Very ML
State-of-the-art Machine Learning News Feed
/r/MachineLearning
последний пост 55 минут назад
[D] Replacement Options for the Stellargraph Library
[D] Replacement Options for the Stellargraph Library

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55 минут назад @ reddit.com
[D] Is there a way to filter out "low quality" text sequences for a text classification task? Is there even a way to define "low quality?"
[D] Is there a way to filter out "low quality" text sequences for a text classification task? Is there even a way to define "low quality?"

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[D] suggestions
[D] suggestions

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[D] Neuromorphic Computing
[D] Neuromorphic Computing

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[P]Implementing OpenAI Whisper with a simple GUI
[P]Implementing OpenAI Whisper with a simple GUI

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[D] DreamBooth Stable Diffusion training now possible in 24GB GPUs, and it runs about 2 times faster.
[D] DreamBooth Stable Diffusion training now possible in 24GB GPUs, and it runs about 2 times faster.

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[D] Where does end-to-end learning fail?
[D] Where does end-to-end learning fail?

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[P] Stable diffusion free discord bot
[P] Stable diffusion free discord bot

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[P] Data Labeling for ML Model Retraining with Label Studio
[P] Data Labeling for ML Model Retraining with Label Studio

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[D] Language Modeling for Sequence Labeling of Long Text with Specialized Corpus
[D] Language Modeling for Sequence Labeling of Long Text with Specialized Corpus

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[R], Behavior-Oriented Design
[R], Behavior-Oriented Design

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[N] Announcing the BigCode project - building large language models for code in an open/responsible way
[N] Announcing the BigCode project - building large language models for code in an open/responsible way

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[D] Extracting the n-th hidden state from GRU output
[D] Extracting the n-th hidden state from GRU output

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[D] Are there significant performance benefits to AVX-512?
[D] Are there significant performance benefits to AVX-512?

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[D] MQTransformer: Not good enough for ICLR but SOTA for Amazon?
[D] MQTransformer: Not good enough for ICLR but SOTA for Amazon?

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15 часов назад @ reddit.com
Towards Data Science
последний пост 55 минут назад
Introducing the White’s Heteroskedasticity Consistent Estimator
Introducing the White’s Heteroskedasticity Consistent Estimator Introducing the White’s Heteroskedasticity Consistent Estimator

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55 минут назад @ towardsdatascience.com
How to Prepare Scikit-Learn Models for Production
How to Prepare Scikit-Learn Models for Production How to Prepare Scikit-Learn Models for Production

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1 час назад @ towardsdatascience.com
OpenAI Whisper Holds the Key to GPT-4
OpenAI Whisper Holds the Key to GPT-4 OpenAI Whisper Holds the Key to GPT-4

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1 час назад @ towardsdatascience.com
10 Less-Known Python Visualization Concepts and Hacks
10 Less-Known Python Visualization Concepts and Hacks 10 Less-Known Python Visualization Concepts and Hacks

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Simple Logistic Regression for Dichotomous Variables in R
Simple Logistic Regression for Dichotomous Variables in R Simple Logistic Regression for Dichotomous Variables in R

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2 часа назад @ towardsdatascience.com
Dispelling Stereotypes Of Digitalization and Data Analytics
Dispelling Stereotypes Of Digitalization and Data Analytics Dispelling Stereotypes Of Digitalization and Data Analytics

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2 часа назад @ towardsdatascience.com
Identifying Goalkeepers’ Build-Up Style Using Machine Learning
Identifying Goalkeepers’ Build-Up Style Using Machine Learning Identifying Goalkeepers’ Build-Up Style Using Machine Learning

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2 часа назад @ towardsdatascience.com
Microsoft Power BI — From Data Modelling to Stunning Reports
Microsoft Power BI — From Data Modelling to Stunning Reports Microsoft Power BI — From Data Modelling to Stunning Reports

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8 часов назад @ towardsdatascience.com
CLIP: The Most Influential AI Model From OpenAI— And How To Use It
CLIP: The Most Influential AI Model From OpenAI— And How To Use It CLIP: The Most Influential AI Model From OpenAI— And How To Use It

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8 часов назад @ towardsdatascience.com
Automate PowerPoint Slides Creation with Python
Automate PowerPoint Slides Creation with Python Automate PowerPoint Slides Creation with Python

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8 часов назад @ towardsdatascience.com
Does my new central heating boiler help with these crazy high gas prices?
Does my new central heating boiler help with these crazy high gas prices? Does my new central heating boiler help with these crazy high gas prices?

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9 часов назад @ towardsdatascience.com
An Equation for Intelligence
An Equation for Intelligence An Equation for Intelligence

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9 часов назад @ towardsdatascience.com
4 Ways I’m Using Data Science Skills to Generate Income From Side Hustles
4 Ways I’m Using Data Science Skills to Generate Income From Side Hustles 4 Ways I’m Using Data Science Skills to Generate Income From Side Hustles

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9 часов назад @ towardsdatascience.com
Transcribe audio files with OpenAI’s Whisper
Transcribe audio files with OpenAI’s Whisper Transcribe audio files with OpenAI’s Whisper

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Statistical Comparison Among Multiple Groups With ANOVA
Statistical Comparison Among Multiple Groups With ANOVA Statistical Comparison Among Multiple Groups With ANOVA

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10 часов назад @ towardsdatascience.com
Distill.pub Distill.pub
последний пост None
The Gradient The Gradient
последний пост 15 часов назад
Artificial Intelligence and the Future of Demos
Artificial Intelligence and the Future of Demos Artificial Intelligence and the Future of Demos

In one of the claimed birthplaces of democracy, Ancient Athens, demos covered all Athenian citizens, who had an equal say in collective decision-making.

And only the real people – the demos – can recognize the ‘real’ from the ‘not-so-real.’In essence, if you are not part of the demos, you have no say in collective decision-making.

Original Photo: Daria Shevtsova / Pixabay, edited by authorIn democracies, it is the demos that should have the topmost power over collective decision-making.

If we want to preserve democracy and/or demos based on equality and freedom, we could start asking ourselves: Is our future demos nation-state-based or global, and how could we align AI development with this…

15 часов назад @ thegradient.pub
Causal Inference: Connecting Data and Reality
Causal Inference: Connecting Data and Reality Causal Inference: Connecting Data and Reality

Any causal inference problem consists of two parts: causal identification and statistical inference.

Causal inference theoryCausal inference is a theory that describes, discriminates, and measures causal relationships, developed from statistics.

Causal representation learningUnlike the traditional causal inference approach, which uses causal graphs to connect random variables to complete the causal discovery and reasoning hypothesis task, the problem of causal representation learning has recently attracted more attention.

is not valid, and causal inference studies exactly such a situation: how to learn a causal model that can work under different distributions, imply a causal mechanism (Cau…

3 недели, 1 день назад @ thegradient.pub
The Future of Speech Recognition: Where Will We Be in 2030?
The Future of Speech Recognition: Where Will We Be in 2030? The Future of Speech Recognition: Where Will We Be in 2030?

"By 2030, speech recognition will feature truly multilingual models, rich standardized output objects, and be available to all and at scale.

Finally, speech recognition will engender the principles of responsible AI, and operate without bias."

Source: Hannun, Awni, “Speech Recognition is not Solved”.

CitationFor attribution in academic contexts or books, please cite this work asMigüel Jetté and Corey Miller, "The Future of Speech Recognition: Where will we be in 2030?

BibTeX citation:@article{miller2021futureofowork,author = {Jetté, Migüel and Miller, Corey},title = {The Future of Speech Recognition: Where will we be in 2030?

1 месяц, 1 неделя назад @ thegradient.pub
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…

1 месяц, 3 недели 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…

2 месяца 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…

3 месяца, 2 недели 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…

3 месяца, 3 недели 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?

3 месяца, 4 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…

4 месяца, 1 неделя 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.

4 месяца, 2 недели 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…

4 месяца, 3 недели 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.

4 месяца, 3 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…

4 месяца, 4 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…

5 месяцев, 1 неделя 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.

5 месяцев, 2 недели назад @ thegradient.pub
TheSequence TheSequence
последний пост 17 часов назад
📝 Guest post: 4 Types of ML Data Errors You Can Fix Right Now*
📝 Guest post: 4 Types of ML Data Errors You Can Fix Right Now* 📝 Guest post: 4 Types of ML Data Errors You Can Fix Right Now*

In this article, we’ll walk you through four of the most prominent types of data errors and show you techniques for fixing them.

Use Galileo to find and fix your ML data errors in minutes.

❗️ML Data Error #2: Class OverlapAnother common data error we see, especially for problems with many ground truth classes, is an overlap in the class definitions.

❗️ML Data Error #4: DriftA model deployed in production only knows to make predictions based on what you trained it on.

We want to empower you to find and fix data errors in minutes instead of hours without worrying about the technical details.

17 часов назад @ thesequence.substack.com
❇️ NVIDIA Continues Pushing AI’s Boundaries
❇️ NVIDIA Continues Pushing AI’s Boundaries ❇️ NVIDIA Continues Pushing AI’s Boundaries

📝 EditorialIn the last few years, NVIDIA has ranked among the top companies advancing AI research and development.

During its latest GTC conference, NVIDIA announced major releases in both AI software and hardware technologies.

On the software side, NVIDIA announced new cloud services such as NeMo for large-scale language models, Omniverse ACE for intelligent avatars, and BioNeMo computational biology.

NVIDIA also announced a number of architectures based on ADA, such as chips optimized for virtual worlds, computational inference, recommendation models, and many others.

Continuing its innovation in areas such as self-driving vehicles and robotics, NVIDIA announced Thor, a new 2,000 teraflop…

1 день, 18 часов назад @ thesequence.substack.com
👾 Edge#228: How Amazon is Improving BERT-Based Models Used in Alexa
👾 Edge#228: How Amazon is Improving BERT-Based Models Used in Alexa 👾 Edge#228: How Amazon is Improving BERT-Based Models Used in Alexa

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: How Amazon is Improving BERT-Based Models Used in AlexaBERT has become one of the most iconic machine learning (ML) methods of the last decade.

Amazon has been one of the main adopters of BERT-based models, particularly in the architecture powering the Alexa digital assistant.

As a result, Amazon Research regularly publishes improvements to BERT-based models in order to address some of the large-scale scenarios required by Alexa.

Recently, we got a glimpse of Amazon Research’s recent work in BERT-based models with the publication of three different p…

4 дня, 17 часов назад @ thesequence.substack.com
📝 Guest post: Unlock the Power of BLOOM With the Broadest Range of GPUs Served On-Demand*
📝 Guest post: Unlock the Power of BLOOM With the Broadest Range of GPUs Served On-Demand* 📝 Guest post: Unlock the Power of BLOOM With the Broadest Range of GPUs Served On-Demand*

Larger contexts may require the RTX 5000, depending on how efficient your inference engine is.

NVIDIA RTX 5000The Turing-based RTX 5000 is the smallest GPU that can run inference for the GPT-J 6B or Fairseq 6.7B models.

NVIDIA A4000The Ampere-based A4000 is a small step up from the RTX 5000, although it may not look like it at first glance.

However, the number of tensor cores is half that of the RTX 5000.

Whether the A4000 or the RTX 5000 work better for your workload depends on your inference framework and what instructions you use.

5 дней, 17 часов назад @ thesequence.substack.com
📃➡️🖼 Edge#227: Autoregressive Text-to-Image Models
📃➡️🖼 Edge#227: Autoregressive Text-to-Image Models 📃➡️🖼 Edge#227: Autoregressive Text-to-Image Models

In this issue:we explain autoregressive text-to-image models ;we discuss Google’s Parti, an impressive autoregressive text-to-image model;we explore MS COCO, one of the most common datasets in text-to-image models.

💡 ML Concept of the Day: Autoregressive Text-to-Image ModelsThe first editions of our text-to-image series have focused on exploring diffusion models, which have been at the forefront of some of the most impressive developments in this area.

Diffusion models work by introducing Gaussian noise into an input image and then training a model to reconstruct the original image from the noise.

An interesting alternative to diffusion models is autoregressive methods used for text-to-imag…

6 дней, 18 часов назад @ thesequence.substack.com
🐦 Follow us on Twitter
🐦 Follow us on Twitter 🐦 Follow us on Twitter

Hi there,A quick Monday note about our Twitter :)We share lots of helpful resources for your data science and ML journey.

Free courses:and books:and helpful lists:As well as our favorite ‘ML Research of the Week’:FOLLOW US ON TWITTERIf you haven’t yet, please help us shape the ML value chain landscapeLet’s create an objective landscape of the ML Value Chain together.

They are typically assembled by either analyst firms, media, or VC firms.

But we trust that TheSequence’s audience only can shape an accurate landscape of the ML Value Chain.

Participate and be the first to receive this super helpful research shaped by you!

1 неделя назад @ thesequence.substack.com
🔥 The PyTorch Foundation
🔥 The PyTorch Foundation 🔥 The PyTorch Foundation

Last week, Meta took an important step in that direction by announcing that PyTorch will move to an independent foundation.

The PyTorch Foundation was incubated under the Linux Foundation, which brings a level of credibility and process robustness to this new effort.

Finding a neutral home was the main catalyst for the creation of the PyTorch Foundation, but there are other intangibles such as robust governance, community ownership, transparency and fairness that are much needed in a project of this scale.

The Linux Foundation certainly has the knowledge and infrastructure to become an incredible home to catalyze the next phase of PyTorch’s growth.

ML training experiments can be more easily…

1 неделя, 1 день назад @ thesequence.substack.com
📌 Event: Leverage your Snowflake, BigQuery, Redshift Data Warehouse with a Real-Time Feature Store / Sept 21
📌 Event: Leverage your Snowflake, BigQuery, Redshift Data Warehouse with a Real-Time Feature Store / Sept 21 📌 Event: Leverage your Snowflake, BigQuery, Redshift Data Warehouse with a Real-Time Feature Store / Sept 21

Hopsworks feature store can be configured to leverage the content of data warehouses to simplify the data science workflow.

For data scientists, using data directly from a data warehouse presents three challenges:data in the data warehouse is often updated making it impossible to reproduce previously generated training data and previous experiments.

Data warehouses often lack the historical view of the data, leaving to data scientists the chore of building it.

In this talk, we will discuss how Hopsworks can be connected to existing cloud-native data warehouses like Snowflake, Redshift and BigQuery.

What : Leverage your Snowflake, BigQuery, Redshift Data Warehouse with a Real-Time Feature St…

1 неделя, 3 дня назад @ thesequence.substack.com
🗜🗜Edge#226: DeepSpeed Compression, a new library for extreme compression of deep learning models
🗜🗜Edge#226: DeepSpeed Compression, a new library for extreme compression of deep learning models 🗜🗜Edge#226: DeepSpeed Compression, a new library for extreme compression of deep learning models

💥 What’s New in AI: Microsoft’s Open Sourced a New Library for Extreme Compression of Deep Learning ModelsLarge neural networks have been dominating the deep learning space for the last few years.

While the performance of large deep learning architectures is certainly impressive, its operational requirements remain prohibited for most organizations.

Not surprisingly, there has been a lot of effort in areas like model compression that can help reduce the size and inference computation of deep learning models.

Similarly, there has also been a resurgence of system optimization techniques that can improve the inference of models without sacrificing their size.

The combination of model compressi…

1 неделя, 4 дня назад @ thesequence.substack.com
🎙Or Itzary/Superwise About Model Observability and Streamlining Large ML Projects
🎙Or Itzary/Superwise About Model Observability and Streamlining Large ML Projects 🎙Or Itzary/Superwise About Model Observability and Streamlining Large ML Projects

Or Itzary (OI): I’m Or Itzary, CTO at Superwise, which, as you’ve all probably heard 😊 is one of the leading model observability platforms.

🛠 ML WorkSuperwise has rapidly become one of the premier ML observability platforms in the market but you guys recently launched a new effort focused on enabling high-scale model observability.

What are some of the unique challenges of monitoring ML pipelines at large scale and what are some of the key differences with ML observability practices for single models?

Superwise Projects are explicitly built to streamline the high-scale observability and monitoring management of ML models.

What are some of the key ideas in ML observability methods to ensure …

1 неделя, 5 дней назад @ thesequence.substack.com
😶‍🌫️ Edge#225: Understanding Latent Diffusion Models
😶‍🌫️ Edge#225: Understanding Latent Diffusion Models 😶‍🌫️ Edge#225: Understanding Latent Diffusion Models

In this issue:we explain latent diffusion models ;we discuss the original latent diffusion paper ;we explore Hugging Face Diffusers, a library for state-of-the-art diffusion models.

💡 ML Concept of the Day: Understanding Latent Diffusion ModelsIn the previous edition of our series about text-to-image synthesis (Edge#223), we explored the different types of diffusion techniques that are used by models in this area.

Latent diffusion has quickly developed as one of the most viable options for implementing diffusion models in the real world without breaking the bank.

Diffusion models have been able to achieve state-of-the-art performance in image synthesis.

Latent diffusion models (LDM) enable

1 неделя, 6 дней назад @ thesequence.substack.com
↕️↔️TensorFlow 2.10 is Here
↕️↔️TensorFlow 2.10 is Here ↕️↔️TensorFlow 2.10 is Here

📝 EditorialTensorFlow and PyTorch have become the two most popular deep learning frameworks within the data science community.

TensorFlow 2.10 has plenty of interesting features, but none were more notable than the improvements in Keras’ capabilities.

The new version of TensorFlow includes improves Keras’ attention layers with features such as causal attention and implicit masking.

Outside Keras, TensorFlow 2.10 includes hardware optimizations such as the support for the Compute Library for the Arm® Architecture (ACL) as well as a wider GPU support for Windows.

TensorFlow 2.10 shouldn’t be considered a major release but certainly incorporates many features that have been highly demanded by …

2 недели, 1 день назад @ thesequence.substack.com
📌 Event: Learn strategies to scale your ML models using Kubernetes - SEP 14
📌 Event: Learn strategies to scale your ML models using Kubernetes - SEP 14 📌 Event: Learn strategies to scale your ML models using Kubernetes - SEP 14

Running distributed workloads is key to the future of AI.

As models become more complex and advanced, distributed workloads will be the only way forward.

Get ahead of the curve, and learn practical hands-on guidance from Kubernetes expert Itay Ariel on how to leverage Kubernetes for distributed workloads.

The main challenges of running distributed workloads on Kubernetes.

How to easily use Kubernetes to scale your ML models.

2 недели, 3 дня назад @ thesequence.substack.com
🤘Edge#224: AlexaTM 20B is Amazon’s New Language Super Model Also Capable of Few-Shot Learning
🤘Edge#224: AlexaTM 20B is Amazon’s New Language Super Model Also Capable of Few-Shot Learning 🤘Edge#224: AlexaTM 20B is Amazon’s New Language Super Model Also Capable of Few-Shot Learning

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: AlexaTM 20B is Amazon’s New Language Super Model Also Capable of Few-Shot LearningIn the last few years, the progress in natural language understanding (NLU) really challenges human imagination.

Some of the milestones achieved by models like OpenAI GPT-3 seemed unimaginable just a few years ago.

Large AI labs like Microsoft Research, Google Brain, Alexa AI, DeepMind, and Meta AI are regularly pushing the boundaries of NLU research.

One of the latest entrances in the language supermodel category came from Amazon’s Alexa AI labs with Alexa Teacher Mode…

2 недели, 4 дня назад @ thesequence.substack.com
🗺 ❓What is the current ML value chain landscape? Help us shape it!
🗺 ❓What is the current ML value chain landscape? Help us shape it! 🗺 ❓What is the current ML value chain landscape? Help us shape it!

We offer you to shape an objective landscape of the ML Value Chain.

But we trust that TheSequence’s audience only can shape an accurate landscape of the ML Value Chain.

These processes tend to fall into six distinct stages of the ML value chain:Data collection:This is the beginning of the ML lifecycle.

We’ve made the first version of the ML value chain landscape specifically for this project (it’s a compilation of existing landscapes), which is meant to serve as a starting point and help you shape your ideal landscape.

Share your insights and shape the best possible version of this landscape that reflects today’s ML value chain processes and needs fairly and accurately.

2 недели, 5 дней назад @ thesequence.substack.com
Synced Review
последний пост 6 часов назад
Columbia U’s Infinitely Deep Probabilistic Model Adapts Its Complexity to the Data at Hand
Columbia U’s Infinitely Deep Probabilistic Model Adapts Its Complexity to the Data at Hand Columbia U’s Infinitely Deep Probabilistic Model Adapts Its Complexity to the Data at Hand

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6 часов назад @ medium.com
Transformers on Edge Devices?
Transformers on Edge Devices? Transformers on Edge Devices?

