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New deep neural networks solving non-linear inverse problems
Presenter
- Matti Lassas
April 20, 2020
IPAM
Shape Analysis and Learning by Geometry and Machine (Overview - Part 2)
Presenter
- Ron Kimmel
February 8, 2016
IPAM
Shape Analysis and Learning by Geometry and Machine (Overview - Part 1)
Presenter
- Hong-Kai Zhao
February 8, 2016
IPAM
Covariant neural network architectures for learning physics
Presenter
- Risi Kondor
November 18, 2019
IPAM
Adam Wagner - Finding counterexamples to conjectures via reinforcement learning - IPAM at UCLA
Presenter
- Adam Wagner
February 14, 2023
IPAM
Can we design deep learning models that are inherently interpretable?
Presenter
- Cynthia Rudin
April 11, 2021
ICERM
Pascal Fontaine - SMT: quantifiers, and future prospects - IPAM at UCLA
Presenter
- Pascal Fontaine
February 16, 2023
IPAM
Geordie Williamson - What can the working mathematician expect from deep learning? - IPAM at UCLA
Presenter
- Geordie Williamson
February 13, 2023
IPAM
Convex Set Disjointness, Distributed Learning of Halfspaces, and Linear Programming
Presenter
- Shay Moran
May 12, 2020
IAS
Public Lecture: AI Breakthroughs & Obstacles to Progress, Mathematical and Otherwise
Presenter
- Yann LeCun
February 6, 2018
IPAM
Learning Intended Costs: Extracting all the right information from all the right places
Presenter
- Anca Dragan
February 25, 2020
IPAM