Videos

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Ryan Sweke - Should we use parameterized quantum circuits for machine learning? - IPAM at UCLA Thumbnail

Ryan Sweke - Should we use parameterized quantum circuits for machine learning? - IPAM at UCLA

Presenter
  • Ryan Sweke
October 17, 2023
IPAM
Marc Lackenby - Using machine learning to formulate mathematical conjectures - IPAM at UCLA Thumbnail

Marc Lackenby - Using machine learning to formulate mathematical conjectures - IPAM at UCLA

Presenter
  • Marc Lackenby
February 14, 2023
IPAM
Learning with entropy-regularized optimal transport Thumbnail

Learning with entropy-regularized optimal transport

Presenter
  • Aude Genevay
May 8, 2020
SLMath
Sarah Haigh - Probing atomic behaviour in liquids with STEM : opportunities for machine learning Thumbnail

Sarah Haigh - Probing atomic behaviour in liquids with STEM : opportunities for machine learning

Presenter
  • Sarah Haigh
November 30, 2022
IPAM
Raymond Clay - Machine Learning in Equation of State and Transport Modeling at Extreme Conditions Thumbnail

Raymond Clay - Machine Learning in Equation of State and Transport Modeling at Extreme Conditions

Presenter
  • Raymond Clay
May 24, 2022
IPAM
PDE Approaches for Deep Learning Thumbnail

PDE Approaches for Deep Learning

Presenter
  • Stanley Osher
May 1, 2019
IPAM
Applications of machine learning in low-latency electromagnetic counterpart inference from gravitational waves Thumbnail

Applications of machine learning in low-latency electromagnetic counterpart inference from gravitational waves

Presenter
  • Deep Chatterjee
November 29, 2021
IPAM
How to assess scientific machine learning models? Prediction errors and predictive uncertainty quantification Thumbnail

How to assess scientific machine learning models? Prediction errors and predictive uncertainty quantification

Presenter
  • Matthias Rupp
November 21, 2019
IPAM
Introduction to Machine Learning Thumbnail

Introduction to Machine Learning

Presenter
  • Felix Brockherde
September 13, 2013
IPAM
New Problems and Perspectives on Sampling, Memory, and Learning Thumbnail

New Problems and Perspectives on Sampling, Memory, and Learning

Presenter
  • Gregory Valiant
April 13, 2021
IMSI

New Progress on Stochastic Variance-Reduced Methods in Machine Learning: Adaptive Restart and Distributed Optimization

Presenter
  • Qihang Lin
December 12, 2017
IMA
Partial Difference Operators on Weighted Graphs for Point Cloud Processing - From 3D Surfaces to Machine Learning Thumbnail

Partial Difference Operators on Weighted Graphs for Point Cloud Processing - From 3D Surfaces to Machine Learning

Presenter
  • Daniel Tenbrinck
February 9, 2016
IPAM