Videos

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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
Random batch method for interacting particle systems and mean field limit Thumbnail

Random batch method for interacting particle systems and mean field limit

Presenter
  • Jian-Guo Lui
November 3, 2021
IMSI
Coordinates, governing equations and limits of model discovery Thumbnail

Coordinates, governing equations and limits of model discovery

Presenter
  • J. Nathan Kutz
October 16, 2019
IPAM

Mathematics is the champion of biomolecular data challenges

Presenter
  • Guowei Wei
September 16, 2016
MBI
Locating Outliers in Large Matrices with Adaptive Compressive Sampling Thumbnail

Locating Outliers in Large Matrices with Adaptive Compressive Sampling

Presenter
  • Jarvis Haupt
December 8, 2015
IMA

Brian Spears - Virtual Talk: The Genesis Mission : A nation-scale AI effort for Mission-Driven Science, National Security, and Applied Energy

May 20, 2026
IPAM
Structure-Based Comparisons for Sequential Data Thumbnail

Structure-Based Comparisons for Sequential Data

Presenter
  • Katherine Kinnaird
April 19, 2016
IMA

Quadratic Assignment on Different Data Models

Presenter
  • Soledad Villar
September 19, 2017
IMA
Max Kleiman-Weiner - Multi-Agent Common Sense Thumbnail

Max Kleiman-Weiner - Multi-Agent Common Sense

Presenter
  • Max Kleiman-Weiner
February 16, 2022
IPAM
Multi-scale modeling of materials revisited: Accelerated computing and machine learning Thumbnail

Multi-scale modeling of materials revisited: Accelerated computing and machine learning

Presenter
  • Kaushik Bhattacharya
February 15, 2021
IMSI

Transfer learning for surrogate models of PDEs

Presenter
  • Adrienne Propp
June 6, 2023
ICERM

Operator Learning For Solving PDE-related Problems.

Presenter
  • zecheng zhang
June 7, 2023
ICERM