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3 дня, 17 часов назад @ medium.com
DeepMind’s MEME Agent Achieves Human-level Atari Game Performance 200x Faster Than Agent57
DeepMind’s MEME Agent Achieves Human-level Atari Game Performance 200x Faster Than Agent57 DeepMind’s MEME Agent Achieves Human-level Atari Game Performance 200x Faster Than Agent57

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Google Brain’s Vec2Text Models for Sentence Generation Excel in Universality, Diversity, Fluency &…
Google Brain’s Vec2Text Models for Sentence Generation Excel in Universality, Diversity, Fluency &… Google Brain’s Vec2Text Models for Sentence Generation Excel in Universality, Diversity, Fluency &…

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6 дней, 9 часов назад @ medium.com
DeepMind’s ‘Expert-Aware’ Data Augmentation Technique Enables Data-Efficient Learning from…
DeepMind’s ‘Expert-Aware’ Data Augmentation Technique Enables Data-Efficient Learning from… DeepMind’s ‘Expert-Aware’ Data Augmentation Technique Enables Data-Efficient Learning from…

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1 неделя назад @ medium.com
Peking U & Microsoft’s Knowledge Attribution Method Enables Editing Factual Knowledge in Pretrained…
Peking U & Microsoft’s Knowledge Attribution Method Enables Editing Factual Knowledge in Pretrained… Peking U & Microsoft’s Knowledge Attribution Method Enables Editing Factual Knowledge in Pretrained…

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1 неделя, 4 дня назад @ medium.com
DeepMind’s Model-Based Offline Options Framework Supports Automatic Skill & Behaviour Discovery…
DeepMind’s Model-Based Offline Options Framework Supports Automatic Skill & Behaviour Discovery… DeepMind’s Model-Based Offline Options Framework Supports Automatic Skill & Behaviour Discovery…

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1 неделя, 5 дней назад @ medium.com
CMU’s ASR2K Pipeline Recognizes Speech in 1909 Languages Without Audio
CMU’s ASR2K Pipeline Recognizes Speech in 1909 Languages Without Audio CMU’s ASR2K Pipeline Recognizes Speech in 1909 Languages Without Audio

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1 неделя, 6 дней назад @ medium.com
Toward a Turing Machine?
Toward a Turing Machine? Toward a Turing Machine?

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Meta AI & Inria Saclay Advance BCIs to Enable Natural Speech Decoding From Non-Invasive Brain…
Meta AI & Inria Saclay Advance BCIs to Enable Natural Speech Decoding From Non-Invasive Brain… Meta AI & Inria Saclay Advance BCIs to Enable Natural Speech Decoding From Non-Invasive Brain…

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DeepMind’s Selection-Inference Language Model System Generates Humanly Interpretable Reasoning…
DeepMind’s Selection-Inference Language Model System Generates Humanly Interpretable Reasoning… DeepMind’s Selection-Inference Language Model System Generates Humanly Interpretable Reasoning…

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Plan, Edit, Explain and Repeat: The PEER Collaborative Language Model Brings a Humanlike Process to…
Plan, Edit, Explain and Repeat: The PEER Collaborative Language Model Brings a Humanlike Process to… Plan, Edit, Explain and Repeat: The PEER Collaborative Language Model Brings a Humanlike Process to…

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3 недели, 4 дня назад @ medium.com
Princeton U & Adobe’s 3D-FM GAN Enables Precise 3D-Controllable Face Manipulation
Princeton U & Adobe’s 3D-FM GAN Enables Precise 3D-Controllable Face Manipulation Princeton U & Adobe’s 3D-FM GAN Enables Precise 3D-Controllable Face Manipulation

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Microsoft’s BEiT-3 Foundation Model: A ‘Big Convergence of Language, Vision, and Multimodal…
Microsoft’s BEiT-3 Foundation Model: A ‘Big Convergence of Language, Vision, and Multimodal… Microsoft’s BEiT-3 Foundation Model: A ‘Big Convergence of Language, Vision, and Multimodal…

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3 недели, 6 дней назад @ medium.com
CMU Details 6 Years of Contributions to the National Science Foundation- Funded DialPort Project…
CMU Details 6 Years of Contributions to the National Science Foundation- Funded DialPort Project… CMU Details 6 Years of Contributions to the National Science Foundation- Funded DialPort Project…

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4 недели назад @ medium.com
📓 Cool Blogs
ODS.ai Habr ODS.ai Habr
последний пост 2 недели назад
Новый запуск курса Natural Language Processing
Новый запуск курса Natural Language Processing Новый запуск курса Natural Language Processing

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

Мы делаем новый запуск курса Natural Language Processing.

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

Сам курс запускается в этом виде в пятый раз.

Ссылка будет в группе курса.

2 недели назад @ habr.com
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.

1 месяц, 2 недели назад @ habr.com
Эй-Яй, крипта, MLOps и командный пет-проджект
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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…

2 месяца, 4 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.

3 месяца, 2 недели 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.

3 месяца, 2 недели 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…

3 месяца, 3 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.

3 месяца, 4 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…

4 месяца 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…

5 месяцев 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().

5 месяцев, 2 недели 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…

5 месяцев, 4 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…

7 месяцев, 2 недели 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.

7 месяцев, 2 недели 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…

8 месяцев, 1 неделя 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, …

8 месяцев, 2 недели назад @ habr.com
Machine Learning Mastery
последний пост 1 неделя, 2 дня назад
The Transformer Model
The Transformer Model

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1 неделя, 2 дня назад @ machinelearningmastery.com
The Transformer Attention Mechanism
The Transformer Attention Mechanism

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1 неделя, 5 дней назад @ machinelearningmastery.com
Understanding Simple Recurrent Neural Networks In Keras
Understanding Simple Recurrent Neural Networks In Keras

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2 недели, 1 день назад @ machinelearningmastery.com
An Introduction To Recurrent Neural Networks And The Math That Powers Them
An Introduction To Recurrent Neural Networks And The Math That Powers Them

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2 недели, 4 дня назад @ machinelearningmastery.com
The Luong Attention Mechanism
The Luong Attention Mechanism

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3 недели, 1 день назад @ machinelearningmastery.com
The Bahdanau Attention Mechanism
The Bahdanau Attention Mechanism

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3 недели, 3 дня назад @ machinelearningmastery.com
Adding A Custom Attention Layer To Recurrent Neural Network In Keras
Adding A Custom Attention Layer To Recurrent Neural Network In Keras

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3 недели, 5 дней назад @ machinelearningmastery.com
Join Doug Turnbull’s ‘ML Powered Search’ Live Cohort
Join Doug Turnbull’s ‘ML Powered Search’ Live Cohort

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3 недели, 6 дней назад @ machinelearningmastery.com
A Tour of Attention-Based Architectures
A Tour of Attention-Based Architectures

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4 недели назад @ machinelearningmastery.com
The Attention Mechanism from Scratch
The Attention Mechanism from Scratch

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1 месяц назад @ machinelearningmastery.com
What is Attention?
What is Attention?

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1 месяц назад @ machinelearningmastery.com
A Bird’s Eye View of Research on Attention
A Bird’s Eye View of Research on Attention

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1 месяц назад @ machinelearningmastery.com
How to Calculate Precision, Recall, F1, and More for Deep Learning Models
How to Calculate Precision, Recall, F1, and More for Deep Learning Models

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1 месяц, 1 неделя назад @ machinelearningmastery.com
Last call: Stefan Krawcyzk’s ‘Mastering MLOps’ Live Cohort
Last call: Stefan Krawcyzk’s ‘Mastering MLOps’ Live Cohort

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1 месяц, 1 неделя назад @ machinelearningmastery.com
Why Initialize a Neural Network with Random Weights?
Why Initialize a Neural Network with Random Weights?

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1 месяц, 2 недели назад @ machinelearningmastery.com
ML in Production
последний пост 4 месяца, 3 недели 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.

4 месяца, 3 недели 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.

5 месяцев, 3 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…

6 месяцев, 1 неделя 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.

7 месяцев, 3 недели назад @ mlinproduction.com
Sorta Insightful Sorta Insightful
последний пост 1 месяц, 1 неделя назад
Seven Years Later
Seven Years Later Seven Years Later

This January, the team I was on won MIT Mystery Hunt, the biggest puzzlehunt of the year.

See, people don’t quite understand how long it takes to write Mystery Hunt.

markdown 414 2022 - 01 - 22 - mh - 2022. markdown 400 2022 - 04 - 15 - do - what - i - mean .

markdownI’m a bit surprised the ML-related post has fewer views than the Mystery Hunt post.

I’m guessing shades of what this post would have been will appear in other posts I write later.

1 месяц, 1 неделя назад @ alexirpan.com
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.

2 месяца, 2 недели 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.

4 месяца, 3 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.

5 месяцев, 2 недели 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.

8 месяцев, 1 неделя 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.

8 месяцев, 4 недели назад @ alexirpan.com
Lil'Log
последний пост 7 месяцев, 1 неделя 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.

7 месяцев, 1 неделя 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…

9 месяцев, 3 недели назад @ lilianweng.github.io
inFERENCe
последний пост 6 месяцев, 3 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…

6 месяцев, 3 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…

7 месяцев назад @ inference.vc
The Spectator
последний пост None
Off the Convex Path
последний пост 2 месяца, 1 неделя 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…

2 месяца, 1 неделя 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…

3 месяца, 3 недели 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…

8 месяцев, 3 недели назад @ offconvex.org
Jay Alammar
последний пост 6 месяцев, 3 недели 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.

6 месяцев, 3 недели 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…

8 месяцев, 3 недели назад @ jalammar.github.io
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 35 минут назад
/kdhht2334/ Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
/kdhht2334/ Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition /kdhht2334/ Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition

Since conventional FER schemes do not explicitly address the inter-identity variation of facial expressions, their neural network models still operate depending on facial identity.

This paper proposes to quantify the inter-identity variation by utilizing pairs of similar expressions explored through a specific matching process.

We formulate the identity matching process as an Optimal Transport (OT) problem.

Then, optimal identity matching to find the optimal flow with minimum transportation cost is performed by Sinkhorn-Knopp iteration.

The proposed matching method is not only easy to plug in to other models, but also requires only acceptable computational overhead.

35 минут назад @ paperswithcode.com
/sssunqing/ Exploring Example Influence in Continual Learning
/sssunqing/ Exploring Example Influence in Continual Learning /sssunqing/ Exploring Example Influence in Continual Learning

Continual Learning (CL) sequentially learns new tasks like human beings, with the goal to achieve better Stability (S, remembering past tasks) and Plasticity (P, adapting to new tasks).

Due to the fact that past training data is not available, it is valuable to explore the influence difference on S and P among training examples, which may improve the learning pattern towards better SP.

Inspired by Influence Function (IF), we first study example influence via adding perturbation to example weight and computing the influence derivation.

Moreover, we propose to fuse two kinds of example influence by solving a dual-objective optimization problem, and obtain a fused influence towards SP Pareto o…

35 минут назад @ paperswithcode.com
/yunlong10/ Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward
/yunlong10/ Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward /yunlong10/ Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward

Advertisement video editing aims to automatically edit advertising videos into shorter videos while retaining coherent content and crucial information conveyed by advertisers.

It mainly contains two stages: video segmentation and segment assemblage.

The existing method performs well at video segmentation stages but suffers from the problems of dependencies on extra cumbersome models and poor performance at the segment assemblage stage.

To address these problems, we propose M-SAN (Multi-modal Segment Assemblage Network) which can perform efficient and coherent segment assemblage task end-to-end.

Ablation experiments further verify that multi-modal representation and importance-coherence rewa…

35 минут назад @ paperswithcode.com
/matteoferrante/ VAESim: A probabilistic approach for self-supervised prototype discovery
/matteoferrante/ VAESim: A probabilistic approach for self-supervised prototype discovery /matteoferrante/ VAESim: A probabilistic approach for self-supervised prototype discovery

We propose an architecture for image stratification based on a conditional variational autoencoder.

The core of the method learns a set of prototypical vectors, each associated with a cluster.

Then, we reconstruct the sample based on a similarity measure between the sample embedding and the prototypical vectors of the clusters.

We test our approach on the MNIST-handwritten digit dataset and on a medical benchmark dataset called PneumoniaMNIST.

We also demonstrate how our model outperforms current, end-to-end models for unsupervised stratification.

35 минут назад @ paperswithcode.com
/chuyan99/ Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Tasks
/chuyan99/ Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Tasks /chuyan99/ Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Tasks

Self-supervised learning (SSL) has achieved remarkable performance on various medical imaging tasks by dint of priors from massive unlabeled data.

However, for a specific downstream task, there is still a lack of an instruction book on how to select suitable pretext tasks and implementation details.

In this work, we first review the latest applications of self-supervised methods in the field of medical imaging analysis.

Based on the experimental results, potential guidelines are presented for self-supervised pretraining in medical imaging.

Finally, we discuss future research directions and raise issues to be aware of when designing new SSL methods and paradigms.

35 минут назад @ paperswithcode.com
/realsarm/ Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique
/realsarm/ Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique /realsarm/ Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique

Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult.

In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data.

Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN), the models face some limitations to implement in clinical practice.

Therefore, in this study, we propose a transformer-based fine-grained named entity recognition (NER) architecture for clinical information e…

35 минут назад @ paperswithcode.com
/danielegrandi-adsk/ Material Prediction for Design Automation Using Graph Representation Learning
/danielegrandi-adsk/ Material Prediction for Design Automation Using Graph Representation Learning /danielegrandi-adsk/ Material Prediction for Design Automation Using Graph Representation Learning

Successful material selection is critical in designing and manufacturing products for design automation.

To enable this, we introduce a graph representation learning framework that supports the material prediction of bodies in assemblies.

We formulate the material selection task as a node-level prediction task over the assembly graph representation of CAD models and tackle it using Graph Neural Networks (GNNs).

The proposed framework can scale to large datasets and incorporate designers' knowledge into the learning process.

These capabilities allow the framework to serve as a recommendation system for design automation and a baseline for future work, narrowing the gap between human designer…

35 минут назад @ paperswithcode.com
/hardikparwana/ FORESEE: Model-based Reinforcement Learning using Unscented Transform with application to Tuning of Control Barrier Functions
/hardikparwana/ FORESEE: Model-based Reinforcement Learning using Unscented Transform with application to Tuning of Control Barrier Functions /hardikparwana/ FORESEE: Model-based Reinforcement Learning using Unscented Transform with application to Tuning of Control Barrier Functions

In this paper, we introduce a novel online model-based reinforcement learning algorithm that uses Unscented Transform to propagate uncertainty for the prediction of the future reward.

Our method, depending on the number of sigma points employed, can propagate either mean and covariance with minimal points, or higher-order moments with more points similarly to Monte Carlo.

Finally, we propose gradient descent inspired by Sequential Quadratic Programming to update policy parameters in the presence of state constraints.

The first one designs a stabilizing controller for the cart-pole problem when the dynamics is known with state-dependent uncertainty.

The second example, following up on our pr…

35 минут назад @ paperswithcode.com
/cram3r95/ Exploring Attention GAN for Vehicle Motion Prediction
/cram3r95/ Exploring Attention GAN for Vehicle Motion Prediction /cram3r95/ Exploring Attention GAN for Vehicle Motion Prediction

These ADS are expected to be driven in highly dynamic environments with full autonomy, and a reliability greater than human beings.

Current state-of-the-art models are typically based on Recurrent, Graph and Convolutional networks, achieving noticeable results in the context of vehicle prediction.

In this paper we explore the influence of attention in generative models for motion prediction, considering both physical and social context to compute the most plausible trajectories.

We first encode the past trajectories using a LSTM network, which serves as input to a Multi-Head Self-Attention module that computes the social context.

We validate our method using the Argoverse Motion Forecasting…

35 минут назад @ paperswithcode.com
/wpeebles/ Learning to Learn with Generative Models of Neural Network Checkpoints
/wpeebles/ Learning to Learn with Generative Models of Neural Network Checkpoints /wpeebles/ Learning to Learn with Generative Models of Neural Network Checkpoints

We explore a data-driven approach for learning to optimize neural networks.

We construct a dataset of neural network checkpoints and train a generative model on the parameters.

At test time, it can optimize neural networks with unseen parameters for downstream tasks in just one update.

We find that our approach successfully generates parameters for a wide range of loss prompts.

We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.

35 минут назад @ paperswithcode.com
/zmzhang1998/ Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset
/zmzhang1998/ Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset /zmzhang1998/ Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset

In recent years, real image super-resolution (SR) has achieved promising results due to the development of SR datasets and corresponding real SR methods.

In contrast, the field of real video SR is lagging behind, especially for real raw videos.

Considering the superiority of raw image SR over sRGB image SR, we construct a real-world raw video SR (Real-RawVSR) dataset and propose a corresponding SR method.

We utilize two DSLR cameras and a beam-splitter to simultaneously capture low-resolution (LR) and high-resolution (HR) raw videos with 2x, 3x, and 4x magnifications.

Experimental results demonstrate that the proposed method outperforms benchmark real and synthetic video SR methods with eit…

35 минут назад @ paperswithcode.com
/hellloxiaotian/ A heterogeneous group CNN for image super-resolution
/hellloxiaotian/ A heterogeneous group CNN for image super-resolution /hellloxiaotian/ A heterogeneous group CNN for image super-resolution

However, these CNNs often achieve poor robustness for image super-resolution (SR) under complex scenes.

In this paper, we present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image.

Specifically, each heterogeneous group block (HGB) of HGSRCNN uses a heterogeneous architecture containing a symmetric group convolutional block and a complementary convolutional block in a parallel way to enhance internal and external relations of different channels for facilitating richer low-frequency structure information of different types.

To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to a…

35 минут назад @ paperswithcode.com
/jylins/ Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization
/jylins/ Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization /jylins/ Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization

Outlier detection tasks have been playing a critical role in AI safety.

Observations show that deep neural network classifiers usually tend to incorrectly classify out-of-distribution (OOD) inputs into in-distribution classes with high confidence.

In this paper, we propose an alternative probabilistic paradigm that is both practically useful and theoretically viable for the OOD detection tasks.

Specifically, we estimate the statistical dependence between inlier and outlier data through the Hilbert-Schmidt Independence Criterion (HSIC), and we penalize such metric during training.

Empirical results show that our method is effective and robust for OOD detection on various benchmarks.

35 минут назад @ paperswithcode.com
/mediabrain-sjtu/ Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
/mediabrain-sjtu/ Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps /mediabrain-sjtu/ Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps

Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication.

It inevitably results in a fundamental trade-off between perception performance and communication bandwidth.

To tackle this bottleneck issue, we propose a spatial confidence map, which reflects the spatial heterogeneity of perceptual information.

Based on this novel spatial confidence map, we propose Where2comm, a communication-efficient collaborative perception framework.

Where2comm consistently outperforms previous methods; for example, it achieves more than $100,000 \times$ lower communication volume and s…

35 минут назад @ paperswithcode.com
/poppinace/ SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
/poppinace/ SAPA: Similarity-Aware Point Affiliation for Feature Upsampling /poppinace/ SAPA: Similarity-Aware Point Affiliation for Feature Upsampling

We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity.

By rethinking point affiliation, we present a generic formulation for generating upsampling kernels.

The key idea of our formulation is to generate similarity-aware kernels by comparing the similarity between each encoder feature point and the spatially associated local region of decoder features.

In this way, the encoder feature point can function as a cue to inform the semantic cluster of upsampled feature points.

To embody the formulation, we further instantiate a lightweight upsampli…

35 минут назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 35 минут назад
/yajunbaby/ TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance
/yajunbaby/ TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance /yajunbaby/ TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance

Automatic traffic accidents detection has appealed to the machine vision community due to its implications on the development of autonomous intelligent transportation systems (ITS) and importance to traffic safety.

Most previous studies on efficient analysis and prediction of traffic accidents, however, have used small-scale datasets with limited coverage, which limits their effect and applicability.

Existing datasets in traffic accidents are either small-scale, not from surveillance cameras, not open-sourced, or not built for freeway scenes.

An open-sourced datasets targeting on freeway traffic accidents collected from surveillance cameras is in great need and of practical importance.

Afte…

35 минут назад @ paperswithcode.com
/hellloxiaotian/ Multi-stage image denoising with the wavelet transform
/hellloxiaotian/ Multi-stage image denoising with the wavelet transform /hellloxiaotian/ Multi-stage image denoising with the wavelet transform

Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information.

However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which may cause training difficulty.

In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and residual block (RB).

DCB uses a dynamic convolution to dynamically adjust parameters of several convolutions for making a tradeoff between denoising performance and computational costs.

WEB uses…

35 минут назад @ paperswithcode.com
/cvmi-lab/ Rethinking Resolution in the Context of Efficient Video Recognition
/cvmi-lab/ Rethinking Resolution in the Context of Efficient Video Recognition /cvmi-lab/ Rethinking Resolution in the Context of Efficient Video Recognition

In this paper, we empirically study how to make the most of low-resolution frames for efficient video recognition.

A major concern is the poor recognition accuracy on low-resolution frames.

We thus start by analyzing the underlying causes of performance degradation on low-resolution frames.

Our work shows that ResKD is a simple but effective method to boost recognition accuracy on low-resolution frames.

The results suggest ResKD can serve as a general inference acceleration method for state-of-the-art video recognition.

35 минут назад @ paperswithcode.com
/law-ai/ Legal Case Document Similarity: You Need Both Network and Text
/law-ai/ Legal Case Document Similarity: You Need Both Network and Text /law-ai/ Legal Case Document Similarity: You Need Both Network and Text

We incorporate domain knowledge for legal document similarity into Hier-SPCNet, thereby obtaining state-of-the-art results for network-based legal document similarity.

Both textual and network similarity provide important signals for legal case similarity; but till now, only trivial attempts have been made to unify the two signals.

In this work, we apply several methods for combining textual and network information for estimating legal case similarity.

Our experiments establish that our proposed network-based methods significantly improve the correlation with domain experts' opinion when compared to the existing methods for network-based legal document similarity.

Our best-performing combin…

35 минут назад @ paperswithcode.com
/yumoxu/ Text Summarization with Oracle Expectation
/yumoxu/ Text Summarization with Oracle Expectation /yumoxu/ Text Summarization with Oracle Expectation

Extractive summarization produces summaries by identifying and concatenating the most important sentences in a document.

Since most summarization datasets do not come with gold labels indicating whether document sentences are summary-worthy, different labeling algorithms have been proposed to extrapolate oracle extracts for model training.

To alleviate both issues, we propose a simple yet effective labeling algorithm that creates soft, expectation-based sentence labels.

We define a new learning objective for extractive summarization which incorporates learning signals from multiple oracle summaries and prove it is equivalent to estimating the oracle expectation for each document sentence.

