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Can we design deep learning models that are inherently interpretable?
Presenter
- Cynthia Rudin
April 11, 2021
ICERM
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
Presenter
- Sanjeev Arora
February 25, 2021
IPAM
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
Presenter
- Adam Oberman
October 5, 2020
IPAM
The Peculiar Optimization and Regularization Challenges in Multi-Task Learning and Meta-Learning
Presenter
- Chelsea Finn
April 16, 2020
IAS
Statistical Mechanics of Deep Manifolds: Mean Field Geometry in High Dimension
Presenter
- Haim Sompolinsky
November 19, 2019
IPAM
Some Statistical Results on Deep Learning: Interpolation, Optimality and Sparsity
Presenter
- Guang Cheng
November 13, 2019
IAS
Improving PDE solvers and PDE-constrained optimization with deep learning and differentiable programming
Presenter
- Stephan Hoyer
October 18, 2019
IPAM
