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Steve Brunton - Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics
Presenter
- Steve Brunton
January 24, 2023
IPAM
Rafael Gomez Bombarelli - End-to-end learning and auto-differentiation: forces, uncertainties, etc.
Presenter
- Rafael Gomez-Bombarelli
January 24, 2023
IPAM
Mark Tuckerman - From A to B via a synthesis of rare-event sampling and machine learning
Presenter
- Mark Tuckerman
January 24, 2023
IPAM
Cecelia Clementi - Coarse-graining classical and quantum systems - IPAM at UCLA
Presenter
- Cecelia Clementi
January 23, 2023
IPAM
Oliver Eberle - Explainable structured machine learning in similarity, graph and transformer models
Presenter
- Oliver Eberle
January 12, 2023
IPAM
Xiao Fu - Multiview and Self-Supervised Representation Learning: Nonlinear Mixture Identification
Presenter
- Xiao Fu
January 9, 2023
IPAM
Kevin Kelly - Machine Learning Enhanced Compressive Hyperspectral Imaging - IPAM at UCLA
Presenter
- Kevin Kelly
December 2, 2022
IPAM
Mary Scott - Supervised and Unsupervised approaches for Electron Microscopy Data Analysis
Presenter
- Mary Scott
December 1, 2022
IPAM
Reinhard Heckel - The role of data and models for deep-learning based image reconstruction
Presenter
- Reinhard Heckel
December 1, 2022
IPAM
Ben Recht - Splitting the difference between deep and shallow solutions of inverse problems
Presenter
- Ben Recht
November 30, 2022
IPAM
Rama Vasudevan - Advancing Microscopy with Machine Learning: Lessons from Scanning Probe Microscopy
Presenter
- Rama Vasudevan
November 29, 2022
IPAM