Naftali Tishby - The Information Bottleneck View of Deep Learning: Why do we need it?

Speaker: Naftali Tishby Title: The Information Bottleneck View of Deep Learning: Why do we need it? Presented at the 2019 Conference on the Mathematical Theory of Deep Learning (DeepMath 2019)

Boris Hanin - NTK in ReLU Nets with Finite Depth and Width
▶︎

Boris Hanin - NTK in ReLU Nets with Finite Depth and Width

The Information Bottleneck Theory of Deep Neural Networks...
▶︎

The Information Bottleneck Theory of Deep Neural Networks...

001. Information Theory of Deep Learning - Naftali Tishby
▶︎

001. Information Theory of Deep Learning - Naftali Tishby

Concept Bottleneck Models (Paper Explained)
▶︎

Concept Bottleneck Models (Paper Explained)

Terence Tao: Nobody Understands Why AI Actually Works
▶︎

Terence Tao: Nobody Understands Why AI Actually Works

Optimal Transport and Information Geometry for  Machine Learning and Data Science
▶︎

Optimal Transport and Information Geometry for Machine Learning and Data Science

The Uncomfortable Truth About AI “Reasoning” | World Science Festival
▶︎

The Uncomfortable Truth About AI “Reasoning” | World Science Festival

Sanjeev Arora: Why do deep nets generalize, that is, predict well on unseen data
▶︎

Sanjeev Arora: Why do deep nets generalize, that is, predict well on unseen data

Stanford Seminar - Information Theory of Deep Learning, Naftali Tishby
▶︎

Stanford Seminar - Information Theory of Deep Learning, Naftali Tishby

Why LeCun Thinks Deep Learning Isn't Enough — Yann LeCun
▶︎

Why LeCun Thinks Deep Learning Isn't Enough — Yann LeCun

Web Scraping Using Python For Beginners and File Handling in Python | Python Web Scraping
▶︎

Web Scraping Using Python For Beginners and File Handling in Python | Python Web Scraping

Stephanie Palmer: "Information bottleneck approaches to quantifying prediction in the brain"
▶︎

Stephanie Palmer: "Information bottleneck approaches to quantifying prediction in the brain"

Transformers, the tech behind LLMs | Deep Learning Chapter 5
▶︎

Transformers, the tech behind LLMs | Deep Learning Chapter 5

Michael Elad - Sparse Modelling of Data and its Relation to Deep Learning
▶︎

Michael Elad - Sparse Modelling of Data and its Relation to Deep Learning

Bayesian Deep Learning and Probabilistic Model Construction - ICML 2020 Tutorial
▶︎

Bayesian Deep Learning and Probabilistic Model Construction - ICML 2020 Tutorial

AlphaFold - The Most Useful Thing AI Has Ever Done
▶︎

AlphaFold - The Most Useful Thing AI Has Ever Done

Computer Science | D3S4 14/18 AI & Autonomous Systems – Part II - Similarities... - Naftali Tishby
▶︎

Computer Science | D3S4 14/18 AI & Autonomous Systems – Part II - Similarities... - Naftali Tishby

Запись трансляции "Information Theory of Deep Learning" (проф.Naftali Tishby)
▶︎

Запись трансляции "Information Theory of Deep Learning" (проф.Naftali Tishby)

Gradient descent, how neural networks learn | Deep Learning Chapter 2
▶︎

Gradient descent, how neural networks learn | Deep Learning Chapter 2

Introduction to Deep Learning and PyTorch
▶︎

Introduction to Deep Learning and PyTorch