W…

35 минут назад @ paperswithcode.com
/donlapark/ An Explainable Machine Learning Approach to Visual-Interactive Labeling: A Case Study on Non-communicable Disease Data
/donlapark/ An Explainable Machine Learning Approach to Visual-Interactive Labeling: A Case Study on Non-communicable Disease Data /donlapark/ An Explainable Machine Learning Approach to Visual-Interactive Labeling: A Case Study on Non-communicable Disease Data

We introduce a new visual-interactive tool: Explainable Labeling Assistant (XLabel) that takes an explainable machine learning approach to data labeling.

The main component of XLabel is the Explainable Boosting Machine (EBM), a predictive model that can calculate the contribution of each input feature towards the final prediction.

As a case study, we use XLabel to predict the labels of four non-communicable diseases (NCDs): diabetes, hypertension, chronic kidney disease, and dyslipidemia.

We demonstrate that EBM is an excellent choice of predictive model by comparing it against a rule-based and four other machine learning models.

By performing 5-fold cross-validation on 427 medical records,…

35 минут назад @ paperswithcode.com
/guillermocarbajal/ Rethinking Motion Deblurring Training: A Segmentation-Based Method for Simulating Non-Uniform Motion Blurred Images
/guillermocarbajal/ Rethinking Motion Deblurring Training: A Segmentation-Based Method for Simulating Non-Uniform Motion Blurred Images /guillermocarbajal/ Rethinking Motion Deblurring Training: A Segmentation-Based Method for Simulating Non-Uniform Motion Blurred Images

Successful training of end-to-end deep networks for real motion deblurring requires datasets of sharp/blurred image pairs that are realistic and diverse enough to achieve generalization to real blurred images.

Secondly, we propose an efficient procedural methodology to generate sharp/blurred image pairs, based on a simple yet effective model for the formation of blurred images.

This allows generating virtually unlimited realistic and diverse training pairs.

We demonstrate the effectiveness of the proposed dataset by training existing deblurring architectures on the simulated pairs and evaluating them across four standard datasets of real blurred images.

We observed superior generalization p…

35 минут назад @ paperswithcode.com
/compostieai/ Device-friendly Guava fruit and leaf disease detection using deep learning
/compostieai/ Device-friendly Guava fruit and leaf disease detection using deep learning /compostieai/ Device-friendly Guava fruit and leaf disease detection using deep learning

This work presents a deep learning-based plant disease diagnostic system using images of fruits and leaves.

Hitherto model accuracy has been the focus for such applications and model optimization has not been accounted for the model to be applicable to end-user devices.

Two model quantization techniques such as float16 and dynamic range quantization have been applied to the five state-of-the-art CNN architectures.

The study shows that the quantized GoogleNet model achieved the size of 0.143 MB with an accuracy of 97%, which is the best candidate model considering the size criterion.

The EfficientNet model achieved the size of 4.2MB with an accuracy of 99%, which is the best model considerin…

35 минут назад @ paperswithcode.com
/joeljang/ Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts
/joeljang/ Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts /joeljang/ Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts

Previous work has shown that there exists a scaling law between the size of Language Models (LMs) and their zero-shot performance on different downstream NLP tasks.

In this work, we show that this phenomenon does not hold when evaluating large LMs on tasks with negated prompts, but instead shows an inverse scaling law.

We evaluate 9 different tasks with negated prompts on (1) pretrained LMs (OPT & GPT-3) of varying sizes (125M - 175B), (2) LMs further pretrained to generalize to novel prompts (InstructGPT), (3) LMs provided with few-shot examples, and (4) LMs fine-tuned specifically on negated prompts; all LM types perform worse on negated prompts as they scale and show a huge performance g…

36 минут назад @ paperswithcode.com
/junweiliang/ Multi-dataset Training of Transformers for Robust Action Recognition
/junweiliang/ Multi-dataset Training of Transformers for Robust Action Recognition /junweiliang/ Multi-dataset Training of Transformers for Robust Action Recognition

We study the task of robust feature representations, aiming to generalize well on multiple datasets for action recognition.

Although we have witnessed great progress for video action recognition in the past decade, it remains challenging yet valuable how to train a single model that can perform well across multiple datasets.

Here, we propose a novel multi-dataset training paradigm, MultiTrain, with the design of two new loss terms, namely informative loss and projection loss, aiming to learn robust representations for action recognition.

In particular, the informative loss maximizes the expressiveness of the feature embedding while the projection loss for each dataset mines the intrinsic re…

36 минут назад @ paperswithcode.com
/lpq29743/ Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation
/lpq29743/ Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation /lpq29743/ Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation

The task of empathetic response generation aims to understand what feelings a speaker expresses on his/her experiences and then reply to the speaker appropriately.

To solve the task, it is essential to model the content-emotion duality of a dialogue, which is composed of the content view (i.e., what personal experiences are described) and the emotion view (i.e., the feelings of the speaker on these experiences).

To this end, we design a framework to model the Content-Emotion Duality (CEDual) via disentanglement for empathetic response generation.

With disentanglement, we encode the dialogue history from both the content and emotion views, and then generate the empathetic response based on t…

36 минут назад @ paperswithcode.com
/eth42/ On Projections to Linear Subspaces
/eth42/ On Projections to Linear Subspaces /eth42/ On Projections to Linear Subspaces

The merit of projecting data onto linear subspaces is well known from, e.g., dimension reduction.

One key aspect of subspace projections, the maximum preservation of variance (principal component analysis), has been thoroughly researched and the effect of random linear projections on measures such as intrinsic dimensionality still is an ongoing effort.

In this paper, we investigate the less explored depths of linear projections onto explicit subspaces of varying dimensionality and the expectations of variance that ensue.

The result is a new family of bounds for Euclidean distances and inner products.

We showcase the quality of these bounds as well as investigate the intimate relation to int…

36 минут назад @ paperswithcode.com
/alibaba/ EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems
/alibaba/ EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems /alibaba/ EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems

We present EasyRec, an easy-to-use, extendable and efficient recommendation framework for building industrial recommendation systems.

Our EasyRec framework is superior in the following aspects: first, EasyRec adopts a modular and pluggable design pattern to reduce the efforts to build custom models; second, EasyRec implements hyper-parameter optimization and feature selection algorithms to improve model performance automatically; third, EasyRec applies online learning to fast adapt to the ever-changing data distribution.

The code is released: https://github.com/alibaba/EasyRec.

PDFAbstract

2 часа назад @ paperswithcode.com
/dvlab-research/ Generalized Parametric Contrastive Learning
/dvlab-research/ Generalized Parametric Contrastive Learning /dvlab-research/ Generalized Parametric Contrastive Learning

In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo) which works well on both imbalanced and balanced data.

Based on theoretical analysis, we observe that supervised contrastive loss tends to bias high-frequency classes and thus increases the difficulty of imbalanced learning.

We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective.

Further, we analyze our GPaCo/PaCo loss under a balanced setting.

On full ImageNet, models from CNNs to vision transformers trained with GPaCo loss show better generalization performance and stronger robustness compared with MAE models.

2 часа назад @ paperswithcode.com
/wanlunsec/ The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices
/wanlunsec/ The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices /wanlunsec/ The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices

Existing defenses usually rely on the assumption of the universal backdoor setting in which poisoned samples share the same uniform trigger.

In this work, we propose a novel technique, Beatrix (backdoor detection via Gram matrix).

Beatrix utilizes Gram matrix to capture not only the feature correlations but also the appropriately high-order information of the representations.

By learning class-conditional statistics from activation patterns of normal samples, Beatrix can identify poisoned samples by capturing the anomalies in activation patterns.

To further improve the performance in identifying target labels, Beatrix leverages kernel-based testing without making any prior assumptions on re…

3 часа назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 35 минут назад
/qu-gg/ Neural State-Space Modeling with Latent Causal-Effect Disentanglement
/qu-gg/ Neural State-Space Modeling with Latent Causal-Effect Disentanglement /qu-gg/ Neural State-Space Modeling with Latent Causal-Effect Disentanglement

Despite substantial progress in deep learning approaches to time-series reconstruction, no existing methods are designed to uncover local activities with minute signal strength due to their negligible contribution to the optimization loss.

Such local activities however can signify important abnormal events in physiological systems, such as an extra foci triggering an abnormal propagation of electrical waves in the heart.

We discuss a novel technique for reconstructing such local activity that, while small in signal strength, is the cause of subsequent global activities that have larger signal strength.

Our central innovation is to approach this by explicitly modeling and disentangling how t…

3 часа назад @ paperswithcode.com
/mv-lab/ Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
/mv-lab/ Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration /mv-lab/ Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration

While most state-of-the-art image restoration methods are based on convolutional neural networks, other transformers-based methods such as SwinIR, show impressive performance on these tasks.

In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario.

Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data.

We conduct experiments on three representative tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image …

12 часов назад @ paperswithcode.com
/summerparadise-0922/ Modular Degradation Simulation and Restoration for Under-Display Camera
/summerparadise-0922/ Modular Degradation Simulation and Restoration for Under-Display Camera /summerparadise-0922/ Modular Degradation Simulation and Restoration for Under-Display Camera

Although this issue can be tackled by image restoration networks, these networks require large-scale image pairs for training.

To this end, we propose a modular network dubbed MPGNet trained using the generative adversarial network (GAN) framework for simulating UDC imaging.

Specifically, we note that the UDC imaging degradation process contains brightness attenuation, blurring, and noise corruption.

Thus we model each degradation with a characteristic-related modular network, and all modular networks are cascaded to form the generator.

Together with a pixel-wise discriminator and supervised loss, we can train the generator to simulate the UDC imaging degradation process.

12 часов назад @ paperswithcode.com
/fiveai/ Query-based Hard-Image Retrieval for Object Detection at Test Time
/fiveai/ Query-based Hard-Image Retrieval for Object Detection at Test Time /fiveai/ Query-based Hard-Image Retrieval for Object Detection at Test Time

There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory.

In real-world applications such as autonomous driving, it is also crucial to characterise potential failures beyond simple requirements of detection performance.

For example, a missed detection of a pedestrian close to an ego vehicle will generally require closer inspection than a missed detection of a car in the distance.

We show experimentally that it can be applied successfully to a wide variety of queries for which it can reliably identify hard images for a given detector without any labelled data.

We provide results on ranking a…

19 часов назад @ paperswithcode.com
/MengZephyr/ Motion Guided Deep Dynamic 3D Garments
/MengZephyr/ Motion Guided Deep Dynamic 3D Garments /MengZephyr/ Motion Guided Deep Dynamic 3D Garments

Realistic dynamic garments on animated characters have many AR/VR applications.

While authoring such dynamic garment geometry is still a challenging task, data-driven simulation provides an attractive alternative, especially if it can be controlled simply using the motion of the underlying character.

In this work, we focus on motion guided dynamic 3D garments, especially for loose garments.

In a data-driven setup, we first learn a generative space of plausible garment geometries.

We demonstrate plausible generalization to unseen body shapes and motion inputs, and show improvements over multiple state-of-the-art alternatives.

21 час назад @ paperswithcode.com
/harsh9524/ Capsule Network based Contrastive Learning of Unsupervised Visual Representations
/harsh9524/ Capsule Network based Contrastive Learning of Unsupervised Visual Representations /harsh9524/ Capsule Network based Contrastive Learning of Unsupervised Visual Representations

Capsule Networks have shown tremendous advancement in the past decade, outperforming the traditional CNNs in various task due to it's equivariant properties.

With the use of vector I/O which provides information of both magnitude and direction of an object or it's part, there lies an enormous possibility of using Capsule Networks in unsupervised learning environment for visual representation tasks such as multi class image classification.

In this paper, we propose Contrastive Capsule (CoCa) Model which is a Siamese style Capsule Network using Contrastive loss with our novel architecture, training and testing algorithm.

We evaluate the model on unsupervised image classification CIFAR-10 data…

22 часа назад @ paperswithcode.com
/vuoristo/ An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
/vuoristo/ An Investigation of the Bias-Variance Tradeoff in Meta-Gradients /vuoristo/ An Investigation of the Bias-Variance Tradeoff in Meta-Gradients

Meta-gradients provide a general approach for optimizing the meta-parameters of reinforcement learning (RL) algorithms.

Estimation of meta-gradients is central to the performance of these meta-algorithms, and has been studied in the setting of MAML-style short-horizon meta-RL problems.

Meanwhile, meta-gradient estimation has been studied less in the important long-horizon setting, where backpropagation through the full inner optimization trajectories is not feasible.

We study the bias and variance tradeoff arising from truncated backpropagation and sampling correction, and additionally compare to evolution strategies, which is a recently popular alternative strategy to long-horizon meta-lea…

1 день, 1 час назад @ paperswithcode.com
/wangkaiwan/ Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers
/wangkaiwan/ Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers /wangkaiwan/ Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers

For Head and Neck Cancers (HNC) patient management, automatic gross tumor volume (GTV) segmentation and accurate pre-treatment cancer recurrence prediction are of great importance to assist physicians in designing personalized management plans, which have the potential to improve the treatment outcome and quality of life for HNC patients.

In this paper, we developed an automated primary tumor (GTVp) and lymph nodes (GTVn) segmentation method based on combined pre-treatment positron emission tomography/computed tomography (PET/CT) scans of HNC patients.

We extracted radiomics features from the segmented tumor volume and constructed a multi-modality tumor recurrence-free survival (RFS) predic…

1 день, 1 час назад @ paperswithcode.com
/milidris/ Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models
/milidris/ Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models /milidris/ Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models

Robustness studies of black-box models is recognized as a necessary task for numerical models based on structural equations and predictive models learned from data.

These studies must assess the model's robustness to possible misspecification of regarding its inputs (e.g., covariate shift).

The study of black-box models, through the prism of uncertainty quantification (UQ), is often based on sensitivity analysis involving a probabilistic structure imposed on the inputs, while ML models are solely constructed from observed data.

Our work aim at unifying the UQ and ML interpretability approaches, by providing relevant and easy-to-use tools for both paradigms.

Numerical experiments on real cas…

1 день, 1 час назад @ paperswithcode.com
/helicopt/ Towards Frame Rate Agnostic Multi-Object Tracking
/helicopt/ Towards Frame Rate Agnostic Multi-Object Tracking /helicopt/ Towards Frame Rate Agnostic Multi-Object Tracking

Multi-Object Tracking (MOT) is one of the most fundamental computer vision tasks which contributes to a variety of video analysis applications.

In fact, we empirically find that the accuracy of all recent state-of-the-art trackers drops dramatically when the input frame rate changes.

For a more intelligent tracking solution, we shift the attention of our research work to the problem of Frame Rate Agnostic MOT (FraMOT).

Specifically, we propose a Frame Rate Agnostic Association Module (FAAM) that infers and encodes the frame rate information to aid identity matching across multi-frame-rate inputs, improving the capability of the learned model in handling complex motion-appearance relations i…

1 день, 1 час назад @ paperswithcode.com
/hepta-col/ Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model
/hepta-col/ Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model /hepta-col/ Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model

Fight detection in videos is an emerging deep learning application with today's prevalence of surveillance systems and streaming media.

In this paper, we propose a simple but effective method that solves the task from a new perspective: we design the fight detection model as a composition of an action-aware feature extractor and an anomaly score generator.

Extensive experiments on a publicly available large-scale dataset, UBI-Fights, demonstrate the effectiveness of our method, and the performance on the dataset exceeds several previous state-of-the-art approaches.

Furthermore, we collect a new dataset, VFD-2000, that specializes in video fight detection, with a larger scale and more scenar…

1 день, 1 час назад @ paperswithcode.com
/junfish/ Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis
/junfish/ Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis /junfish/ Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task.

Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI.

In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls.

Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear re…

1 день, 1 час назад @ paperswithcode.com
/vips4/ I-SPLIT: Deep Network Interpretability for Split Computing
/vips4/ I-SPLIT: Deep Network Interpretability for Split Computing /vips4/ I-SPLIT: Deep Network Interpretability for Split Computing

This work makes a substantial step in the field of split computing, i.e., how to split a deep neural network to host its early part on an embedded device and the rest on a server.

So far, potential split locations have been identified exploiting uniquely architectural aspects, i.e., based on the layer sizes.

Under this paradigm, the efficacy of the split in terms of accuracy can be evaluated only after having performed the split and retrained the entire pipeline, making an exhaustive evaluation of all the plausible splitting points prohibitive in terms of time.

It follows that a split should be applied right after a layer with a high density of important neurons, in order to preserve the in…

1 день, 1 час назад @ paperswithcode.com
/idkiro/ Rethinking Performance Gains in Image Dehazing Networks
/idkiro/ Rethinking Performance Gains in Image Dehazing Networks /idkiro/ Rethinking Performance Gains in Image Dehazing Networks

Image dehazing is an active topic in low-level vision, and many image dehazing networks have been proposed with the rapid development of deep learning.

Although these networks' pipelines work fine, the key mechanism to improving image dehazing performance remains unclear.

For this reason, we do not target to propose a dehazing network with fancy modules; rather, we make minimal modifications to popular U-Net to obtain a compact dehazing network.

As a result, with a significantly reduced overhead, gUNet is superior to state-of-the-art methods on multiple image dehazing datasets.

Finally, we verify these key designs to the performance gain of image dehazing networks through extensive ablation…

1 день, 1 час назад @ paperswithcode.com
/pbelcak/ A Neural Model for Regular Grammar Induction
/pbelcak/ A Neural Model for Regular Grammar Induction /pbelcak/ A Neural Model for Regular Grammar Induction

Grammatical inference is a classical problem in computational learning theory and a topic of wider influence in natural language processing.

We treat grammars as a model of computation and propose a novel neural approach to induction of regular grammars from positive and negative examples.

Our model is fully explainable, its intermediate results are directly interpretable as partial parses, and it can be used to learn arbitrary regular grammars when provided with sufficient data.

Our method consistently attains high recall and precision scores across a range of tests of varying complexity.

We make the detailed results and code readily available.

1 день, 1 час назад @ paperswithcode.com
💼 University and corporation labs
DeepMind DeepMind
последний пост через 2 месяца
On the Expressivity of Markov Reward
On the Expressivity of Markov Reward On the Expressivity of Markov Reward

For a Markov reward function to express this task, it would need to make these two policies strictly higher in value than all other deterministic policies.

However, there is no such Markov reward function: the optimality of a single “move clockwise” action will depend on whether the agent was already moving in that direction in the past.

Since the reward function must be Markov, it cannot convey this kind of information.

Similar examples demonstrate that Markov reward cannot capture every policy order and trajectory order, too.

Further, if there is a reward function that captures the given task, we would ideally like to be able to output such a reward function.

через 2 месяца @ deepmind.com
Supporting the next generation of AI leaders
Supporting the next generation of AI leaders Supporting the next generation of AI leaders

These barriers not only contribute to the existing attainment gap, they directly impact the number of opportunities students have to pursue a career in STEM related fields, including AI, down the line.

Developing new AI resources with the Raspberry Pi FoundationÂWe will be working closely with the Raspberry Pi Foundation, a charity that promotes the study of computing and digital technologies, to develop new AI-focused resources including lesson plans for students and training for teachers.

By focusing on education at an early age, there’s an opportunity to help break down long-standing barriers that have facilitated a system of inequalities.

Amplifying the reach of existing programmesÂDe…

1 день, 5 часов назад @ deepmind.com
Building safer dialogue agents
Building safer dialogue agents Building safer dialogue agents

However, dialogue agents powered by LLMs can express inaccurate or invented information, use discriminatory language, or encourage unsafe behaviour.

To create safer dialogue agents, we need to be able to learn from human feedback.

Applying reinforcement learning based on input from research participants, we explore new methods for training dialogue agents that show promise for a safer system.

Sparrow is a research model and proof of concept, designed with the goal of training dialogue agents to be more helpful, correct, and harmless.

Sparrow is a significant step forward in understanding how to train dialogue agents to be more useful and safer.

5 дней, 5 часов назад @ deepmind.com
How our principles helped define AlphaFold’s release
How our principles helped define AlphaFold’s release How our principles helped define AlphaFold’s release

Our Operating Principles have come to define both our commitment to prioritising widespread benefit, as well as the areas of research and applications we refuse to pursue.

From principles to practiceWritten principles are only part of the puzzle – how they’re put into practice is key.

A major release of protein structure predictions in partnership with EMBL-EBI (EMBL’s European Bioinformatics Institute), the established community leader.

As a public institution, EMBL-EBI enables anyone to look up protein structure predictions as easily as a Google search.

As a public institution, EMBL-EBI enables anyone to look up protein structure predictions as easily as a Google search.

1 неделя, 6 дней назад @ deepmind.com
Maximising the impact of our breakthroughs
Maximising the impact of our breakthroughs Maximising the impact of our breakthroughs

Applying our AI research to help enrich the lives of billions of people around the worldBuilding useful products with new technologies has always been one of my greatest joys.

Taking research out of the labMy main focus as CBO is on taking our cutting-edge research breakthroughs and matching our technologies to solving everyday business problems.

I’m often asked, as a future-facing research organisation, why it’s important to work on global challenges that impact people every day?

that makes DeepMind so special is our ability to bridge leading AI research to hundreds, if not thousands, of AI-ready problems that impact billions of people.

And as we go along this journey, we’re continuo…

2 недели, 4 дня назад @ deepmind.com
My journey from DeepMind intern to mentor
My journey from DeepMind intern to mentor My journey from DeepMind intern to mentor

Former intern turned intern manager, Richard Everett, describes his journey to DeepMind, sharing tips and advice for aspiring DeepMinders.

However, after working on several research projects with my professors, I developed a taste for research and decided to continue on towards a PhD.

Can you describe the internship interview process?

Today's interns can expect the entire process to last just a few months, which includes a technical and a team interview.

Any tips for aspiring DeepMind interns?

2 недели, 5 дней назад @ deepmind.com
In conversation with artificial intelligence: aligning language models with human values
In conversation with artificial intelligence: aligning language models with human values In conversation with artificial intelligence: aligning language models with human values

Recent breakthroughs in AI research have led to the creation of conversational agents that are able to communicate with humans in nuanced ways.

