ep. 195) Improving your AI by finding issues within data pockets (Ep. 195)

May 16, 2022 podcastIn this episode I have a conversation with, Itai Bar-Sinai, CPO & Cofounder of Mona.

Why is AI monitoring so different from monitoring classic software?

How to reduce the gap between data science and business?

What is the role of MLOps in the data monitoring field?

With over 10 years of experience with AI and as the CPO and head of customer success at Mona, the leading AI monitoring intelligence company, Itai has a unique view of the AI industry.

3 месяца назад @ datascienceathome.com
Fake data that looks, feels, and behaves like production.(Ep.194)
Fake data that looks, feels, and behaves like production.(Ep.194) Fake data that looks, feels, and behaves like production.(Ep.194)

April 21, 2022 podcastI am with Ander Steele, data scientist and mathematician with a passion for privacy and Shannon Bayatpur, product manager with a background in technical writing and computer science, from Tonic.ai.

We speak about data.

But all we say is authentic.

LinksTonic websiteCareer pageNeural networks for synthetic data

3 месяца, 4 недели назад @ datascienceathome.com
Batteries and AI in Automotive (Ep. 193)
Batteries and AI in Automotive (Ep. 193) Batteries and AI in Automotive (Ep. 193)

April 21, 2022 podcastIn this episode my friend and I speak about AI, batteries and automotive.

Dennis Berner, founder of Digitlabs has been operating in the field of automotive and batteries for a long time.

His point of views are absolutely a must to listen to.

Below a list of the links he mentioned in the show.

3 месяца, 4 недели назад @ datascienceathome.com
Bayesian Machine Learning with Ravin Kumar (Ep. 191)
Bayesian Machine Learning with Ravin Kumar (Ep. 191) Bayesian Machine Learning with Ravin Kumar (Ep. 191)

April 21, 2022 podcastThis is one episode where passion for math, statistics and computers are merged.

I have a very interesting conversation with Ravin, data scientist at Google where he uses data to inform decisions.

All opinions in this episode are his own and none of the companies he has worked for are represented.

and by Amethix TechnologiesAmethix 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.

3 месяца, 4 недели назад @ datascienceathome.com
What is spatial data science? With Matt Forest from Carto (Ep. 190)
What is spatial data science? With Matt Forest from Carto (Ep. 190) What is spatial data science? With Matt Forest from Carto (Ep. 190)

We speak about machine learning applied to spatial data, spatial SQL and GIS (Geographic Information System).

Just tell them you came through Data Science at Home podcast.

and by Amethix TechnologiesAmethix 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.

ReferencesCarto https://carto.comSpatial Feature Engineering: https://geographicdata.science/book/intro.htmlCARTO Blog: https://carto.com/blog/Spati…

3 месяца, 4 недели назад @ datascienceathome.com
Connect. Collect. Normalize. Analyze. An interview with the people from Railz AI (Ep. 189)
Connect. Collect. Normalize. Analyze. An interview with the people from Railz AI (Ep. 189) Connect. Collect. Normalize. Analyze. An interview with the people from Railz AI (Ep. 189)

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

By clicking “Accept”, you consent to the use of ALL the cookies.

4 месяца, 2 недели назад @ datascienceathome.com
History of data science [RB] (Ep. 188)
History of data science [RB] (Ep. 188) History of data science [RB] (Ep. 188)

We answer such questions and much more in this wonderful episode with Triveni Gandhi, Senior Data Scientist and Shaun McGirr, AI Evangelist at Dataiku.

We cover topics about the history of data science, ethical AI and…This episode is brought to you by DataikuWith Dataiku, you have everything you need to build and deploy AI projects in one place, including easy-to-use data preparation and pipelines, AutoML, and advanced automation.

Get secure and private access to the internet by surfing nordvpn.com/DATASCIENCE or use coupon code DATASCIENCE and get a massive discount.

and by Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and pre…

4 месяца, 2 недели назад @ datascienceathome.com
Artificial Intelligence and Cloud Automation with Leon Kuperman from Cast.ai (Ep. 187)
Artificial Intelligence and Cloud Automation with Leon Kuperman from Cast.ai (Ep. 187) Artificial Intelligence and Cloud Automation with Leon Kuperman from Cast.ai (Ep. 187)

April 1, 2022 podcastIn this episode I speak about AI and cloud automation with Leon Kuperman, co-founder and CTO at CAST AI.

Chat with meJoin us on Discord community chat to discuss the show, suggest new episodes and chat with other listeners!

Sponsored by Amethix TechnologiesAmethix 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.

Get secure and private access to the internet by surfing nordvpn.com/…

4 месяца, 2 недели назад @ datascienceathome.com
Embedded Machine Learning: Part 5 – Machine Learning Compiler Optimization (Ep. 186)
Embedded Machine Learning: Part 5 – Machine Learning Compiler Optimization (Ep. 186) Embedded Machine Learning: Part 5 – Machine Learning Compiler Optimization (Ep. 186)

February 3, 2022 podcastThis is the last episode of the series “Embedded ML” and I made it for the bravest 🙂I speak about machine learning compiler optimization to a much greater detail.

Enjoy the episode!

Chat with meJoin us on Discord community chat to discuss the show, suggest new episodes and chat with other listeners!

Sponsored by Amethix TechnologiesAmethix 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…

6 месяцев, 2 недели назад @ datascienceathome.com
Embedded Machine Learning: Part 4 – Machine Learning Compilers (Ep. 185)
Embedded Machine Learning: Part 4 – Machine Learning Compilers (Ep. 185) Embedded Machine Learning: Part 4 – Machine Learning Compilers (Ep. 185)

January 25, 2022 podcastIn this episode I speak about machine learning compilers, the most important tools to bridge the gap between high level frontends, ML backends and hardware target architectures.

There are several compilers one can choose.

Chat with meJoin us on Discord community chat to discuss the show, suggest new episodes and chat with other listeners!

Sponsored by Amethix TechnologiesAmethix 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 s…

6 месяцев, 3 недели назад @ datascienceathome.com
Embedded Machine Learning: Part 3 – Network Quantization (Ep. 184)
Embedded Machine Learning: Part 3 – Network Quantization (Ep. 184) Embedded Machine Learning: Part 3 – Network Quantization (Ep. 184)

January 20, 2022 podcastIn this episode I speak about neural network quantization, a technique that makes networks feasible for embedded systems and small devices.

There are many quantization techniques depending on several factors that are all important to consider during design and implementation.

Chat with meJoin us on Discord community chat to discuss the show, suggest new episodes and chat with other listeners!

Sponsored by Amethix TechnologiesAmethix 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 hig…

6 месяцев, 4 недели назад @ datascienceathome.com