Videos

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Learning models: connections between boosting, hard-core distributions, dense models, GAN, and regularity I Thumbnail

Learning models: connections between boosting, hard-core distributions, dense models, GAN, and regularity I

Presenter
  • Russell Impagliazzo
November 13, 2017
IAS

Neural net word embeddings: What was old is new again

Presenter
  • Jeremy Bellay
October 6, 2017
IMA
Navigating colloidal design space with machine learning Thumbnail

Navigating colloidal design space with machine learning

Presenter
  • Matthew Spellings
December 7, 2016
IPAM
Learning dynamics with dynamical distances: From diffusion maps to commute maps and coherence Thumbnail

Learning dynamics with dynamical distances: From diffusion maps to commute maps and coherence

Presenter
  • Ralf Banisch
December 5, 2016
IPAM
A Story of Principal Component Analysis in the Distributed Model Thumbnail

A Story of Principal Component Analysis in the Distributed Model

Presenter
  • David Woodruff
May 16, 2016
IMA
Haomin Zhou - A supervised learning scheme for Hamilton-Jacobi equation via density coupling Thumbnail

Haomin Zhou - A supervised learning scheme for Hamilton-Jacobi equation via density coupling

Presenter
  • Haomin Zhou
July 15, 2025
IPAM

Analysis of singular subspaces under random perturbations

Presenter
  • Ke Wang
May 20, 2024
ICERM
Learning a Neural Free-Energy Functional from Pair-Correlation Functions Thumbnail

Learning a Neural Free-Energy Functional from Pair-Correlation Functions

Presenter
  • Bernd Ensing
April 25, 2024
IMSI
Bridging Scales in Materials Modeling With Occam-Shaved Machine Learning Thumbnail

Bridging Scales in Materials Modeling With Occam-Shaved Machine Learning

Presenter
  • Luca Ghiringhelli
March 29, 2024
IMSI
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
Vinod Nair - Restricted Boltzmann Machines for Maximum Satisfiability - IPAM at UCLA Thumbnail

Vinod Nair - Restricted Boltzmann Machines for Maximum Satisfiability - IPAM at UCLA

Presenter
  • Vinod Nair
February 27, 2023
IPAM
Michele Ceriotti - Physical insights from atomic-scale machine learning - IPAM at UCLA Thumbnail

Michele Ceriotti - Physical insights from atomic-scale machine learning - IPAM at UCLA

Presenter
  • Michele Ceriotti
January 11, 2023
IPAM