These shortcomings limit the productive use of conversational agents in applied settings and draw attention to the way in which they fall short of certain communicative ideals.

Persistent Anti-Muslim Bias in Large Language Models.

"On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?."

Challenges in Detoxifying Language Models.

3 недели назад @ deepmind.com
In conversation with AI: building better language models
In conversation with AI: building better language models In conversation with AI: building better language models

New research drawing upon pragmatics and philosophy proposes ways to align conversational agents with human values.

Recent breakthroughs in AI research have led to the creation of conversational agents that are able to communicate with humans in nuanced ways.

Persistent Anti-Muslim Bias in Large Language Models.

"On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?."

Challenges in Detoxifying Language Models.

3 недели назад @ deepmind.com
From motor control to embodied intelligence
From motor control to embodied intelligence From motor control to embodied intelligence

Distilling data into controllable motor primitives using NPMPAn NPMP is a general-purpose motor control module that translates short-horizon motor intentions to low-level control signals, and it’s trained offline or via RL by imitating motion capture (MoCap) data, recorded with trackers on humans or animals performing motions of interest.

This low-level controller can then be used as a plug-and-play motor control module on a new task (right).

The NPMP allowed us to observe a similar effect but in a scenario that required significantly more advanced motor control.

The players exhibit both agile high-frequency motor control and long-term decision-making that involves anticipation of teammat…

3 недели, 6 дней назад @ deepmind.com
Advancing conservation with AI-based facial recognition of turtles
Advancing conservation with AI-based facial recognition of turtles Advancing conservation with AI-based facial recognition of turtles

Inspired by Zindi’s bounding box turtle challenge, we landed on a project with the potential for real impact: turtle facial recognition.ÂBiologists consider turtles to be an indicator species.

To help solve some of these challenges, we launched an ML challenge called Turtle Recall.

Example of image data for four turtles taken from the tutorial colab notebook.

Differences in lighting, scale, background, pose, and similarities between turtles added to the complexity of the prediction challenge.

Given the additional challenge of keeping a turtle still enough to locate their tag, the Turtle Recall challenge aimed to circumvent these problems with turtle facial recognition.

1 месяц назад @ deepmind.com
Discovering when an agent is present in a system
Discovering when an agent is present in a system Discovering when an agent is present in a system

New, formal definition of agency gives clear principles for causal modelling of AI agents and the incentives they face.

By relating training setups to the incentives that shape agent behaviour, CIDs help illuminate potential risks before training an agent and can inspire better agent designs.

Causal discovery of agentsCausal discovery infers a causal graph from experiments involving interventions.

Our second algorithm transforms this mechanised causal graph to a game graph:Algorithm 2 takes as input a mechanised causal graph and maps it to a game graph.

Our third algorithm transforms the game graph into a mechanised causal graph, allowing us to translate between the game and mechanised caus…

1 месяц, 1 неделя назад @ deepmind.com
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!)

1 месяц, 2 недели 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 месяца 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…

2 месяца, 1 неделя назад @ deepmind.com
Perceiver AR: general-purpose, long-context autoregressive generation
Perceiver AR: general-purpose, long-context autoregressive generation Perceiver AR: general-purpose, long-context autoregressive generation

Perceiver AR can be trained for end-to-end autoregressive generation, all while making use of very long input sequences.

For example, we find that a 60-layer Perceiver AR with context length 8192 outperforms a 42-layer Transformer-XL on a book-length generation task, while running faster in real wall-clock terms.

On standard, long-context image (ImageNet 64x64), language (PG-19), and music (MAESTRO) generation benchmarks, Perceiver AR produces state-of-the-art results.

This makes Perceiver AR easy to apply to settings that don’t have a natural left-to-right ordering, such as data like images, with structure that spans more than one dimension.

Using a dataset of piano music, we trained Per…

2 месяца, 1 неделя назад @ deepmind.com
Google
последний пост 3 дня, 13 часов назад
Building a Machine Learning Platform with Kubeflow and Ray on Google Kubernetes Engine
Building a Machine Learning Platform with Kubeflow and Ray on Google Kubernetes Engine Building a Machine Learning Platform with Kubeflow and Ray on Google Kubernetes Engine

Increasingly more enterprises adopt Machine Learning (ML) capabilities to enhance their services, products, and operations. As their ML capabilities mature, they build centralized ML Platforms to serve many teams and users across their organization. Machine learning is inherently an experimental process requiring repeated iterations. An ML Platform standardizes the model development and deployment workflow to offer greater consistency for the repeated process. This facilitates productivity and reduces time from prototype to production.When first trying ML in the cloud, many practitioners will start with fully managed ML platforms like Google Cloud’s Vertex AI. Fully-managed platforms abstra…

3 дня, 13 часов назад @ cloud.google.com
Building a Machine Learning Platform with Kubeflow and Ray on Google Kubernetes Engine
Building a Machine Learning Platform with Kubeflow and Ray on Google Kubernetes Engine Building a Machine Learning Platform with Kubeflow and Ray on Google Kubernetes Engine

Increasingly more enterprises adopt Machine Learning (ML) capabilities to enhance their services, products, and operations. As their ML capabilities mature, they build centralized ML Platforms to serve many teams and users across their organization. Machine learning is inherently an experimental process requiring repeated iterations. An ML Platform standardizes the model development and deployment workflow to offer greater consistency for the repeated process. This facilitates productivity and reduces time from prototype to production.When first trying ML in the cloud, many practitioners will start with fully managed ML platforms like Google Cloud’s Vertex AI. Fully-managed platforms abstra…

3 дня, 13 часов назад @ cloud.google.com
TensorStore for High-Performance, Scalable Array Storage
TensorStore for High-Performance, Scalable Array Storage TensorStore for High-Performance, Scalable Array Storage

In these settings, even a single dataset may require terabytes or petabytes of data storage.

Familiar API for Data Access and ManipulationTensorStore provides a simple Python API for loading and manipulating large array data.

Checkpoints are stored in zarr format using TensorStore, with a chunk structure chosen to allow the partition for each TPU to be read and written independently in parallel.

Specifically, TensorStore has managed some of the largest and most widely accessed connectomic datasets, with Google Cloud Storage as the underlying object storage system.

A fly brain reconstruction for which the underlying data can be easily accessed and manipulated using TensorStore.

4 дня, 8 часов назад @ ai.googleblog.com
What’s New with Google’s Unified, Open and Intelligent Data Cloud
What’s New with Google’s Unified, Open and Intelligent Data Cloud What’s New with Google’s Unified, Open and Intelligent Data Cloud

We’re fortunate to work with some of the world’s most innovative customers on a daily basis, many of whom come to Google Cloud for our well-established expertise in data analytics and AI. As we’ve worked and partnered with these data leaders, we have encountered similar priorities among many of them: to remove the barriers of data complexity, unlock new use cases, and reach more people with more impact. These innovators and industry disruptors power their data innovation with a data cloud that lets their people work with data of any type, any source, any size, and at any speed, without capacity limits. A data cloud that lets them easily and securely move across workloads: from SQL to Spark,…

4 дня, 13 часов назад @ cloud.google.com
View Synthesis with Transformers
View Synthesis with Transformers View Synthesis with Transformers

They are purely transformer-based, operating on sets of image patches, and they leverage a 4D light field representation for positional encoding, which helps to model view-dependent effects.

Instead of processing each reference image completely, we look only at the regions that are likely to influence the target pixel.

The first transformer aggregates information along each epipolar line, and the second along each reference image.

Generalizing to New ScenesOne limitation of LFNR is that the first transformer collapses the information along each epipolar line independently for each reference image.

Image patches are mapped via the linear projection layer to initial features (shown as blue an…

5 дней, 10 часов назад @ ai.googleblog.com
Enabling real-time AI with Streaming Ingestion in Vertex AI
Enabling real-time AI with Streaming Ingestion in Vertex AI Enabling real-time AI with Streaming Ingestion in Vertex AI

Many machine learning (ML) use cases, like fraud detection, ad targeting, and recommendation engines, require near real-time predictions. The performance of these predictions is heavily dependent on access to the most up-to-date data, with delays of even a few seconds making all the difference. But it’s difficult to set up the infrastructure needed to support high-throughput updates and low-latency retrieval of data. Starting this month, Vertex AI Matching Engine and Feature Store will support real-time Streaming Ingestion as Preview features. With Streaming Ingestion for Matching Engine, a fully managed vector database for vector similarity search, items in an index are updated continuousl…

6 дней, 12 часов назад @ cloud.google.com
FindIt: Generalized Object Localization with Natural Language Queries
FindIt: Generalized Object Localization with Natural Language Queries FindIt: Generalized Object Localization with Natural Language Queries

To address these limitations, we are presenting “FindIt: Generalized Localization with Natural Language Queries” at ECCV 2022.

FindIt is a unified model for referring expression comprehension (col. 1), text-based localization (col. 2), and the object detection task (col. 3).

This motivates the need for a multi-level image-text fusion model for efficient processing of higher resolution images over different localization tasks.

For the text-based localization task, we generate a set of queries over the categories present in the image.

On the text-based localization benchmark, FindIt achieves 79.7%, higher than the GPV (73.0%), and Faster R-CNN baselines (75.2%).

6 дней, 12 часов назад @ ai.googleblog.com
Google at Interspeech 2022
Google at Interspeech 2022 Google at Interspeech 2022

This week, the 23rd Annual Conference of the International Speech Communication Association (INTERSPEECH 2022) is being held in Incheon, South Korea, representing one of the world’s most extensive conferences on research and technology of spoken language understanding and processing.

Over 2,000 experts in speech-related research fields gather to take part in oral presentations and poster sessions and to collaborate with streamed events across the globe.

We are excited to be a Diamond Sponsor of INTERSPEECH 2022, where we will be showcasing nearly 50 research publications and supporting a number of workshops, special sessions and tutorials.

In addition, online attendees are encouraged to vis…

1 неделя, 2 дня назад @ ai.googleblog.com
Robust Online Allocation with Dual Mirror Descent
Robust Online Allocation with Dual Mirror Descent Robust Online Allocation with Dual Mirror Descent

In particular, we have recently developed a new class of algorithms for online allocation problems, called dual mirror descent, that are simple, robust, and flexible.

Online Allocation ProblemsIn an online allocation problem, a decision maker has a limited amount of total resources (B) and receives a certain number of requests over time (T).

Because prices for resources are referred to as "dual variables" in the field of optimization, we call the resulting algorithm dual mirror descent.

Performance of dual mirror descent, a training based method, and an adversarial method relative to the optimal offline solution.

ConclusionIn this post we introduced dual mirror descent, an algorithm for onl…

1 неделя, 3 дня назад @ ai.googleblog.com
Cloud Wisdom Weekly: 4 ways AI/ML boosts innovation and reduces costs
Cloud Wisdom Weekly: 4 ways AI/ML boosts innovation and reduces costs Cloud Wisdom Weekly: 4 ways AI/ML boosts innovation and reduces costs

“Cloud Wisdom Weekly: for tech companies and startups” is a new blog series we’re running this fall to answer common questions our tech and startup customers ask us about how to build apps faster, smarter, and cheaper. In this installment, we explore how to leverage artificial intelligence (AI) and machine learning (ML) for faster innovation and efficient operational growth. Whether they’re trying to extract insights from data, create faster and more efficient workflows via intelligent automation, or build innovative customer experiences, leaders at today’s tech companies and startups know that proficiency in AI and ML is more important than ever.AI and ML technologies are often expensive a…

1 неделя, 3 дня назад @ cloud.google.com
PaLI: Scaling Language-Image Learning in 100+ Languages
PaLI: Scaling Language-Image Learning in 100+ Languages PaLI: Scaling Language-Image Learning in 100+ Languages

In “PaLI: A Jointly-Scaled Multilingual Language-Image Model”, we introduce a unified language-image model trained to perform many tasks and in over 100 languages.

The PaLI model architecture is simple, reusable and scalable.

Model Scaling ResultsWe examine how the image and language model components interact with each other with regards to model scaling and where the model yields the most gains.

Scaling both the language and the visual components of the PaLI model contribute to improved performance.

Multilingual captioning greatly benefits from scaling the PaLI models.

1 неделя, 4 дня назад @ ai.googleblog.com
LOLNeRF: Learn from One Look
LOLNeRF: Learn from One Look LOLNeRF: Learn from One Look

An important aspect of human vision is our ability to comprehend 3D shape from the 2D images we observe.

However it is possible to estimate 3D structure based on what kind of 3D objects occur commonly and what similar structures look like from different perspectives.

In “LOLNeRF: Learn from One Look”, presented at CVPR 2022, we propose a framework that learns to model 3D structure and appearance from collections of single-view images.

Color and density values are accumulated along rays, one ray for each pixel in a 2D image.

ConclusionWe’ve developed a technique that is effective at discovering 3D structure from single 2D images.

1 неделя, 6 дней назад @ ai.googleblog.com
How Let’s Enhance uses NVIDIA AI and GKE to power AI-based photo editing
How Let’s Enhance uses NVIDIA AI and GKE to power AI-based photo editing How Let’s Enhance uses NVIDIA AI and GKE to power AI-based photo editing

There’s an explosion in the number of digital images generated and used for both personal and business needs. On e-commerce platforms and online marketplaces for example, product images and visuals heavily influence the consumer’s perception, decision making and ultimately conversion rates. In addition, there’s been a rapid shift towards user-generated visual content for ecommerce — think seller-generated product imagery, host-generated rental property photos and influencer-generated social media content. The challenge? These user-generated images are often captured using mobile cameras and vary greatly in terms of their size, quality, compression ratios and resolution, making it difficult …

1 неделя, 6 дней назад @ cloud.google.com
Take your ML models from prototype to production with Vertex AI
Take your ML models from prototype to production with Vertex AI Take your ML models from prototype to production with Vertex AI

You’re working on a new machine learning problem, and the first environment you use is a notebook. Your data is stored on your local machine, and you try out different model architectures and configurations, executing the cells of your notebook manually each time. This workflow is great for experimentation, but you quickly hit a wall when it comes time to elevate your experiments up to production scale. Suddenly, your concerns are more than just getting the highest accuracy score.Sound familiar?Developing production applications or training large models requires additional tooling to help you scale beyond just code in a notebook, and using a cloud service provider can help. But that process…

1 неделя, 6 дней назад @ cloud.google.com
Learning to Walk in the Wild from Terrain Semantics
Learning to Walk in the Wild from Terrain Semantics Learning to Walk in the Wild from Terrain Semantics

In “Learning Semantics-Aware Locomotion Skills from Human Demonstrations”, we design a hierarchical learning framework to improve a robot’s ability to traverse complex, off-road environments.

We first compute the speed from terrain semantics, and then select a gait based on the speed.

The skill policy selects a locomotion skill based on camera images, and the motor controller converts the selected skill into motor commands.

The high-level skill policy is further decomposed into a learned speed policy and a heuristic-based gait selector.

To decide a skill, the speed policy first computes the desired forward speed, based on the semantic information from the onboard RGB camera.

2 недели, 3 дня назад @ ai.googleblog.com
OpenAI OpenAI
последний пост 5 дней, 13 часов назад
Introducing Whisper
Introducing Whisper Introducing Whisper

We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.

Whisper examples: Reveal TranscriptWhisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web.

We show that the use of such a large and diverse dataset leads to improved robustness to accents, background noise and technical language.

We find this approach is particularly effective at learning speech to text translation and outperforms the supervised SOTA on CoVoST2 to English translation zero-shot.

Check out the paper, model card, and code to learn more det…

5 дней, 13 часов назад @ openai.com
DALL·E: Introducing Outpainting
DALL·E: Introducing Outpainting DALL·E: Introducing Outpainting

Now, with Outpainting, users can extend the original image, creating large-scale images in any aspect ratio.

Outpainting takes into account the image’s existing visual elements — including shadows, reflections, and textures — to maintain the context of the original image.

More than one million people are using DALL·E, the AI system that generates original images and artwork from a natural language description, as a creative tool today.

Artists have already created remarkable images with the new Outpainting feature, and helped us better understand its capabilities in the process.

Original outpainting by Tyna Eloundou Original outpainting by OpenAI Outpainting by David Schnurr Original outpai…

3 недели, 5 дней назад @ openai.com
Our approach to alignment research
Our approach to alignment research Our approach to alignment research

IntroOur alignment research aims to make artificial general intelligence (AGI) aligned with human values and follow human intent.

We believe that even without fundamentally new alignment ideas, we can likely build sufficiently aligned AI systems to substantially advance alignment research itself.

At a high-level, our approach to alignment research focuses on engineering a scalable training signal for very smart AI systems that is aligned with human intent.

Instead, we aim for a more pragmatic approach: building and aligning a system that can make faster and better alignment research progress than humans can.

Therefore human researchers will focus more and more of their effort on reviewing a…

1 месяц назад @ openai.com
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 месяц, 2 недели 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…

2 месяца, 1 неделя 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.

2 месяца, 1 неделя 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…

2 месяца, 2 недели 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…

3 месяца 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…

3 месяца 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.

3 месяца, 2 недели 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…

3 месяца, 2 недели 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…

3 месяца, 3 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.

4 месяца 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.

4 месяца, 1 неделя 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 …

4 месяца, 3 недели назад @ openai.com
Microsoft Microsoft
последний пост 13 часов назад
Assessing AI system performance: thinking beyond models to deployment contexts
Assessing AI system performance: thinking beyond models to deployment contexts Assessing AI system performance: thinking beyond models to deployment contexts

When assessing the performance of AI models, we often rely on aggregate performance metrics like percentage of accuracy.

Getting started: AI model or AI system performance?

AI system performance in the context of the user experienceA user experience can only be as good as the underlying AI system.

We know, for example, that improving AI system performance does not necessarily correspond to improved performance of AI teams (reference).

Documenting AI system performance should include a range of approaches, from metrics scorecards to system performance metrics for a deployed user experience, to visualization tools.

13 часов назад @ microsoft.com
AI Models vs. AI Systems: Understanding Units of Performance Assessment
AI Models vs. AI Systems: Understanding Units of Performance Assessment AI Models vs. AI Systems: Understanding Units of Performance Assessment

AI Model vs. AI SystemFigure 1: Differentiating AI performance assessment on the level of AI model versus AI system.

The figure above illustrates the distinction between the AI model and AI system in this example (figure 1).

This example illustrates how AI model performance metrics are not sufficient to determine whether an AI system is ‘good enough’ for real-life application.

So, how do we decide when AI system performance is ‘good enough’ for use in real-life application?

It is clear that high levels of AI model performance alone are not sufficient—we must consider every element of the AI system.

1 неделя назад @ microsoft.com
Microsoft Research Summit 2022: What’s Next for Technology and Humanity?
Microsoft Research Summit 2022: What’s Next for Technology and Humanity? Microsoft Research Summit 2022: What’s Next for Technology and Humanity?

Realizing the benefits of these new breakthroughs demands that we come together in new ways across the global research community.

That’s why I’m excited to invite you to join us for this year’s Microsoft Research Summit, which will take place on October 18-20, 2022.

This virtual event is where the global research community convenes to explore how emerging research might best address societal challenges and have significant impact on our lives in the coming years.

This year’s event will feature over 120 speakers, including researchers and leaders from across the research community at Microsoft, alongside partners and collaborators from industry, academia and government who are advancing the …

1 неделя, 5 дней назад @ microsoft.com
CCF: Bringing efficiency and usability to a decentralized trust model
CCF: Bringing efficiency and usability to a decentralized trust model CCF: Bringing efficiency and usability to a decentralized trust model

In a distributed trust model, network participants validate transactions over a network by performing computation on those transactions themselves and comparing the outputs.

CCF is based on a distributed trust model like that of blockchain while maintaining data confidentiality through secure centralized computation.

This centralized confidential computation model also provides another benefit—it addresses the substantial amount of energy used in blockchain and other distributed computation environments.

We also had to prove that there were important use cases for maintaining confidentiality in a distributed trust system.

We built basic proofs of concept and gave numerous demonstrations sho…

1 неделя, 5 дней назад @ microsoft.com
Microsoft shares what's next in machine learning at NVIDIA GTC
Microsoft shares what's next in machine learning at NVIDIA GTC

Finding scalable solutions for today’s global challenges requires forward-thinking, transformative tools. As environmental, economic, and public health concerns mount, Microsoft Azure is addressing these challenges head on with high-performance computing (HPC), AI, and machine learning.

1 неделя, 5 дней назад @ azure.microsoft.com
A game-theoretic approach to provably correct and scalable offline RL
A game-theoretic approach to provably correct and scalable offline RL A game-theoretic approach to provably correct and scalable offline RL

Unlike the conventional online RL, offline RL can learn policies without collecting online data and even without interacting with a simulator.

Moreover, since offline RL does not blindly mimic the behaviors seen in data, like imitation learning (an alternate strategy of RL), offline RL does not require expensive expert-quality decision examples, and the learned policy of offline RL can potentially outperform the best data-collection policy.

Subscribe todayIn this post, we introduce a generic game-theoretic framework for offline RL.

Thinking offline RL as two-player gamesIf we think about uncertainties as hypotheses in a version space, then a natural strategy of designing offline RL agents i…

2 недели, 5 дней назад @ microsoft.com
MoCapAct: Training humanoid robots to “Move Like Jagger”
MoCapAct: Training humanoid robots to “Move Like Jagger” MoCapAct: Training humanoid robots to “Move Like Jagger”

This will enable advanced research on artificial humanoid control at a fraction of the compute resources currently required.

The reason why humanoid control research has been so computationally demanding is subtle and, at the first glance, paradoxical.

