Videos

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Mathematics Of Machine Learning: Deep Learning Theory Pt 2 Thumbnail

Mathematics Of Machine Learning: Deep Learning Theory Pt 2

Presenter
  • Joan Bruna
July 29, 2022
SLMath
Mathematics Of Machine Learning: Deep Learning Theory Thumbnail

Mathematics Of Machine Learning: Deep Learning Theory

Presenter
  • Joan Bruna
July 29, 2022
SLMath
DISTRIBUTIONALLY ROBUST LEARNING OVER DEEP NEURAL NETWORKS AND THEIR ASSOCIATED REGULARIZED RISK Thumbnail

DISTRIBUTIONALLY ROBUST LEARNING OVER DEEP NEURAL NETWORKS AND THEIR ASSOCIATED REGULARIZED RISK

Presenter
  • Camilo Garcia Trillos
May 24, 2022
IMSI
Structured Adaptation & Deep Learning: When Prediction Yields Adaptation Thumbnail

Structured Adaptation & Deep Learning: When Prediction Yields Adaptation

March 10, 2022
SLMath
Efficient Tensor Representation for Deep Learning with TensorLy and PyTorch Thumbnail

Efficient Tensor Representation for Deep Learning with TensorLy and PyTorch

Presenter
  • Jean Kossaifi
May 19, 2021
IPAM

Can we design deep learning models that are inherently interpretable?

Presenter
  • Cynthia Rudin
April 11, 2021
ICERM

Breaking into a Deep Learning box

Presenter
  • Ivan Tyukin
April 10, 2021
ICERM
Implicit Regularization in Deep Learning: Lessons Learned from Matrix and Tensor Factorization Thumbnail

Implicit Regularization in Deep Learning: Lessons Learned from Matrix and Tensor Factorization

Presenter
  • Nadav Cohen
March 31, 2021
IPAM
How to allow deep learning on your data without revealing your data Thumbnail

How to allow deep learning on your data without revealing your data

Presenter
  • Sanjeev Arora
February 25, 2021
IPAM
Parsimonious structure-exploiting deep neural network surrogates for Bayesian inverse problems and optimal experimental design Thumbnail

Parsimonious structure-exploiting deep neural network surrogates for Bayesian inverse problems and optimal experimental design

Presenter
  • Omar Ghattas
October 7, 2020
IMSI
Contributions to deep learning using a mathematical approach: improved model uncertainty, certified robust models, and faster training of Neural ODEs Thumbnail

Contributions to deep learning using a mathematical approach: improved model uncertainty, certified robust models, and faster training of Neural ODEs

Presenter
  • Adam Oberman
October 5, 2020
IPAM
The Peculiar Optimization and Regularization Challenges in Multi-Task Learning and Meta-Learning Thumbnail

The Peculiar Optimization and Regularization Challenges in Multi-Task Learning and Meta-Learning

Presenter
  • Chelsea Finn
April 16, 2020
IAS