Videos

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Representation equivalent Neural Operators

Presenter
  • Rima Alaifari
June 7, 2023
ICERM
Patrick Riley - Symbolic Regression for Discovery of a DFT Functional - IPAM at UCLA Thumbnail

Patrick Riley - Symbolic Regression for Discovery of a DFT Functional - IPAM at UCLA

Presenter
  • Patrick Riley
January 23, 2023
IPAM
Lucas Wagner - Compact representations of excited states from QMC, as data for low-energy models Thumbnail

Lucas Wagner - Compact representations of excited states from QMC, as data for low-energy models

Presenter
  • Lucas Wagner
May 26, 2022
IPAM
Andrea Tirelli - Monte Carlo and Machine Learning Approaches in Quantum Mechanics - IPAM at UCLA Thumbnail

Andrea Tirelli - Monte Carlo and Machine Learning Approaches in Quantum Mechanics - IPAM at UCLA

Presenter
  • Andrea Tirelli
May 25, 2022
IPAM
A Machine Learning Application in Multi-Messenger Astrophysics Thumbnail

A Machine Learning Application in Multi-Messenger Astrophysics

Presenter
  • Irene Di Palma
October 7, 2021
IPAM
Chasing the Long Tail: What Neural Networks Memorize and Why Thumbnail

Chasing the Long Tail: What Neural Networks Memorize and Why

Presenter
  • Vitaly Feldman
April 14, 2021
IMSI
Benign overfitting Thumbnail

Benign overfitting

Presenter
  • Peter Bartlett
April 12, 2021
IMSI
Tradeoffs between Robustness and Accuracy Thumbnail

Tradeoffs between Robustness and Accuracy

Presenter
  • Percy Liang
April 16, 2020
IAS
Understanding machine learning via exactly solvable statistical physics models Thumbnail

Understanding machine learning via exactly solvable statistical physics models

Presenter
  • Lenka Zdeborova
November 18, 2019
IPAM
Quantum Machine Learning Thumbnail

Quantum Machine Learning

Presenter
  • Anatole von Lilienfeld
October 17, 2019
IPAM
Model Interpretability for Building Confidence and Sparking Insight in Scientific Applications Thumbnail

Model Interpretability for Building Confidence and Sparking Insight in Scientific Applications

Presenter
  • Julia Ling
October 14, 2019
IPAM
Background reduction in searches for gravitational-wave signals from supernovae: A machine learning approach Thumbnail

Background reduction in searches for gravitational-wave signals from supernovae: A machine learning approach

Presenter
  • Marco Cavaglia
February 1, 2019
IPAM