For each of the trained skill policies above, MoCapAct supplies a set of recorded trajectories generated by executing that skill’s control policy on the dm_control’s humanoid agent.

Figure 2: The hierarchical policy consists of a high-level policy and low-level policy.

The low-level policy takes the skill and the humanoid observation and outputs an action that best realizes the skill.

1 месяц назад @ microsoft.com
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 месяц, 2 недели 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 месяц, 2 недели 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 месяц, 2 недели 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…

2 месяца 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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2 месяца, 1 неделя 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…

2 месяца, 1 неделя 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…

2 месяца, 1 неделя 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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2 месяца, 2 недели назад @ blogs.microsoft.com
MIT AI MIT AI
последний пост 10 часов назад
Q&A: Global challenges surrounding the deployment of AI
Q&A: Global challenges surrounding the deployment of AI Q&A: Global challenges surrounding the deployment of AI

Here, they discuss talk some of the key issues facing the AI policy landscape today and the challenges surrounding the deployment of AI.

The three are co-organizers of the upcoming AI Policy Forum Summit on Sept. 28, which will further explore the issues discussed here.

Q: Can you talk about the ­ongoing work of the AI Policy Forum and the AI policy landscape generally?

Who is accountable for the potential problems in AI systems deployed through several layers of outsourcing?

Additionally, some have proposed taxes on online advertising to address the negative externalities caused by current social media business model.

10 часов назад @ news.mit.edu
Understanding reality through algorithms
Understanding reality through algorithms Understanding reality through algorithms

There, De La Torre became enamored by the possibilities of using computation to model the human brain.

With McDermott, De La Torre is attempting to understand how the brain integrates vision and sound.

Given an ambiguous image, that simple auditory cue is all it takes to create a different perception of reality.

De La Torre is using behavioral experiments to probe how the human brain makes sense of multisensory cues to construct a particular perception.

“What we want to do is model exactly what’s happening,” says De La Torre.

2 дня, 1 час назад @ news.mit.edu
In-home wireless device tracks disease progression in Parkinson’s patients
In-home wireless device tracks disease progression in Parkinson’s patients In-home wireless device tracks disease progression in Parkinson’s patients

Parkinson’s disease is the fastest-growing neurological disease, now affecting more than 10 million people worldwide, yet clinicians still face huge challenges in tracking its severity and progression.

In an effort to address these problems, researchers from MIT and elsewhere demonstrated an in-home device that can monitor a patient’s movement and gait speed, which can be used to evaluate Parkinson’s severity, the progression of the disease, and the patient’s response to medication.

Advanced algorithms use these movement data to compute gait speed — how fast the person is walking.

They used statistical methods to analyze the data and found that in-home gait speed can be used to effectively …

5 дней, 11 часов назад @ news.mit.edu
Empowering Cambridge youth through data activism
Empowering Cambridge youth through data activism Empowering Cambridge youth through data activism

When Kundargi reached out to MIT pK-12 collaborators, MIT PRG’s graduate research assistant Raechel Walker proposed the Data Activism curriculum.

Walker defines “data activism” as utilizing data, computing, and art to analyze how power operates in the world, challenge power, and empathize with people who are oppressed.

A different side of STEAMThe development and execution of the Data Activism curriculum contributed to Walker’s and postdoc Xiaoxue Du’s respective research at PRG.

Walker is studying AI education, specifically creating and teaching data activism curricula for minoritized communities.

Throughout the program, the Data Activism team taught students in small groups, continually a…

5 дней, 14 часов назад @ news.mit.edu
Protecting maternal health in Rwanda
Protecting maternal health in Rwanda Protecting maternal health in Rwanda

They have developed a mobile health (mHealth) platform that uses artificial intelligence and real-time computer vision to predict infection in C-section wounds with roughly 90 percent accuracy.

The first step in the project was gathering a database of wound images taken by community health workers in rural Rwanda.

Health workers are instructed to place the frame over the wound and open the app, which provides real-time feedback on the camera placement.

The app has been well received by women and community health workers in Rwanda.

As the team looks to develop the comprehensive app for maternal health, privacy and data protection are a top priority.

1 неделя, 2 дня назад @ news.mit.edu
Computing for the health of the planet
Computing for the health of the planet Computing for the health of the planet

The health of the planet is one of the most important challenges facing humankind today.

Ensuring the health and safety of our planet necessitates approaches that connect scientific, engineering, social, economic, and political aspects.

The MIT Schwarzman College of Computing is committed to hiring multiple new faculty in computing for climate and the environment, as part of MIT’s plan to recruit 20 climate-focused faculty under its climate action plan.

The college also undertook searches for core computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

The goal is to build capacity at MIT to help more deeply infuse computing and other disciplines in depart…

1 неделя, 6 дней назад @ news.mit.edu
AI system makes models like DALL-E 2 more creative
AI system makes models like DALL-E 2 more creative AI system makes models like DALL-E 2 more creative

DALL-E 2 uses something called a diffusion model, where it tries to encode the entire text into one description to generate an image.

The seemingly magical models behind image generation work by suggesting a series of iterative refinement steps to get to the desired image.

“The model can effectively model object positions and relational descriptions, which is challenging for existing image-generation models.

“The fact that our model is composable means that you can learn different portions of the model, one at a time.

“This is a nice idea that leverages the energy-based interpretation of diffusion models so that old ideas around compositionality using energy-based models can be applied.

2 недели, 4 дня назад @ news.mit.edu
Collaborative machine learning that preserves privacy
Collaborative machine learning that preserves privacy Collaborative machine learning that preserves privacy

Federated learning is a collaborative method for training a machine-learning model that keeps sensitive user data private.

But federated learning has some drawbacks.

“A lot of papers have addressed one of the problems of federated learning, but the challenge was to put all of this together.

Other methods have used this pruning technique for federated learning to create smaller machine-learning models which could be transferred more efficiently.

Mugunthan is hopeful this work inspires other researchers to rethink how they approach federated learning.

2 недели, 6 дней назад @ news.mit.edu
Analyzing the potential of AlphaFold in drug discovery
Analyzing the potential of AlphaFold in drug discovery Analyzing the potential of AlphaFold in drug discovery

“Breakthroughs such as AlphaFold are expanding the possibilities for in silico drug discovery efforts, but these developments need to be coupled with additional advances in other aspects of modeling that are part of drug discovery efforts,” Collins says.

However, more improvement will be necessary to fully take advantage of the protein structures provided by AlphaFold, the researchers say.

AlphaFold, an AI software developed by DeepMind and Google, has accurately predicted protein structures from their amino acid sequences.

The researchers found similar results when they used this modeling approach with protein structures that have been experimentally determined, instead of the structures p…

2 недели, 6 дней назад @ news.mit.edu
Using machine learning to identify undiagnosable cancers
Using machine learning to identify undiagnosable cancers Using machine learning to identify undiagnosable cancers

Machine learning in developmentParsing the differences in the gene expression among different kinds of tumors of unknown primary is an ideal problem for machine learning to solve.

Cancer cells look and behave quite differently from normal cells, in part because of extensive alterations to how their genes are expressed.

Many of the gene expression programs that drive embryogenesis are known to be reactivated or dysregulated in cancer cells.

Oncologists initially could not find a tumor mass, and could not classify cancer cells using the tools they had at the time.

“Developmental gene expression represents only one small slice of all the factors that could be used to diagnose and treat cancers…

3 недели, 4 дня назад @ news.mit.edu
AI that can learn the patterns of human language
AI that can learn the patterns of human language AI that can learn the patterns of human language

They have demonstrated an artificial intelligence system that can learn the rules and patterns of human languages on its own.

This model can also automatically learn higher-level language patterns that can apply to many languages, enabling it to achieve better results.

This system could be used to study language hypotheses and investigate subtle similarities in the way diverse languages transform words.

Instead of learning weights, can the model learn expressions or rules?

“Linguists have thought that in order to really understand the rules of a human language, to empathize with what it is that makes the system tick, you have to be human.

3 недели, 6 дней назад @ news.mit.edu
Taking a magnifying glass to data center operations
Taking a magnifying glass to data center operations Taking a magnifying glass to data center operations

Their goal is to empower computer scientists and data center operators to better understand avenues for data center optimization — an important task as processing needs continue to grow.

While cloud providers are actively working on optimizing their data centers, they do not often make their data or models available for the broader high-performance computing (HPC) community to leverage.

"Data centers are changing.

"Until now, there hasn't been a great way to analyze the impact to data centers.

Workload classificationAmong the world's TOP500 supercomputers, TX-GAIA combines traditional computing hardware (central processing units, or CPUs) with nearly 900 graphics processing unit (GPU) accel…

1 месяц назад @ news.mit.edu
Building better batteries, faster
Building better batteries, faster Building better batteries, faster

With his tool in hand, Leon plans to help search for new materials to enable the development of powerful and lightweight batteries.

Leveraging machine learning to research battery materialsScientists investigating new battery materials generally use computer simulations to understand how different combinations of materials perform.

Leon’s tool comes at an opportune time, when many scientists are investigating a new paradigm of batteries: solid-state batteries.

Compared to traditional batteries, which contain liquid electrolytes, solid-state batteries are safer, lighter, and easier to manufacture.

This past year, he served as the academic chair on his department’s graduate student organizati…

1 месяц назад @ news.mit.edu
Artificial intelligence model can detect Parkinson’s from breathing patterns
Artificial intelligence model can detect Parkinson’s from breathing patterns Artificial intelligence model can detect Parkinson’s from breathing patterns

The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson’s from their nocturnal breathing — i.e., breathing patterns that occur while sleeping.

The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone’s Parkinson’s disease and track the progression of their disease over time.

The MIT researchers demonstrated that the artificial intelligence assessment of Parkinson's can be done every night at home while the person is asleep and without touching their body.

“A relationship between Parkinson’s and breathi…

1 месяц назад @ news.mit.edu
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…

1 месяц, 2 недели назад @ news.mit.edu
Berkeley AI
последний пост 1 неделя назад
Keeping Learning-Based Control Safe by Regulating Distributional Shift
Keeping Learning-Based Control Safe by Regulating Distributional Shift Keeping Learning-Based Control Safe by Regulating Distributional Shift

Keeping Learning-Based Control Safe by Regulating Distributional ShiftTo regulate the distribution shift experience by learning-based controllers, we seek a mechanism for constraining the agent to regions of high data density throughout its trajectory (left).

The central idea behind our work is to view the training data distribution as a safety constraint, and to draw on tools from control theory to control the distributional shift experienced by the agent during closed-loop control.

To use an LDM in control, we can train an LDM and learning-based controller on the same training dataset and constrain the controller’s action outputs with an LDM constraint ($G(s, a)) \leq -\log(c)$).

The ce…

1 неделя назад @ bair.berkeley.edu
Reverse engineering the NTK: towards first-principles architecture design
Reverse engineering the NTK: towards first-principles architecture design Reverse engineering the NTK: towards first-principles architecture design

Reverse engineering the NTK: towards first-principles architecture designFoundational works showed how to find the kernel corresponding to a wide network.

The NTK of a 4HL $\textrm{ReLU}$ FCN as a function of the cosine between two input vectors $x_1$ and $x_2$.

Shallowification of a deep $\textrm{ReLU}$ FCN into a 1HL FCN with an engineered activation function $\tilde{\phi}$.

4 below shows a “mimic” activation function \(\tilde{\phi}\) that gives virtually the same NTK as a deep \(\textrm{ReLU}\) FCN.

This is interesting from an engineering perspective because the shallow network uses fewer parameters than the deep network to achieve the same performance.

4 недели назад @ bair.berkeley.edu
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…

2 месяца, 2 недели 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…

2 месяца, 4 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…

4 месяца, 1 неделя 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…

4 месяца, 3 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…

5 месяцев 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…

5 месяцев 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…

5 месяцев, 1 неделя 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…

6 месяцев, 1 неделя 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…

7 месяцев 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.

7 месяцев, 3 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, …

9 месяцев, 2 недели назад @ bair.berkeley.edu
AWS Machine Learning AWS Machine Learning
последний пост 11 часов назад
Introducing self-service quota management and higher default service quotas for Amazon Textract
Introducing self-service quota management and higher default service quotas for Amazon Textract Introducing self-service quota management and higher default service quotas for Amazon Textract

With this launch, we’re improving Amazon Textract support for service quotas by enabling you to self-manage your service quotas via the Service Quotas console.

In this post, we discuss the updated default service quotas, the new service quota management capabilities, and the service quota calculator for Amazon Textract.

The default quota value is the default value of the quota in that specific Region, and the applied quota value is the currently applied value for that quota for the account in that Region.

Amazon Textract Service Quota CalculatorWe’re introducing a new quota calculator on the Amazon Textract console.

To learn more about the Amazon Textract service quota calculator and extend…

11 часов назад @ aws.amazon.com
Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models
Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models

BCAI partnered with the Amazon ML Solutions Lab (MLSL) to incorporate the latest advances in deep neural network (DNN)-based models for revenue forecasting.

We include CNN-QR and DeepAR+, two off-the-shelf models in Amazon Forecast, as well as a custom Transformer model trained using Amazon SageMaker.

The following figure shows a simplified example with a two-level structure, which mimics the hierarchical revenue forecasting structure at Bosch.

Amazon ForecastForecast is a fully-managed AI/ML service from AWS that provides preconfigured, state-of-the-art time series forecasting models.

The system can be extended to incorporate new forecast models and supporting functions such as generating …

3 дня, 12 часов назад @ aws.amazon.com
Detect population variance of endangered species using Amazon Rekognition
Detect population variance of endangered species using Amazon Rekognition Detect population variance of endangered species using Amazon Rekognition

Having accurate and regular information about endangered animals in the wild will improve wildlife conservationists’ ability to study and conserve endangered species.

PrerequisitesA good training set is required to build an effective model using Rekognition Custom Labels.

For instructions, refer to Creating a project, Creating training and test datasets, and Training an Amazon Rekognition Custom Labels model.

ConclusionIn this post, we presented an automated system that identifies endangered species, records their population count, and provides insights about variance in population over time.

For more information about Rekognition Custom Labels, refer to Getting started with Amazon Rekognit…

4 дня, 8 часов назад @ aws.amazon.com
How Amazon Search reduced ML inference costs by 85% with AWS Inferentia
How Amazon Search reduced ML inference costs by 85% with AWS Inferentia How Amazon Search reduced ML inference costs by 85% with AWS Inferentia

The Amazon Search team develops machine learning (ML) technology that powers the Amazon.com search engine and helps customers search effortlessly.

In this post, we describe how Amazon Search uses AWS Inferentia, a high-performance accelerator purpose built by AWS to accelerate deep learning inference workloads.

AWS Inferentia and the AWS Neuron SDKEC2 Inf1 instances are powered by AWS Inferentia, the first ML accelerator purpose built by AWS to accelerate deep learning inference workloads.

The team switched to inf1.6xlarge instances, each with 4 AWS Inferentia accelerators, and 16 NeuronCores (4 cores per AWS Inferentia chip).

He is mostly focused on NLP use cases and helping customers opti…

4 дня, 11 часов назад @ aws.amazon.com
Amazon Comprehend Targeted Sentiment adds synchronous support
Amazon Comprehend Targeted Sentiment adds synchronous support Amazon Comprehend Targeted Sentiment adds synchronous support

Targeted sentiment use casesReal-time targeted sentiment analysis in Amazon Comprehend has several applications to enable accurate and scalable brand and competitor insights.

Get started with Targeted SentimentTo use targeted sentiment on the Amazon Comprehend console, complete the following steps:On the Amazon Comprehend console, choose Launch Amazon Comprehend.

After the document has been analyzed, the output of the Targeted Sentiment API can be found on the Targeted sentiment tab in the Insights section.

For a more detailed breakdown, refer to Extract granular sentiment in text with Amazon Comprehend Targeted Sentiment or Output file organization.

To learn more about Targeted Sentiment f…

5 дней, 8 часов назад @ aws.amazon.com
Run machine learning enablement events at scale using AWS DeepRacer multi-user account mode
Run machine learning enablement events at scale using AWS DeepRacer multi-user account mode Run machine learning enablement events at scale using AWS DeepRacer multi-user account mode

This post shows how companies can introduce hundreds of employees to ML concepts by easily running AWS DeepRacer events at scale.

With AWS DeepRacer multi-user account management, event organizers can provide hundreds of participants access to AWS DeepRacer using a single AWS account, simplifying event management and improving the participant experience.

Build a solution around AWS DeepRacer multi-user account managementYou can use AWS DeepRacer multi-user account management to set usage quotas on training hours, monitor spending on training and storage, enable and disable training, and view and manage models for every event participant.

By activating multi-user account mode, you enable par…

5 дней, 13 часов назад @ aws.amazon.com
Enable intelligent decision-making with Amazon SageMaker Canvas and Amazon QuickSight
Enable intelligent decision-making with Amazon SageMaker Canvas and Amazon QuickSight Enable intelligent decision-making with Amazon SageMaker Canvas and Amazon QuickSight

In this post, we show you how to visualize the predictions generated from Canvas in a QuickSight dashboard, enabling intelligent decision-making via ML.

For the full list of supported data sources, refer to Importing data in Amazon SageMaker Canvas.

If you’re not sure how to access Canvas, refer to Getting started with using Amazon SageMaker Canvas.

We have successfully used our model to predict churn risk for our current customer population.

To learn more about using Canvas, see Build, Share, Deploy: how business analysts and data scientists achieve faster time-to-market using no-code ML and Amazon SageMaker Canvas.

5 дней, 13 часов назад @ aws.amazon.com
Amazon SageMaker Autopilot is up to eight times faster with new ensemble training mode powered by AutoGluon
Amazon SageMaker Autopilot is up to eight times faster with new ensemble training mode powered by AutoGluon Amazon SageMaker Autopilot is up to eight times faster with new ensemble training mode powered by AutoGluon

For datasets less than 100 MB, ensemble training mode builds machine learning (ML) models with high accuracy quickly—up to eight times faster than hyperparameter optimization (HPO) training mode with 250 trials, and up to 5.8 times faster than HPO training mode with 100 trials.

How Autopilot’s ensemble training mode worksDifferent datasets have characteristics that are suitable for different algorithms.

The tests compared ensemble training mode to HPO mode with 250 trials and HPO mode with 100 trials.

We observed that ensemble training mode performed better than HPO training mode (both 100 and 250 trials).

The following screenshot shows the final results of our titanic-ens ensemble training…

5 дней, 14 часов назад @ aws.amazon.com
Configure a custom Amazon S3 query output location and data retention policy for Amazon Athena data sources in Amazon SageMaker Data Wrangler
Configure a custom Amazon S3 query output location and data retention policy for Amazon Athena data sources in Amazon SageMaker Data Wrangler Configure a custom Amazon S3 query output location and data retention policy for Amazon Athena data sources in Amazon SageMaker Data Wrangler

You can import data from multiple data sources such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Snowflake, and 26 federated query data sources supported by Amazon Athena.

Starting today, when importing data from Athena data sources, you can configure the S3 query output location and data retention period to import data in Data Wrangler to control where and how long Athena stores the intermediary data.

When you use Athena to import data, you can use Data Wrangler’s default S3 location for the Athena query output, or specify an Athena workgroup to enforce a custom S3 location.

For Amazon S3 location of query results, enter your S3 location.

Analyze and process data with Dat…

6 дней, 6 часов назад @ aws.amazon.com
Use RStudio on Amazon SageMaker to create regulatory submissions for the life sciences industry
Use RStudio on Amazon SageMaker to create regulatory submissions for the life sciences industry Use RStudio on Amazon SageMaker to create regulatory submissions for the life sciences industry

The clinical trial submission package consists of tabulated data, analysis data, trial metadata, and statistical reports consisting of statistical tables, listings, and figures.

In this post, we demonstrate how we can use RStudio on Amazon SageMaker to create such regulatory submission deliverables.

Along with the RStudio Workbench, the RStudio suite for R developers also offers RStudio Connect and RStudio Package Manager.

After data has been ingested from a couple of different sources, we process it and create R data frames for a table.

Set up RStudio on SageMakerFor instructions on setting up RStudio on SageMaker in your environment, refer to Get started with RStudio on SageMaker.

6 дней, 11 часов назад @ aws.amazon.com
Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabular
Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabular Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabular

– The built-in algorithms come with parallelization across multiple compute instances and GPU support right out of the box for all applicable algorithms.

Automatic model tuning of tabular algorithmsHyperparameters control how our underlying algorithms operate and influence the performance of the model.

To select the best model, we apply SageMaker automatic model tuning to each of the four trained SageMaker tabular algorithms.

For more information about automatic model tuning, refer to Amazon SageMaker Automatic Model Tuning: Using Machine Learning for Machine Learning or Amazon SageMaker automatic model tuning: Scalable gradient-free optimization.

About the authorsDr. Xin Huang is an Applie…

6 дней, 11 часов назад @ aws.amazon.com
Parallel data processing with RStudio on Amazon SageMaker
Parallel data processing with RStudio on Amazon SageMaker Parallel data processing with RStudio on Amazon SageMaker

Parallel data processing, or data parallelization, takes large existing datasets and distributes them across multiple processers or nodes to operate on the data simultaneously.

Solution overviewWithin Amazon SageMaker, many customers use SageMaker Processing to help implement parallel data processing.

With SageMaker Processing, you can use a simplified, managed experience on SageMaker to run your data processing workloads, such as feature engineering, data validation, model evaluation, and model interpretation.

This file contains a series of steps that helps us create our parallel data processing pipeline using SageMaker Processing.

To learn more about the features and services used in this…

1 неделя назад @ aws.amazon.com
Discover insights from Zendesk with Amazon Kendra intelligent search
Discover insights from Zendesk with Amazon Kendra intelligent search Discover insights from Zendesk with Amazon Kendra intelligent search

This post shows how to configure the Amazon Kendra Zendesk connector to index your Zendesk domain and take advantage of Amazon Kendra intelligent search.

