Videos

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The mean-squared-error is not enough: Improved prediction of chaotic dynamical systems with scientific machine learning Thumbnail

The mean-squared-error is not enough: Improved prediction of chaotic dynamical systems with scientific machine learning

Presenter
  • Romit Maulik
June 13, 2025
IMSI

Recent developments in nonlinear random matrices

Presenter
  • Yizhe Zhu
May 22, 2024
ICERM
Expressing The Density Matrix as Functional Of The Density: How To Profit From Machine Learning? Thumbnail

Expressing The Density Matrix as Functional Of The Density: How To Profit From Machine Learning?

Presenter
  • Lucia Reining
March 28, 2024
IMSI
Spectral Gap in Regular Graphs and Hypergraphs Thumbnail

Spectral Gap in Regular Graphs and Hypergraphs

Presenter
  • Ioana Dumitriu
September 24, 2021
SLMath
Active learning and experimental design - who should we test? Thumbnail

Active learning and experimental design - who should we test?

Presenter
  • Eldad Haber
April 21, 2020
IPAM
Towards Robust Artificial Intelligence Thumbnail

Towards Robust Artificial Intelligence

Presenter
  • Pushmeet Kohli
April 15, 2020
IAS
Solving high dimensional HJ equations using generalized Hopf-Lax formulas vs using Machine Learning Thumbnail

Solving high dimensional HJ equations using generalized Hopf-Lax formulas vs using Machine Learning

Presenters
  • Stanley Osher
  • Samy Wu Fung
April 1, 2020
IPAM
Trainability and accuracy of artificial neural networks Thumbnail

Trainability and accuracy of artificial neural networks

Presenter
  • Eric Vanden-Eijnden
November 18, 2019
IPAM
Validation and Reproducibility by Geometry, for Unsupervised Learning Thumbnail

Validation and Reproducibility by Geometry, for Unsupervised Learning

Presenter
  • Marina Meila
October 31, 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
Why Deep Learning Works: Heavy-Tailed Random Matrix Theory as an Example of Physics Informed Machine Learning Thumbnail

Why Deep Learning Works: Heavy-Tailed Random Matrix Theory as an Example of Physics Informed Machine Learning

Presenter
  • Michael Mahoney
October 14, 2019
IPAM
Deep Learning Opening Workshop: Solution Estimators in Stochastic Optimization, Amitabh Basu Thumbnail

Deep Learning Opening Workshop: Solution Estimators in Stochastic Optimization, Amitabh Basu

August 12, 2019
SAMSI