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Anna Korba: Wasserstein gradient flows and applications to sampling in machine learning - Lecture 1
Density estimation with normalizing flow in a minute
Optimal Transport and PDE: Gradient Flows in the Wasserstein Metric
Nik Nuesken: Stein geometry in machine learning: gradient flows, optimal transport, large deviations
What are Normalizing Flows
Sampling as optimization in the space of measures ...
Best Explanation of Partial Derivatives and Gradients
Anna Korba: Wasserstein gradient flows and applications to sampling in machine learning - Lecture 3
Wuchen Li: Accelerated Information Gradient Flow
Anna Korba: Wasserstein gradient flows and applications to sampling in machine learning - Lecture 2
FKTW03 | Jr. Prof. Dr. Anna Korba | Sampling with kernelized Wasserstein gradient flows
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Last Updated: September 6, 2026
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Summary
In recent years, particle-based variational CONFERENCE Recording during the thematic meeting : « Aggregation-Diffusion Equations & Collective Behavior » the April 11 ... Sahani Pathiraja (UNSW Sydney) Rocco Caprio (University of Warwick) Anna Korba (ENSAE/CREST) Paula Cordero Encinar ... Katy Craig (UC Santa Barbara) simons.berkeley.edu/talks/tbd-335 Geometric Methods in Optimization and Presentation given by Nik Nuesken on May 26th 2021 in the one world seminar on the mathematics of machine This short tutorial covers the basics of normalizing High Dimensional Hamilton-Jacobi PDEs 2020 Workshop II: PDE and Inverse Problem Methods in Machine FKTW03 | Jr. Prof. Dr. Anna Korba |
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