In this post, we show how to use the Amazon Kendra connector for Zendesk to index data from your Zendesk domain for intelligent search.

Configure the data source using the Amazon Kendra connector for ZendeskYou can add the Zendesk connector data source to an existing Amazon Kendra index or create a new index.

Run queries with the Amazon Kendra search consoleNow that the data is synced, we can run a few search queries on the Amazon Kendra search console by navigating to the Search indexed content page.

To learn more about the Amazon Kendra …

1 неделя, 3 дня назад @ aws.amazon.com
Amazon SageMaker Automatic Model Tuning now provides up to three times faster hyperparameter tuning with Hyperband
Amazon SageMaker Automatic Model Tuning now provides up to three times faster hyperparameter tuning with Hyperband Amazon SageMaker Automatic Model Tuning now provides up to three times faster hyperparameter tuning with Hyperband

Amazon SageMaker Automatic Model Tuning introduces Hyperband, a multi-fidelity technique to tune hyperparameters as a faster and more efficient way to find an optimal model.

In this post, we show how automatic model tuning with Hyperband can provide faster hyperparameter tuning—up to three times as fast.

Hyperband with SageMakerThe new Hyperband approach implemented for hyperparameter tuning has a few new data elements changed through AWS API calls.

For more details on how to configure and run automatic model tuning, refer to Specify the Hyperparameter Tuning Job Settings.

He works in the team owning the service for SageMaker Automatic Model Tuning.

1 неделя, 3 дня назад @ aws.amazon.com
Read webpages and highlight content using Amazon Polly
Read webpages and highlight content using Amazon Polly Read webpages and highlight content using Amazon Polly

We accelerate that journey by demonstrating how to achieve this goal using Amazon Polly.

For a full list of standard and neural voices in Amazon Polly, see Voices in Amazon Polly.

Amazon Polly outputs the files in an Amazon Simple Storage Service (Amazon S3) bucket; the script copies them to your web server.

PrerequisitesTo run this example, you need an AWS account with permission to use Amazon Polly, Amazon S3, Amazon Cognito, and (for demo purposes) AWS Cloud9.

ConclusionIn this post, we demonstrated a technical solution to a high-value business problem: how to use Amazon Polly to read the content of a webpage and highlight the content as it’s being read.

1 неделя, 3 дня назад @ aws.amazon.com
NVIDIA
последний пост 3 дня, 11 часов назад
An Elevated Experience: XPENG Launches G9 EV, Taking Innovation Even Higher with NVIDIA DRIVE Orin
An Elevated Experience: XPENG Launches G9 EV, Taking Innovation Even Higher with NVIDIA DRIVE Orin An Elevated Experience: XPENG Launches G9 EV, Taking Innovation Even Higher with NVIDIA DRIVE Orin

Editor’s Note: This post has been updated to reflect the XPENG G9 launch.

Electric automaker XPENG launched the G9 SUV this week during NVIDIA GTC.

The intelligent, software-defined vehicle is built on the high-performance compute of NVIDIA DRIVE Orin and delivers AI capabilities that are continuously upgraded with each over-the-air update.

The XPENG G9 and its fellow EVs are elevating the driving experience with intelligent features that are always at the cutting edge.

It is built on two NVIDIA DRIVE Orin systems-on-a-chip (SoC), achieving 508 trillion operations per second (TOPS).

3 дня, 11 часов назад @ blogs.nvidia.com
World-Class: NVIDIA Research Builds AI Model to Populate Virtual Worlds With 3D Objects, Characters
World-Class: NVIDIA Research Builds AI Model to Populate Virtual Worlds With 3D Objects, Characters World-Class: NVIDIA Research Builds AI Model to Populate Virtual Worlds With 3D Objects, Characters

Trained using only 2D images, NVIDIA GET3D generates 3D shapes with high-fidelity textures and complex geometric details.

GET3D can generate a virtually unlimited number of 3D shapes based on the data it’s trained on.

Manually modeling a 3D virtual world that reflects this is incredibly time consuming, making it difficult to fill out a detailed digital environment.

NVIDIA researchers trained GET3D on synthetic data consisting of 2D images of 3D shapes captured from different camera angles.

For the latest news from NVIDIA AI research, watch the replay of NVIDIA founder and CEO Jensen Huang’s keynote address at GTC:

3 дня, 16 часов назад @ blogs.nvidia.com
Go Hands On: Logitech G CLOUD Launches With Support for GeForce NOW
Go Hands On: Logitech G CLOUD Launches With Support for GeForce NOW Go Hands On: Logitech G CLOUD Launches With Support for GeForce NOW

The Logitech G CLOUD is the latest gaming handheld device to support GeForce NOW, giving members a brand new way to keep the gaming going.

But that’s not all: Portal with RTX joins GeForce NOW in November, free for Portal owners.

A New Way to PlayThe just-announced G CLOUD is the latest way to stream your PC library from the cloud on GeForce NOW.

The Hottest Games, Streaming SoonGet ready to play three new release titles coming to the cloud in the near future.

Charge Into the ‘Total Warhammer’ Series This WeekMake your move in the incoming additions from the Total War series by SEGA and Creative Assembly – Total War: WARHAMMER, Total War: WARHAMMER II and Total War: WARHAMMER III are stream…

4 дня, 16 часов назад @ blogs.nvidia.com
Continental and AEye Join NVIDIA DRIVE Sim Sensor Ecosystem, Providing Rich Capabilities for AV Development
Continental and AEye Join NVIDIA DRIVE Sim Sensor Ecosystem, Providing Rich Capabilities for AV Development Continental and AEye Join NVIDIA DRIVE Sim Sensor Ecosystem, Providing Rich Capabilities for AV Development

Global tier-1 supplier Continental and software-defined lidar maker AEye announced this week at NVIDIA GTC that they will migrate their intelligent lidar sensor model into NVIDIA DRIVE Sim.

Now, the companies are contributing this sensor model to DRIVE Sim, helping to bring their vision to the industry.

DRIVE Sim is open and modular — users can create their own extensions or choose from a rich library of sensor plugins from ecosystem partners.

By joining this rich community of DRIVE Sim users, Continental and AEye can now rapidly simulate edge cases in varying environments to test and validate lidar performance.

‘’With the scalability and accuracy of NVIDIA DRIVE Sim, we’re able to validate…

4 дня, 16 часов назад @ blogs.nvidia.com
Detecting Threats Faster with AI-Based Cybersecurity
Detecting Threats Faster with AI-Based Cybersecurity Detecting Threats Faster with AI-Based Cybersecurity

The NVIDIA Morpheus GPU-accelerated cybersecurity AI framework enables, for the first time, the ability to inspect all network traffic in real time to address the cybersecurity data problem on a scale previously impossible.

New visualization capabilities help pinpoint threats fasterThe latest release of NVIDIA Morpheus provides visualizations for cybersecurity data, enabling cybersecurity analysts to detect and remediate threats more efficiently.

The sensitive information detection model is trained to identify sensitive information, such as AWS credentials, GitHub credentials, private keys, and passwords.

Enabling new AI-based cybersecurity solutionsWith Morpheus, cybersecurity practitioner…

4 дня, 23 часа назад @ developer.nvidia.com
Inside AI: NVIDIA DRIVE Ecosystem Creates Pioneering In-Cabin Features With NVIDIA DRIVE IX
Inside AI: NVIDIA DRIVE Ecosystem Creates Pioneering In-Cabin Features With NVIDIA DRIVE IX Inside AI: NVIDIA DRIVE Ecosystem Creates Pioneering In-Cabin Features With NVIDIA DRIVE IX

These partners are joining a diverse ecosystem of companies developing on DRIVE IX, including Soundhound, Jungo and VisionLabs, providing cutting-edge solutions for any in-vehicle need.

Using DRIVE IX, it can empower both embedded and cloud-based natural language processing on the same architecture, ensuring drivers have access to important capabilities regardless of connectivity.

Using DRIVE IX, Rightware is creating a seamless visual experience across all cockpit and infotainment domains.

The Audio Weaver development platform from DSP Concepts can be integrated into the DRIVE IX advanced sound engine.

By combining the flexibility of DRIVE IX with leading in-cabin solutions providers, spen…

5 дней, 10 часов назад @ blogs.nvidia.com
HARMAN to Deliver Immersive In-Vehicle Experience With NVIDIA DRIVE IX
HARMAN to Deliver Immersive In-Vehicle Experience With NVIDIA DRIVE IX HARMAN to Deliver Immersive In-Vehicle Experience With NVIDIA DRIVE IX

With the introduction of NVIDIA DRIVE Thor, automakers can build unified AI compute platforms that combine advanced driver-assistance systems and in-vehicle infotainment.

The centralized NVIDIA DRIVE architecture supports novel features in the vehicle, including the ability to support content across multiple displays.

HARMAN, a global leader in rich, connected in-vehicle solutions, will be working with the NVIDIA DRIVE platform to develop multi-domain infotainment solutions.

HARMAN is using the DRIVE IX intelligent experience software stack to bring immersive cinematic experiences to every seat in the vehicle, including with individual sound zones for personalized audio.

With the high perfo…

5 дней, 13 часов назад @ blogs.nvidia.com
Solving AI Inference Challenges with NVIDIA Triton
Solving AI Inference Challenges with NVIDIA Triton Solving AI Inference Challenges with NVIDIA Triton

Challenges to consider when deploying AI inferenceAI inference is the production phase of running AI models to make predictions.

Constantly evolving models: Models in production must be updated continuously based on new data and algorithms, without business disruptions.

New AI inference use cases using NVIDIA TritonNVIDIA Triton Inference Server (Triton), is an open source inference serving software that supports all major model frameworks (TensorFlow, PyTorch, TensorRT, XGBoost, ONNX, OpenVINO, Python, and others).

NIO used the Triton model ensemble feature to move their pre– and post-processing functions from client application to Triton Inference Server.

Model speedup with the FasterTran…

5 дней, 13 часов назад @ developer.nvidia.com
Now You’re Speaking My Language: NVIDIA Riva Sets New Bar for Fully Customizable Speech AI
Now You’re Speaking My Language: NVIDIA Riva Sets New Bar for Fully Customizable Speech AI Now You’re Speaking My Language: NVIDIA Riva Sets New Bar for Fully Customizable Speech AI

Whether for virtual assistants, transcriptions or contact centers, voice AI services are turning words and conversations into bits and bytes of business magic.

Today at GTC, NVIDIA announced new additions to NVIDIA Riva, a GPU-accelerated software development kit for building and deploying speech AI applications.

Riva is built to be fully customizable at every stage of the speech AI pipeline to help solve unique problems efficiently.

Developers can easily access Riva and pretrained models through NVIDIA NGC, a hub for GPU-optimized AI software, models and Jupyter Notebook examples.

Try NVIDIA Riva with guided labs on ready-to-run infrastructure in NVIDIA LaunchPad.

5 дней, 14 часов назад @ blogs.nvidia.com
A Podcast With Teeth: How Overjet Brings AI to Dentists’ Offices
A Podcast With Teeth: How Overjet Brings AI to Dentists’ Offices A Podcast With Teeth: How Overjet Brings AI to Dentists’ Offices

Overjet, a member of the NVIDIA Inception program for startups, is moving fast to bring AI to dentists’ offices.

On this episode of the NVIDIA AI Podcast, host Noah Kravitz talks to Dr. Wardha Inam, CEO of Overjet, about how her company uses AI to improve patient care.

Subscribe to the AI Podcast: Now Available on Amazon MusicYou can now listen to the AI Podcast through Amazon Music.

Also get the AI Podcast through iTunes, Google Podcasts, Google Play, Castbox, DoggCatcher, Overcast, PlayerFM, Pocket Casts, Podbay, PodBean, PodCruncher, PodKicker, Soundcloud, Spotify, Stitcher and TuneIn.

Make the AI Podcast better: Have a few minutes to spare?

5 дней, 16 часов назад @ blogs.nvidia.com
Democratizing and Accelerating Genome Sequencing Analysis with NVIDIA Clara Parabricks v4.0
Democratizing and Accelerating Genome Sequencing Analysis with NVIDIA Clara Parabricks v4.0 Democratizing and Accelerating Genome Sequencing Analysis with NVIDIA Clara Parabricks v4.0

At GTC 2022, we announced the release of NVIDIA Clara Parabricks v4.0, which brings significant improvements to how genomic researchers and bioinformaticians deploy and scale genome sequencing analysis pipelines.

The NVIDIA Clara Parabricks v4.0 toolsetThe individual Clara Parabricks tools are also now offered in individual containers in the Clara Parabricks collection on NGC or as a unified container that encompasses all tools in one.

The models have been GPU-accelerated in collaboration with the NVIDIA Clara Parabricks team to provide rapid and high-accuracy variant calls across sequencing instruments.

Get started with Clara Parabricks v4.0To start using Clara Parabricks for free, visit t…

6 дней, 11 часов назад @ developer.nvidia.com
New Languages, Enhanced Cybersecurity, and Medical AI Frameworks Unveiled at GTC
New Languages, Enhanced Cybersecurity, and Medical AI Frameworks Unveiled at GTC New Languages, Enhanced Cybersecurity, and Medical AI Frameworks Unveiled at GTC

At GTC 2022, NVIDIA revealed major updates to its suite of NVIDIA AI frameworks for building real-time speech AI applications, designing high-performing recommenders at scale, applying AI to cybersecurity challenges, creating AI-powered medical devices, and more.

When organizations put their AI frameworks into production, enterprise support with NVIDIA AI Enterprise ensures the success of these AI applications.

Access to NVIDIA AI experts, training, and knowledge-based resources through NVIDIA Enterprise Support with the purchase of NVIDIA AI Enterprise software.

Add this GTC session to your calendar:NVIDIA AI EnterpriseNVIDIA AI Enterprise is a powerful cloud-native software suite that str…

6 дней, 12 часов назад @ developer.nvidia.com
No Hang Ups With Hangul: KT Trains Smart Speakers, Customer Call Centers With NVIDIA AI
No Hang Ups With Hangul: KT Trains Smart Speakers, Customer Call Centers With NVIDIA AI No Hang Ups With Hangul: KT Trains Smart Speakers, Customer Call Centers With NVIDIA AI

It has mastered its conversational skills in the highly complex Korean language thanks to large language models (LLMs) — machine learning algorithms that can recognize, understand, predict and generate human languages based on huge text datasets.

“With transformer-based models, we’ve achieved significant quality improvements for the GiGA Genie smart speaker, as well as our customer services platform AI Contact Center, or AICC,” said Hwijung Ryu, LLM development team lead at KT.

AICC is an all-in-one, cloud-based platform that offers AI voice agents and other customer service-related applications.

The NVIDIA AI platform simplified and sped up this process for KT.

NeMo Megatron enabled the te…

6 дней, 12 часов назад @ blogs.nvidia.com
New NVIDIA DGX System Software and Infrastructure Solutions Supercharge Enterprise AI
New NVIDIA DGX System Software and Infrastructure Solutions Supercharge Enterprise AI New NVIDIA DGX System Software and Infrastructure Solutions Supercharge Enterprise AI

With 32 petaflops of performance at FP8 precision, NVIDIA DGX H100 delivers a leap in efficiency for enterprise AI development.

New NVIDIA Base Command software, which simplifies and speeds AI development, powers every DGX system — from single nodes to DGX SuperPODs.

Base Command works with the NVIDIA AI Enterprise software suite, which is now included with every DGX system.

The NVIDIA AI software enables end-to-end AI development and deployment with supported AI and data science tools, optimized frameworks and pretrained models.

Leaders Power AI Breakthroughs With DGX SystemsEnterprises around the world choose NVIDIA DGX systems to power their most advanced AI workloads.

6 дней, 12 часов назад @ blogs.nvidia.com
Keynote Wrap-Up: NVIDIA CEO Unveils Next-Gen RTX GPUs, AI Workflows in the Cloud
Keynote Wrap-Up: NVIDIA CEO Unveils Next-Gen RTX GPUs, AI Workflows in the Cloud Keynote Wrap-Up: NVIDIA CEO Unveils Next-Gen RTX GPUs, AI Workflows in the Cloud

To speed adoption, he announced Deloitte, the world’s largest professional services firm, is bringing new services built on NVIDIA AI and NVIDIA Omniverse to the world’s enterprises.

With DLSS 3, it’s twice as fast in today’s games as the GeForce RTX 3080 Ti, and more powerful than the GeForce RTX 3090 Ti at lower power.

Thor will be the processor for robotics, medical instruments, industrial automation and edge AI systems, Huang said.

Haung also detailed NVIDIA Omniverse Cloud, an infrastructure-as-a-service that connects Omniverse applications running in the cloud, on premises or on a device.

“Today, we announced new chips, new advances to our platforms, and, for the very first time, new …

6 дней, 12 часов назад @ blogs.nvidia.com
Facebook
последний пост 1 месяц, 2 недели 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 месяц, 2 недели 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.

3 месяца, 2 недели 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…

6 месяцев, 2 недели назад @ engineering.fb.com
Uber Engineering Uber Engineering
последний пост 1 месяц, 3 недели 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…

1 месяц, 3 недели 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 месяца 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,…

3 месяца, 2 недели 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…

7 месяцев, 2 недели 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.

7 месяцев, 3 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…

8 месяцев, 1 неделя назад @ eng.uber.com
neptune.ai neptune.ai
последний пост 1 неделя, 6 дней назад
How to Solve the Data Ingestion and Feature Store Component of the MLOps Stack
How to Solve the Data Ingestion and Feature Store Component of the MLOps Stack How to Solve the Data Ingestion and Feature Store Component of the MLOps Stack

Bookmark for later How to Solve the Model Serving Component of the MLOps StackWhat is a feature store?

You could use Redshift as an offline feature store and DynamoDB or Redis as an online feature store.

But if this is not the case and you’re running a public cloud-heavy workload, using AWS SageMaker Feature Store or GCP Vertex AI Feature Store can be good options to start with.

Amazon SageMaker Feature Store for machine learning | SourceDatabricks also offers an embedded Feature Store service, which is also a good option and would be perfectly compatible with a tool like MLFlow.

Read also Setting up MLOps at a Reasonable Scale With Jacopo TagliabueExplanation of a feature store | SourceWho…

1 неделя, 6 дней назад @ neptune.ai
Feature Selection Methods and How to Choose Them
Feature Selection Methods and How to Choose Them Feature Selection Methods and How to Choose Them

Then, we will take a glimpse behind the hood of Boruta, the state-of-the-art feature selection algorithm, to check out a clever way to combine different feature selection methodsAnd we’ll look into how feature selection is leveraged in the industry.

This is what feature selection is, but it is equally important to understand what feature selection is not – it is neither feature extraction/feature engineering nor it is dimensionality reduction.

Feature selection methods | Source: authorUnsupervised feature selection methodsJust like unsupervised learning is the type of learning that looks for patterns in unlabeled data, similarly, unsupervised feature selection methods are such methods that …

2 недели, 3 дня назад @ neptune.ai
Exploratory Data Analysis for Tabular Data
Exploratory Data Analysis for Tabular Data Exploratory Data Analysis for Tabular Data

May interest you Exploratory Data Analysis for Natural Language Processing: A Complete Guide to Python ToolsExploratory Data Analysis vs.

Classical Data AnalysisApart from EDA, there are also other data analysis approaches, Classical Data Analysis being one of the most popular ones.

Both Exploratory Data Analysis and Classical Data Analysis start with a problem, followed by collecting the related data that can be used to understand the problem.

This is where their similarities end, let us see the differences now:Parameters Exploratory Data Analysis Classical Data Analysis Model Exploratory Data Analysis: does not impose deterministic or probabilistic models on the data.

Exploratory Data Ana…

2 недели, 3 дня назад @ neptune.ai
Best ML Model Registry Tools
Best ML Model Registry Tools Best ML Model Registry Tools

This article will discuss the model registry tools and evaluation criteria for such tools.

Evaluation criteria for choosing model registry toolsThe model registry is an important part of MLOps platforms/tools.

Competence in managing the model dependenciesThe model registry tool must have compatibility with all the dependencies the ML model needs.

Model registry toolsHere are a number of model registry tools that are used across the industry:Neptune provides a central processing unit to store, log, compare, display, query, and organize all metadata.

Comparison of model registry toolsEvery model registry tool has different features and performs various unique operations.

3 недели назад @ neptune.ai
Building ML Pipeline: 6 Problems & Solutions [From a Data Scientist’s Experience]
Building ML Pipeline: 6 Problems & Solutions [From a Data Scientist’s Experience] Building ML Pipeline: 6 Problems & Solutions [From a Data Scientist’s Experience]

Problem 2: No high-level separation of concernsThe separation of concerns in ML code bases is often missing at a high level.

What this means is that more often than not, so-called ML code is also doing feature transformations like operations that have nothing to do with ML – think physical document ingestion, conversion of administrative data, etc.

Problem 4: No configuration Data ModelA data model for handling ML configuration is often missing.

Problem 5: Handling legacy modelsSince the process of training a ML model often involves manual efforts (see problem 1) it can take really long to do so.

MLConfiguration contains the ML data model: enums and classes that do not contain any processin…

3 недели, 3 дня назад @ neptune.ai
Recommender Systems: Lessons From Building and Deployment
Recommender Systems: Lessons From Building and Deployment Recommender Systems: Lessons From Building and Deployment

If you look at recommender systems papers, a large number of them come from the industry instead of academia.

Recommender systems: model trainingLarge NLP or Vision models have billions of parameters distributed among linear, convolution, recurrent, or attention layers.

According to the team:“TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys).

Recommender systems: model evaluationOffline evaluationTypical classification task optimizes for metrics like accuracy, precision, recall, or F1-score.

