Все публикации

0:55:06

Daniel Hsu - Computational Lower Bounds for Tensor PCA

0:59:17

Lenka Zdeborova - Overparametrization: Insights from Solvable Models

0:43:47

Adit Radhakrishnan - Over-parameterized Autoencoders with Applications in Genomics

0:58:07

Francis Bach - The Quest for Adaptivity

1:02:15

Andrea Montanari - From Projection Pursuit to Interpolation Thresholds in Small Neural Networks

0:05:53

TOPML 2022: Opening Remarks - Yehuda Dar

1:00:59

Vidya Muthukumar - Overparameterized Classification vs Regression: Does the Loss Function Matter?

0:57:50

Edgar Dobriban - T-Cal: An Optimal Test for the Calibration of Predictive Models

1:10:52

Michael Mahoney - Practical Theory and Neural Network Models

1:02:24

TOPML Workshop 2021: Lightning Talk Session #1

0:51:41

TOPML Workshop 2021: Lightning Talk Session #4

0:53:57

TOPML Workshop 2021: Lightning Talk Session #3

1:03:41

TOPML Workshop 2021: Lightning Talk Session #2

0:24:54

Jeremy Bernstein - Computing the Typical Information Content of Infinitely Wide Neural Networks

0:25:07

Raaz Dwivedi - Revisiting Complexity and the Bias-Variance Tradeoff

0:26:43

Xiangyu Chang - Provable Benefits of Overparameterization in Model Compression

0:26:30

Nicole Muecke - The Influence of Overparameterization and Regularization on Distributed Learning

0:56:13

Tomaso Poggio - Deep Puzzles

0:56:24

Gitta Kutyniok - Graph Convolutional Neural Networks: The Mystery of Generalization

0:09:27

Opening Remarks - Richard Baraniuk and Yehuda Dar

0:59:03

Matthieu Wyart- A Phase Diagram for Deep Learning Unifying Jamming, Feature Learning & Lazy Training

0:59:34

Robert Nowak - Banach Space Representer Theorems for Neural Networks

1:01:39

Florent Krzakala - Generalization in Machine Learning: Insights from Simple Models

1:01:04

Peter Bartlett - Benign Overfitting