Recommender systems: A/B testingImproving recommender systems is a continuous process.

1 месяц назад @ neptune.ai
Pillars of MLOps and How to Implement Them
Pillars of MLOps and How to Implement Them Pillars of MLOps and How to Implement Them

This is exactly the problem that is supposed to be solved by MLOps (Machine Learning Operations).

In this article, I will explain:what it is about,what are the pillars of MLOps,and how to implement them in your current or future projects.

Read also Setting up MLOps at a Reasonable Scale With Jacopo TagliabueThe pillars of MLOps: core ingredients for a robust MLOps strategyNow that we have a basic understanding of MLOps and its general role in machine learning projects let’s dig deeper to understand what are the key concepts/techniques that will help you implement MLOps best practices in your existing or future projects.

MLOps pillar: reproducibility and versioningOne of the core features of…

1 месяц назад @ neptune.ai
Deploying ML Models: How to Make Sure the New Model Is Better Than the One in Production? [Practical Guide]
Deploying ML Models: How to Make Sure the New Model Is Better Than the One in Production? [Practical Guide] Deploying ML Models: How to Make Sure the New Model Is Better Than the One in Production? [Practical Guide]

But before deploying a new model, we need to make sure that it’s indeed a better model than the old one.

Lastly, we have to point out the importance of testing before deploying an ML model.

ML model deployment is a process of integrating the model into an existing production environment to make practical business decisions.

ML models almost always require deployment to provide business value, but unfortunately, most of the models never make it to production.

Models run asynchronously, firstly the old model in production and after the new shadow model.

1 месяц, 1 неделя назад @ neptune.ai
Leveraging Unlabeled Image Data With Self-Supervised Learning or Pseudo Labeling With Mateusz Opala
Leveraging Unlabeled Image Data With Self-Supervised Learning or Pseudo Labeling With Mateusz Opala Leveraging Unlabeled Image Data With Self-Supervised Learning or Pseudo Labeling With Mateusz Opala

Every episode is focused on one specific ML topic, and during this one, we talked to Mateusz Opala about leveraging unlabeled image data with self-supervised learning or pseudo-labeling.

Sabine: With us today, we have Mateusz Opala, who is going to be answering questions about leveraging unlabeled image data with self-supervised learning or pseudo-labeling.

Can you walk us through some of the different use cases where you apply pseudo-labeling for image data in Brainly?

Mateusz: In general, most of the techniques we use it’s still supervised learning, and we label data, but it’s limited and it’s time-consuming.

Mateusz:My biggest challenge right now is connecting all the steps in the whole …

1 месяц, 1 неделя назад @ neptune.ai
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…

1 месяц, 1 неделя 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 месяц, 2 недели 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 месяц, 2 недели 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…

1 месяц, 4 недели 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.

2 месяца 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.

2 месяца, 1 неделя назад @ neptune.ai
▶️ YouTube
Yannic Kilcher Yannic Kilcher
последний пост 1 неделя, 1 день назад
[ML News] Stable Diffusion Takes Over! (Open Source AI Art)
[ML News] Stable Diffusion Takes Over! (Open Source AI Art) [ML News] Stable Diffusion Takes Over! (Open Source AI Art)

#stablediffusion #aiart #mlnews Stable Diffusion has been released and is riding a wave of creativity and collaboration. But not everyone is happy about this... Sponsor: NVIDIA

GPU Raffle: https://ykilcher.com/gtc OUTLINE:

0:00 - Introduction

0:30 - What is Stable Diffusion?

2:25 - Open-Source Contributions and Creations

7:55 - Textual Inversion

9:30 - OpenAI vs Open AI

14:20 - Journalists be outraged

16:20 - AI Ethics be even more outraged

19:45 - Do we need a new social contract?

21:30 - More applications

22:55 - Helpful Things

23:45 - Sponsor: NVIDIA (& how to enter the GPU raffle) References: https://early-hair-c20.notion.site/Stable-Diffusion-Takes-Over-Referenes-7a2f45b8f7e04ae0ba19db…

1 неделя, 1 день назад @ youtube.com
How to make your CPU as fast as a GPU - Advances in Sparsity w/ Nir Shavit
How to make your CPU as fast as a GPU - Advances in Sparsity w/ Nir Shavit How to make your CPU as fast as a GPU - Advances in Sparsity w/ Nir Shavit

#ai #sparsity #gpu Sparsity is awesome, but only recently has it become possible to properly handle sparse models at good performance. Neural Magic does exactly this, using a plain CPU. No specialized hardware needed, just clever algorithms for pruning and forward-propagation of neural networks. Nir Shavit and I talk about how this is possible, what it means in terms of applications, and why sparsity should play a much larger role in the Deep Learning community. Sponsor: AssemblyAI

Link: https://www.assemblyai.com/?utm_source=youtube&utm_medium=social&utm_campaign=yannic_autochapters Check out Neural Magic: https://neuralmagic.com/

and DeepSparse: https://github.com/neuralmagic/deepsparse O…

1 неделя, 2 дня назад @ youtube.com
More Is Different for AI - Scaling Up, Emergence, and Paperclip Maximizers (w/ Jacob Steinhardt)
More Is Different for AI - Scaling Up, Emergence, and Paperclip Maximizers (w/ Jacob Steinhardt) More Is Different for AI - Scaling Up, Emergence, and Paperclip Maximizers (w/ Jacob Steinhardt)

#ai #interview #research Jacob Steinhardt believes that future AI systems will be fundamentally different than the ones we know currently. We talk about how emergence happens when scaling up, what implications that has on AI Safety, and why thought experiments like the Paperclip Maximizer might be more useful than most people think. OUTLINE:

0:00 Introduction

1:10 Start of Interview

2:10 Blog posts series

3:56 More Is Different for AI (Blog Post)

7:40 Do you think this emergence is mainly a property from the interaction of things?

9:17 How does phase transition or scaling-up play into AI and Machine Learning?

12:10 GPT-3 as an example of qualitative difference in scaling up

14:08 GPT-3 as a…

1 неделя, 6 дней назад @ youtube.com
The hidden dangers of loading open-source AI models (ARBITRARY CODE EXPLOIT!)
The hidden dangers of loading open-source AI models (ARBITRARY CODE EXPLOIT!) The hidden dangers of loading open-source AI models (ARBITRARY CODE EXPLOIT!)

#huggingface #pickle #exploit Did you know that something as simple as loading a model can execute arbitrary code on your machine? Try the model: https://huggingface.co/ykilcher/totally-harmless-model

Get the code: https://github.com/yk/patch-torch-save Sponsor: Weights & Biases

Go here: https://wandb.me/yannic OUTLINE:

0:00 - Introduction

1:10 - Sponsor: Weights & Biases

3:20 - How Hugging Face models are loaded

5:30 - From PyTorch to pickle

7:10 - Understanding how pickle saves data

13:00 - Executing arbitrary code

15:05 - The final code

17:25 - How can you protect yourself? Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTube: https://www.youtube.com/c/yannicki…

3 недели, 3 дня назад @ youtube.com
The Future of AI is Self-Organizing and Self-Assembling (w/ Prof. Sebastian Risi)
The Future of AI is Self-Organizing and Self-Assembling (w/ Prof. Sebastian Risi) The Future of AI is Self-Organizing and Self-Assembling (w/ Prof. Sebastian Risi)

#ai #selforganization #emergence Read Sebastian's article here: https://sebastianrisi.com/self_assembling_ai/ OUTLINE:

0:00 - Introduction

2:25 - Start of Interview

4:00 - The intelligence of swarms

9:15 - The game of life & neural cellular automata

14:10 - What's missing from neural CAs?

17:20 - How does local computation compare to centralized computation?

25:40 - Applications beyond games and graphics

33:00 - Can we do away with goals?

35:30 - Where do these methods shine?

43:30 - The paradox of scales & brains

49:45 - Connections to graphical systems & GNNs

51:30 - Could this solve ARC?

57:45 - Where can people get started? References:

https://sebastianrisi.com/

https://modl.ai/

https:/…

1 месяц назад @ youtube.com
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…

1 месяц, 2 недели 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…

1 месяц, 2 недели 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 месяц, 2 недели 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…

1 месяц, 3 недели 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 месяца 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…

2 месяца, 3 недели назад @ youtube.com
ARC Challenge Live Coding
ARC Challenge Live Coding ARC Challenge Live Coding

Chatting & Coding

2 месяца, 3 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…

3 месяца 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…

3 месяца 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/…

3 месяца, 1 неделя назад @ youtube.com
Henry AI Labs Henry AI Labs
последний пост 1 месяц, 3 недели 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 месяц, 3 недели 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

2 месяца, 2 недели 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…

3 месяца, 4 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

5 месяцев, 3 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…

6 месяцев 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

6 месяцев, 1 неделя 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

6 месяцев, 1 неделя 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

6 месяцев, 1 неделя 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

6 месяцев, 2 недели 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

6 месяцев, 4 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

7 месяцев, 2 недели 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…

7 месяцев, 3 недели 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

7 месяцев, 3 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…

7 месяцев, 4 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…

8 месяцев назад @ youtube.com
3blue1brown 3blue1brown
последний пост 2 месяца, 3 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/…

2 месяца, 3 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…

3 месяца, 2 недели 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 -----------------…

4 месяца 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…

7 месяцев, 2 недели 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…

7 месяцев, 3 недели 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…

9 месяцев, 1 неделя назад @ youtube.com
Two Minute Papers Two Minute Papers
последний пост 1 день, 14 часов назад
Google's New AI: Dog Goes In, Statue Comes Out! 🗽
Google's New AI: Dog Goes In, Statue Comes Out! 🗽 Google's New AI: Dog Goes In, Statue Comes Out! 🗽

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation" is available here:

https://dreambooth.github.io/ AI Image interpolation:

https://twitter.com/xsteenbrugge/status/1558508866463219712 Felícia Zsolnai-Fehér’s works:

https://twitter.com/twominutepapers/status/1534817417238614017 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 🙏 W…

1 день, 14 часов назад @ youtube.com
NVIDIA’s New AI: Beautiful Simulations, Cheaper! 💨
NVIDIA’s New AI: Beautiful Simulations, Cheaper! 💨 NVIDIA’s New AI: Beautiful Simulations, Cheaper! 💨

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks" is available here:

https://developer.nvidia.com/rendering-technologies/neuralvdb

https://blogs.nvidia.com/blog/2022/08/09/neuralvdb-ai/

https://arxiv.org/abs/2208.04448 📝 The paper with the water simulation is available here: 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 🙏 We would li…

5 дней, 13 часов назад @ youtube.com
NVIDIA’s AI: DeepFakes And Virtual Avatars!
NVIDIA’s AI: DeepFakes And Virtual Avatars! NVIDIA’s AI: DeepFakes And Virtual Avatars!

❤️ Check out Weights & Biases and say hi in their community forum here: https://wandb.me/paperforum 📝 The paper "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing" is available here:

https://nvlabs.github.io/face-vid2vid/ Try it out: http://imaginaire.cc/vid2vid-cameo/ ❤️ 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…

1 неделя, 1 день назад @ youtube.com
DeepMind’s AlphaFold: 200 More Gifts To Humanity! 🧬
DeepMind’s AlphaFold: 200 More Gifts To Humanity! 🧬 DeepMind’s AlphaFold: 200 More Gifts To Humanity! 🧬

❤️ Check out Cohere and sign up for free today: https://cohere.ai/papers 📝 The paper "Highly accurate protein structure prediction with #AlphaFold" is available here:

https://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universe

https://www.nature.com/articles/s41586-021-03819-2

https://www.deepmind.com/blog/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology Database:

https://www.uniprot.org/ ❤️ 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 Min…

1 неделя, 6 дней назад @ youtube.com
Google’s New AI: Fly INTO Photos! 🐦
Google’s New AI: Fly INTO Photos! 🐦 Google’s New AI: Fly INTO Photos! 🐦

❤️ Train a neural network and track your experiments with Weights & Biases here: http://wandb.me/paperintro 📝 The paper "InfiniteNature-Zero Learning Perpetual View Generation of Natural Scenes from Single Images" is available here:

https://infinite-nature.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, Christian Ahlin, Eric Martel, Geronimo Morale…

2 недели, 1 день назад @ youtube.com
Stable Diffusion: DALL-E 2 For Free, For Everyone!
Stable Diffusion: DALL-E 2 For Free, For Everyone! Stable Diffusion: DALL-E 2 For Free, For Everyone!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "High-Resolution Image Synthesis with Latent Diffusion Models" is available here:

https://ommer-lab.com/research/latent-diffusion-models/

https://github.com/mallorbc/stable-diffusion-klms-gui ❗Try it here: https://huggingface.co/spaces/stabilityai/stable-diffusion

❗Or here: https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb Great notebooks to try:

https://www.reddit.com/r/StableDiffusion/comments/wzk78c/colab_notebook_sd_hiki_by_daswerq123_has_a/

https://github.com/pinilpypinilpy/sd-webui-colab-simplified

https://github.com/victordibia/pe…

2 недели, 6 дней назад @ youtube.com
A 1,000,000,000 Particle Simulation! 🌊
A 1,000,000,000 Particle Simulation! 🌊 A 1,000,000,000 Particle Simulation! 🌊

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers 📝 The paper "A Fast Unsmoothed Aggregation Algebraic Multigrid Framework for the Large-Scale Simulation of Incompressible Flow" is available here:

http://computationalsciences.org/publications/shao-2022-multigrid.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, Christ…

3 недели, 1 день назад @ youtube.com
OpenAI’s DALL-E 2 - AI-Based Art Is Here! 🧑‍🎨
OpenAI’s DALL-E 2 - AI-Based Art Is Here! 🧑‍🎨 OpenAI’s DALL-E 2 - AI-Based Art Is Here! 🧑‍🎨

❤️ 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/

https://openai.com/blog/dall-e-2-extending-creativity/ 🧑‍🎨 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/ Credits:

Stan Brown - https://www.artstation.com/artwork/Le94a5

Road maker - https://twitter.com/Bbbn192/status/1550150050562674692

GuyP - https://twitter.com/GuyP/status/1552681944437166081 + https://dallery.gallery/free-photo-image-ed…

3 недели, 3 дня назад @ youtube.com
Microsoft’s New AI: The Selfies Of The Future! 🤳
Microsoft’s New AI: The Selfies Of The Future! 🤳 Microsoft’s New AI: The Selfies Of The Future! 🤳

❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd 📝 The paper "GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds" is available here:

https://jeffreyxiang.github.io/GRAM-HD/ ❤️ 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, Iv…

4 недели, 1 день назад @ youtube.com
Is Google’s New AI As Smart As A Human? 🤖
Is Google’s New AI As Smart As A Human? 🤖 Is Google’s New AI As Smart As A Human? 🤖

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "Minerva - Solving Quantitative Reasoning Problems with Language Models" is available here:

https://arxiv.org/abs/2206.14858 ❤️ 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 Lukic, Joh…

1 месяц назад @ youtube.com
Microsoft's New AI: Virtual Humans Became Real! 🤯
Microsoft's New AI: Virtual Humans Became Real! 🤯 Microsoft's New AI: Virtual Humans Became Real! 🤯

❤️ Check out Runway and try it for free here: https://runwayml.com/papers/ 📝 The paper "3D Face Reconstruction with Dense Landmarks" is available here:

https://microsoft.github.io/DenseLandmarks/ 🙏 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 Tedder, Nevin Spoljaric, Nikhil Velpanur, Owen Campbell-Moore, O…

1 месяц, 1 неделя назад @ youtube.com
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…

1 месяц, 1 неделя 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…

1 месяц, 2 недели 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 месяц, 2 недели 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 месяц, 3 недели назад @ youtube.com
DataFest Video DataFest Video
последний пост None
Семинары JetBrains Research Семинары JetBrains Research
последний пост 3 месяца, 3 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

3 месяца, 3 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

4 месяца 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…

4 месяца, 1 неделя назад @ youtube.com
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO
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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…

5 месяцев назад @ youtube.com
Predicting What You Already Know Helps: Provable Self-Supervised Learning
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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.. …

5 месяцев, 1 неделя назад @ youtube.com
Emerging Properties in Self-Supervised Vision Transforms
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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.

5 месяцев, 1 неделя 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.

5 месяцев, 2 недели назад @ youtube.com
Block-Recurrent Transformers
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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…

5 месяцев, 2 недели назад @ youtube.com
Assessing Project-Level Fine-Tuning of ML4SE Models
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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

5 месяцев, 2 недели назад @ youtube.com
Предсказание типов для исходного кода с использованием графовых нейронных сетей
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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…

5 месяцев, 2 недели назад @ youtube.com
Industry-scale IR-based Bug Localization: A Perspective from Facebook
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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…

5 месяцев, 2 недели назад @ youtube.com
Code Smells for Machine Learning Applications
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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…

5 месяцев, 2 недели назад @ youtube.com
Fastformer: Additive Attention Can Be All You Need
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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

5 месяцев, 2 недели назад @ youtube.com
Language Models are Unsupervised Multitask Learners
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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

5 месяцев, 3 недели назад @ youtube.com
Neural Code Completion: Research & Practice
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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.

7 месяцев назад @ youtube.com
Яндекс. Компьютерные науки Яндекс. Компьютерные науки
последний пост 1 неделя назад
Data Dojo — ML тренировка 22 сентября 2022
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Data Dojo — тренировки по машинному обучению и место встречи специалистов в сфере анализа данных. Задавайте вопросы спикерам в телеграм-чате (https://t.me/+OsKnLNG-7DE1ZTFi) с хештегом #вопрос, чтобы ведущий зачитал их в прямом эфире. Программа: 19:00 — Открытие

19:05 — Бенчмарк приемлемости предложений на русском языке (RuCoLA) + секретный релиз / Максим Рябинин (Яндекс)

19:40 — Перерыв 20:00 — Верификация моделей автомобилей (Machines Can See 2022) / Дмитрий Гаус (VisionLabs) и Артём Стрекалов (АО Уфанет)

1 неделя назад @ youtube.com
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

5 месяцев 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

5 месяцев 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

5 месяцев назад @ youtube.com
Модели вычислений. Анализ учетных стоимостей. Часть 1
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Время и память как основные ресурсы. RAM машина. Сложность на заданном входе, сложность в худшем случае, сложность в среднем случае, рандомизированная сложность.

Учетная стоимость операций, метод потенциалов, банковский метод анализа сложности.

Массивы переменного размера. Реаллокация. Анализ учетной сложности операции push-back. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

5 месяцев назад @ youtube.com
Модели вычислений. Анализ учетных стоимостей. Часть 2
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Время и память как основные ресурсы. RAM машина. Сложность на заданном входе, сложность в худшем случае, сложность в среднем случае, рандомизированная сложность.

Учетная стоимость операций, метод потенциалов, банковский метод анализа сложности.

Массивы переменного размера. Реаллокация. Анализ учетной сложности операции push-back. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

5 месяцев назад @ youtube.com
Очередь и стэки. Иммутабельность и персистентность
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Реализация очереди на паре стеков с константной учетной сложностью. Динамические минимумы-максимумы в стеках и очередях. Персистентные структуры данных. Виды персистентности. Модель вычислений Pointer Machine. Персистентные стеки и очереди. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

5 месяцев назад @ youtube.com
Misra-Gries. Деревья поиска. RB-деревья. Декартовы деревья и дучи.
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Алгоритм Misra-Gries.

Деревья поиска. Вставка и удаление элементов. Inorder-обход дерева. Красно черные деревья: определение и основные свойства. Реализация операций вставки для красно-черного дерева. Дучи (treaps). Единственность дучи для заданного набора различных ключей и приоритетов. Логарифмическая оценка матожидания высоты дучи. Операции слияния и разделения для дуч. Операции вставки и удаления элементов для дуч. Подробнее о поступлении в Школу анализа данных: https://academy.yandex.ru/dataschool

5 месяцев назад @ youtube.com
Быстрая сортировка и сортировка слиянием 2. Бинарный поиск. Длиннейшая возрастающая подпоследователь
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Быстрая сортировка (Quick-Sort). Способы выбора разделяющего элемента. Элиминация хвостовой рекурсии. Порядковые статистики. Рандомизированный алгоритм Quick-Select. Детермининированный алгоритм поиска (метод "медианы медиан").

Бинарный поиск. Galloping.

Линейное по времени слияние упорядоченных последовательностей. Оптимальное по числу сравнений слияние упорядоченных последовательностей.

Задача о длиннейшей возврастающей подпоследовательности. Динамическое программирование. O(n log n)-алгоритм. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

5 месяцев назад @ youtube.com
Задачи RMQ и LCA. Часть 1
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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

5 месяцев назад @ youtube.com
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Репозиторий соревнования (БКИ): https://github.com/SmirnovValeriy/dl-fintech-bki

Tg-канал “Нескучный Data Science”: https://t.me/not_boring_ds

1 месяц 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

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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 месяца назад @ youtube.com
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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

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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

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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

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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

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- 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

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- 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

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- 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

2 месяца, 1 неделя назад @ youtube.com
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- 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

2 месяца, 1 неделя назад @ youtube.com
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- 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

2 месяца, 1 неделя назад @ youtube.com
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https://ourworldindata.org/longtermism

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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

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- Twitter: @primerlearning

- Reddit: r/primerlearning Plush blobs and other merch: https://store.dftba.com/collections/primer

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2 дня, 12 часов назад @ lexfridman.com
#322 – Rana el Kaliouby: Emotion AI, Social Robots, and Self-Driving Cars
#322 – Rana el Kaliouby: Emotion AI, Social Robots, and Self-Driving Cars #322 – Rana el Kaliouby: Emotion AI, Social Robots, and Self-Driving Cars

Rana el Kaliouby is a pioneer in the field of emotion recognition and human-centric AI.

She is the founder of Affectiva, deputy CEO of Smart Eye, and author of Girl Decoded.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– Weights & Biases: https://lexfridman.com/wnb– Notion: https://notion.com– InsideTracker: https://insidetracker.com/lex to get 20% off– ExpressVPN: https://expressvpn.com/lexpod to get 3 months freeEPISODE LINKS:Rana’s Twitter: https://twitter.com/kalioubyRana’s Instagram: https://instagram.com/ranaelkalioubyRana’s Facebook: https://facebook.com/RanaelKalioubyAffectiva (website): https://affe…

5 дней, 13 часов назад @ lexfridman.com
#321 – Ray Kurzweil: Singularity, Superintelligence, and Immortality
#321 – Ray Kurzweil: Singularity, Superintelligence, and Immortality #321 – Ray Kurzweil: Singularity, Superintelligence, and Immortality

Ray Kurzweil is an author, inventor, and futurist.

Please support this podcast by checking out our sponsors:– Shopify: https://shopify.com/lex to get 14-day free trial– NetSuite: http://netsuite.com/lex to get free product tour– Linode: https://linode.com/lex to get $100 free credit– MasterClass: https://masterclass.com/lex to get 15% off– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Ray’s Website: https://kurzweilai.netRay’s Books:The Singularity Is Nearer (pre-order): https://amzn.to/3BNXmGRHow To Create A Mind: https://amzn.to/3qqlkBwThe Singularity Is Near: https://amzn.to/3DfXP5zThe Age of Spiritual Machines: https://amzn.to/3RSjtAXDanielle: https://amzn.to/3Bww2N7Tran…

1 неделя, 2 дня назад @ lexfridman.com
#320 – Christopher Capozzola: World War I, Ideology, Propaganda, and Politics
#320 – Christopher Capozzola: World War I, Ideology, Propaganda, and Politics #320 – Christopher Capozzola: World War I, Ideology, Propaganda, and Politics

Christopher Capozzola is a professor of history at MIT.

Please support this podcast by checking out our sponsors:– Wealthfront: https://wealthfront.com/lex to get $50 sign-up bonus– InsideTracker: https://insidetracker.com/lex to get 20% off– LMNT: https://drinkLMNT.com/lex to get free sample pack– SimpliSafe: https://simplisafe.com/lexEPISODE LINKS:Christopher’s Instagram: https://instagram.com/boundbywarbookChristopher’s Website: https://boundbywarbook.comChristopher’s books:Bound by War: https://amzn.to/3QssboAUncle Sam Wants You: https://amzn.to/3Bw5KdTPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS…

1 неделя, 5 дней назад @ lexfridman.com
#319 – Botez Sisters: Chess, Streaming, and Fame
#319 – Botez Sisters: Chess, Streaming, and Fame #319 – Botez Sisters: Chess, Streaming, and Fame

Alexandra and Andrea Botez are chess players, commentators, educators, entertainers, and streamers.

Please support this podcast by checking out our sponsors:– Calm: https://calm.com/lex to get 40% off premium– Weights & Biases: https://lexfridman.com/wnb– BiOptimizers: http://www.magbreakthrough.com/lex to get 10% off– InsideTracker: https://insidetracker.com/lex to get 20% off– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:BotezLive Twitch: https://twitch.tv/botezliveBotezLive YouTube: https://youtube.com/c/BotezLiveBotezLive Instagram: https://instagram.com/botezliveAlexandra’s Instagram: https://instagram.com/missbotezAndrea’s Instagram: https://instagram.com/itsandreabot…

2 недели, 3 дня назад @ lexfridman.com
#318 – Nick Lane: Origin of Life, Evolution, Aliens, Biology, and Consciousness
#318 – Nick Lane: Origin of Life, Evolution, Aliens, Biology, and Consciousness #318 – Nick Lane: Origin of Life, Evolution, Aliens, Biology, and Consciousness

Nick Lane is a biochemist at UCL and author of Transformer, The Vital Question, and many other amazing books on biology, chemistry, and life.

Please support this podcast by checking out our sponsors:– Backbone: https://playbackbone.com/lex to get perks with order– Notion: https://notion.com– BetterHelp: https://betterhelp.com/lex to get 10% off– Blinkist: https://blinkist.com/lex to get 25% off premiumEPISODE LINKS:Nick’s Website: https://nick-lane.netNick’s Books:Transformer: https://amzn.to/3cy7lpOThe Vital Question: https://amzn.to/3q0vN6qOxygen: https://amzn.to/3edy3V5Power, Sex, Suicide: https://amzn.to/3B3OInkLife Ascending: https://amzn.to/3wKIsOEBooks mentioned:21 Lessons for the 21…

2 недели, 5 дней назад @ lexfridman.com
#317 – John Vervaeke: Meaning Crisis, Atheism, Religion & the Search for Wisdom
#317 – John Vervaeke: Meaning Crisis, Atheism, Religion & the Search for Wisdom #317 – John Vervaeke: Meaning Crisis, Atheism, Religion & the Search for Wisdom

John Vervaeke is a psychologist and cognitive scientist at University of Toronto.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– InsideTracker: https://insidetracker.com/lex to get 20% off– Eight Sleep: https://www.eightsleep.com/lex to get special savings– Athletic Greens: https://athleticgreens.com/lex to get 1 month of fish oil– Onnit: https://lexfridman.com/onnit to get up to 10% offEPISODE LINKS:John’s YouTube: https://youtube.com/johnvervaekeJohn’s Twitter: https://twitter.com/vervaeke_johnJohn’s Facebook: https://facebook.com/VervaekeJohnJohn’s Website: https://johnvervaeke.comBooks mentioned:Flow: ht…

3 недели, 1 день назад @ lexfridman.com
#316 – Noam Chomsky: Putin, Ukraine, China, and Nuclear War
#316 – Noam Chomsky: Putin, Ukraine, China, and Nuclear War #316 – Noam Chomsky: Putin, Ukraine, China, and Nuclear War

Noam Chomsky is a linguist, philosopher, and political activist.

Please support this podcast by checking out our sponsors:– Skiff: https://skiff.com/lex– InsideTracker: https://insidetracker.com/lex to get 20% off– Onnit: https://lexfridman.com/onnit to get up to 10% off– Blinkist: https://blinkist.com/lex to get 25% off premiumEPISODE LINKS:Noam’s Website: https://chomsky.info/Noam’s Instagram: https://instagram.com/noam.chomskyofficialManufacturing Consent (book): https://amzn.to/3KaEc0dPODCAST 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://…

3 недели, 5 дней назад @ lexfridman.com
#315 – Magnus Carlsen: Greatest Chess Player of All Time
#315 – Magnus Carlsen: Greatest Chess Player of All Time #315 – Magnus Carlsen: Greatest Chess Player of All Time

Magnus Carlsen is the highest-rated chess player in history and widely considered to be the greatest chess player of all time.

Please support this podcast by checking out our sponsors:– Shopify: https://shopify.com/lex to get 14-day free trial– Athletic Greens: https://athleticgreens.com/lex to get 1 month of fish oil– Fundrise: https://fundrise.com/lex– BetterHelp: https://betterhelp.com/lex to get 10% off– InsideTracker: https://insidetracker.com/lex to get 20% offEPISODE LINKS:Magnus’s Twitter: https://twitter.com/MagnusCarlsenMagnus’s Instagram: https://instagram.com/magnus_carlsenMagnus’s YouTube: https://youtube.com/c/themagnuscarlsenMagnus’s Website: https://magnuscarlsen.comPODCAST …

1 месяц назад @ lexfridman.com
#314 – Liv Boeree: Poker, Game Theory, AI, Simulation, Aliens & Existential Risk
#314 – Liv Boeree: Poker, Game Theory, AI, Simulation, Aliens & Existential Risk #314 – Liv Boeree: Poker, Game Theory, AI, Simulation, Aliens & Existential Risk

Liv Boeree is a poker champion and science educator on topics of game theory, physics, complexity, and life.

Please support this podcast by checking out our sponsors:– Audible: https://audible.com/lex to get 30-day free trial– GiveWell: https://www.givewell.org and use code Lex Fridman Podcast– Linode: https://linode.com/lex to get $100 free credit– Indeed: https://indeed.com/lex to get $75 credit– ExpressVPN: https://expressvpn.com/lexpod to get 3 months freeEPISODE LINKS:Liv’s Twitter: https://twitter.com/liv_boereeLiv’s Instagram: https://instagram.com/liv_boereeLiv’s Facebook: https://facebook.com/livboereeLiv’s YouTube: https://youtube.com/user/LivBoereeBooks and resources mentioned:No…

1 месяц назад @ lexfridman.com
#313 – Jordan Peterson: Life, Death, Power, Fame, and Meaning
#313 – Jordan Peterson: Life, Death, Power, Fame, and Meaning #313 – Jordan Peterson: Life, Death, Power, Fame, and Meaning

Jordan Peterson is a psychologist, lecturer, podcast host, and author.

Please support this podcast by checking out our sponsors:– Weights & Biases: https://lexfridman.com/wnb– Notion: https://notion.com/startups to get up to $1000 off team plan– InsideTracker: https://insidetracker.com/lex to get 20% off– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– Blinkist: https://blinkist.com/lex to get 25% off premiumEPISODE LINKS:Jordan’s Twitter: https://twitter.com/jordanbpetersonJordan’s Website: https://jordanbpeterson.comJordan’s Books:Beyond Order: https://amzn.to/3T4LRBw12 Rules for Life: https://amzn.to/3c4sqYFMaps of Meaning: https://amzn.to/3A1Ods2Webs…

1 месяц, 1 неделя назад @ lexfridman.com
#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 месяц, 1 неделя 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…

1 месяц, 2 недели 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 месяц, 2 недели 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 месяц, 3 недели назад @ lexfridman.com
Microsoft Research Podcast Microsoft Research Podcast
последний пост 5 месяцев, 2 недели 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…

5 месяцев, 2 недели 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…

5 месяцев, 4 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…

6 месяцев, 1 неделя назад @ blubrry.com
Data Skeptic
последний пост 16 часов назад
First Party Tracking Cookies
First Party Tracking Cookies First Party Tracking Cookies

Shaoor, whose research interest lies around the development and evaluation of privacy-preserving technologies, joins us to discuss his recent publication titled, COOKIEGRAPH: Measuring and Countering First-Party Tracking Cookies.

Shaoor began the conversation with an overview of privacy-preserving technologies.

According to him, the field of privacy-preserving technologies is ever evolving and there is a lot more awareness about it today than ever before.

Shaoor discussed the reaction of advertisers to this development, one of which includes migrating to first-party cookies.

Shaoor discussed the model prediction accuracy.

16 часов назад @ dataskeptic.com
The Harms of Targeted Weight Loss Ads
The Harms of Targeted Weight Loss Ads The Harms of Targeted Weight Loss Ads

The Harms of Targeted Weight Loss AdsToday, we are joined by Liza Gak, a Ph.D student at UC Berkeley.

Liza’s research interest lies around human-computer interaction (HCI), social computing, and how people are harmed online.

Liza explained how she grouped and coded the qualitative data using the inductive iterative approach.

She spoke about her findings, iterating how weight loss ads target the vulnerable.

She also explained how ad distribution platforms can play a role in ameliorating the harm ads cause to users.

1 неделя назад @ dataskeptic.com
Podcast Advertising
Podcast Advertising Podcast Advertising

Podcast AdvertisingToday, we are joined by Rob Walch, the VP of Podcast Relations at Libsyn.

Libsyn is a popular podcast hosting platform, where the Data Skeptic podcast is hosted.

He then explained how podcasters can monetize their podcasts using Host Read.

Rob then discusses how to engage the podcast audience using surveys.

He also explained why iOS users have 5 times more podcast listeners than Android users even though there are 5X more Android phones than iPhones.

2 недели назад @ dataskeptic.com
Fairness in e-Commerce Search
Fairness in e-Commerce Search Fairness in e-Commerce Search

Fairness in e-commerce SearchOn the show today, we are joined by Abhisek Dash and Saptarshi Ghosh.

Fairness and Interpretability Issues in E-commerce Search through Smart Speakers.

Abhisek started by giving some background on what fairness in machine learning is.

He also explained what it means to audit machine learning systems.

They both discussed some concerning discrepancies in search results between Amazon smart speakers and the desktop website.

3 недели назад @ dataskeptic.com
Fraudulent Amazon Reviewers
Fraudulent Amazon Reviewers Fraudulent Amazon Reviewers

Fraudulent Amazon ReviewersOn the show today, we are joined by Rajvardhan Oak, an applied Scientist at Microsoft.

Raj delved deeper into how these fraudulent reviews work with real-life scenarios.

Raj discussed the quantitative and qualitative analysis he carried out on the dataset.

Rounding up, Raj discussed how fraudulent review companies avoid being detected by Amazon.

He finally discussed recommendations to forestall fraudulent reviews on e-commerce platforms.

4 недели назад @ dataskeptic.com
Ad Targeting in Amazon Smart Speakers
Ad Targeting in Amazon Smart Speakers Ad Targeting in Amazon Smart Speakers

Ad Targeting in Amazon Smart SpeakersOur guest today is Umar Iqbal.

Umar joins us to discuss his study on ad targeting in Amazon smart speakers.

While the regulatory bodies require a level of transparency in the usage of users’ data, Umar explained how their regulations are barely effective.

Umar went further to explain what makes voice data special, and the possibilities it brings when mined.

He also discussed the involvement of third-party skills in collecting and sharing users’ voice data.

1 месяц назад @ dataskeptic.com
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.

1 месяц, 1 неделя 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.

1 месяц, 2 недели 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 месяц, 2 недели 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.

1 месяц, 3 недели 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.

2 месяца 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.

2 месяца, 1 неделя 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.

2 месяца, 1 неделя 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.

2 месяца, 1 неделя 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.

2 месяца, 2 недели назад @ dataskeptic.com
SuperDataScience SuperDataScience
последний пост 3 дня, 18 часов назад
SDS 612: More Guests on Fridays
SDS 612: More Guests on Fridays SDS 612: More Guests on Fridays

Some exciting changes are coming to our popular Five-Minute Friday series!

From longer episodes to new guests, tune in to hear what's next.

Additional materials: www.superdatascience.com/612Interested in sponsoring a…

3 дня, 18 часов назад @ soundcloud.com
SDS 611: Open-Ended A.I.: Practical Applications for Humans and Machines
SDS 611: Open-Ended A.I.: Practical Applications for Humans and Machines SDS 611: Open-Ended A.I.: Practical Applications for Humans and Machines

Dr. Ken Stanley, a world-leading expert on Open-Ended AI and author of the genre-bending book "Why Greatness Cannot be Planned," joins Jon Krohn for a discussion that has the potential to shift your entire view on life.

6 дней, 18 часов назад @ soundcloud.com
SDS 610: Who Dares Wins
SDS 610: Who Dares Wins SDS 610: Who Dares Wins

On this episode of Five-Minute Friday, host Jon Krohn shares his life motto, “Who dares, wins”, and the sentiment behind it: that to get anywhere in life, it is first necessary to try.

Jon believes that “daring”, in this…

1 неделя, 3 дня назад @ soundcloud.com
SDS 609: Data Mesh
SDS 609: Data Mesh SDS 609: Data Mesh

Jon Krohn speaks with Zhamak Dehghani, the empathetic technologist who coined the term “data mesh”.

They explore what a data mesh is, and how its approach toward secure interconnectivity will help solve a roster of data-…

1 неделя, 6 дней назад @ soundcloud.com
607: Inferring Causality
607: Inferring Causality 607: Inferring Causality

Dr. Jennifer Hill, Professor of Applied Statistics at New York University, joins Jon this week for a discussion that covers causality, correlation, and inference in data science.

In this episode you will learn:• How ca…

2 недели, 3 дня назад @ soundcloud.com
SDS 607: Inferring Causality
SDS 607: Inferring Causality SDS 607: Inferring Causality

We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science.

In this episode you will…

2 недели, 4 дня назад @ soundcloud.com
SDS 608: Daily Habit #11: Assigning Deliverables
SDS 608: Daily Habit #11: Assigning Deliverables SDS 608: Daily Habit #11: Assigning Deliverables

Company meetings should be held to solve problems.

So, why do we often feel like the weekly stand-ups and check-ins are a waste of everyone’s time?

On this episode of Five-Minute Friday, host Jon Krohn brings his habit-m…

2 недели, 4 дня назад @ soundcloud.com
SDS 607: Inferring Causality
SDS 607: Inferring Causality SDS 607: Inferring Causality

We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science.

In this episode you will…

2 недели, 6 дней назад @ soundcloud.com
SDS 606: Four Thousand Weeks
SDS 606: Four Thousand Weeks SDS 606: Four Thousand Weeks

Four thousand weeks equate to roughly 80 years—a lifetime for those of us lucky enough to get there.

What do we choose to do with this time?

How can we stop ourselves from feeling like time in general is slipping away?

3 недели, 3 дня назад @ soundcloud.com
SDS 605: Upskilling in Data Science and Machine Learning
SDS 605: Upskilling in Data Science and Machine Learning SDS 605: Upskilling in Data Science and Machine Learning

Kian Katanforoosh, CEO of Workera and Lecturer at Stanford University, joins Jon Krohn to reveal the tools, frameworks, and machine learning models that power his platform and remote team.

In this episode you will learn…

3 недели, 6 дней назад @ soundcloud.com
SDS 604: Ignition: A Landmark Nuclear Fusion Milestone is Achieved
SDS 604: Ignition: A Landmark Nuclear Fusion Milestone is Achieved SDS 604: Ignition: A Landmark Nuclear Fusion Milestone is Achieved

During this week's Five-Minute Friday episode features, Jon explores recent groundbreaking developments in nuclear fusion –ignition–and what that signals for the future.

Additional materials: www.superdatascience.com/60…

1 месяц назад @ soundcloud.com
SDS 603: Geospatial Data and Unconventional Routes into Data Careers
SDS 603: Geospatial Data and Unconventional Routes into Data Careers SDS 603: Geospatial Data and Unconventional Routes into Data Careers

Christina Stathopoulos, Analytical Lead for Waze and Adjunct Professor at IE Business School, joins the podcast to shed light on her work with geospatial data and how she nurtured an entire data career while abroad in Sp…

1 месяц назад @ soundcloud.com
SDS 602: We Are Living in Ancient Times
SDS 602: We Are Living in Ancient Times SDS 602: We Are Living in Ancient Times

Inspired by a quote from by science fiction writer, Teresa Nielsen Hayden, Jon Krohn reflects on the notion of living in ancient times and the machine learning-related implications that arise from this perspective.

1 месяц, 1 неделя назад @ soundcloud.com
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 месяц, 1 неделя 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…

1 месяц, 2 недели назад @ soundcloud.com
Data Science at Home Data Science at Home
последний пост 4 дня назад
Predicting Out Of Memory Kill events with Machine Learning (Ep. 203)
Predicting Out Of Memory Kill events with Machine Learning (Ep. 203) Predicting Out Of Memory Kill events with Machine Learning (Ep. 203)

Can we use machine learning to predict and eventually detect out of memory kills from the operating system?

200:00:09,142 –> 00:00:19,170This time we have something for you if you want to help us shape the data science leaders of the future, we have created the the Data Science at Home’s Ambassador program.

300:00:19,340 –> 00:00:28,378Ambassadors are volunteers who are passionate about data science and want to give back to our growing community of data science professionals and enthusiasts.

1100:01:39,226 –> 00:01:56,218Regardless of your application, is a video streaming application or any other communication type of application, or a fintech application, or energy, or whatever, this memo…

4 дня назад @ datascienceathome.com
Is studying AI in academia a waste of time? (Ep. 202)
Is studying AI in academia a waste of time? (Ep. 202) Is studying AI in academia a waste of time? (Ep. 202)

Companies and other business entities are actively involved in defining data products and applied research every year.

Academia has always played a role in creating new methods and solutions/algorithms in the fields of machine learning and artificial intelligence.

However, there is doubt about how powerful and effective such research efforts are.

Is studying AI in academia a waste of time?

Check it out at https://arcticwolf.com/datascienceAmethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve.

1 неделя, 5 дней назад @ datascienceathome.com
Zero-Cost Proxies: How to find the best neural network without training (Ep. 201)
Zero-Cost Proxies: How to find the best neural network without training (Ep. 201) Zero-Cost Proxies: How to find the best neural network without training (Ep. 201)

Neural networks are becoming massive monsters that are hard to train (without the “regular” 12 last-generation GPUs).

Is there a way to skip that?

Let me introduce you to Zero-Cost proxiesReferences

2 недели, 5 дней назад @ datascienceathome.com
Online learning is better than batch, right? Wrong! (Ep. 200)
Online learning is better than batch, right? Wrong! (Ep. 200) Online learning is better than batch, right? Wrong! (Ep. 200)

In this episode, I speak about online machine learning systems and why blindly choosing such a paradigm can lead to unpredictable and expensive outcomes.

Also, in this episode, I have to deal with an intruder 🙂LinksBirman, K.; Joseph, T. (1987).

“Exploiting virtual synchrony in distributed systems”.

Proceedings of the Eleventh ACM Symposium on Operating Systems Principles – SOSP ’87.

S2CID 7739589.

2 недели, 5 дней назад @ datascienceathome.com
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

3 месяца, 3 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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4 месяца 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

4 месяца, 1 неделя 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

4 месяца, 1 неделя 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.

4 месяца, 1 неделя 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

5 месяцев, 1 неделя 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.

5 месяцев, 1 неделя 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.

5 месяцев, 1 неделя 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…

5 месяцев, 1 неделя 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.

5 месяцев, 4 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…

5 месяцев, 4 недели назад @ datascienceathome.com