Monte Carlo Seminar| Manon Michel| Harnessing Newtonian dynamics in generative models
Автор: Monte Carlo Seminar
Загружено: 2026-01-27
Просмотров: 66
Описание:
Online Monte Carlo Seminar
sites.google.com/view/monte-carlo-seminar
Tuesday, January 27, 2026
Speaker: Manon Michel (CNRS, Université Clermont-Auvergne)
Title: Harnessing Newtonian dynamics in generative models
Abstract: Generative modeling seeks to learn and sample from complex, high-dimensional data distributions and plays a key role in machine learning, Bayesian inference, and computational physics. One powerful class of methods, called Normalizing Flows, works by gradually transforming simple random noise into complex data using reversible steps. While these models are reliable and mathematically well-understood, they can become slow and expensive to use when dealing with high dimensions. In this talk, I will explain how ideas from classical physics, in particular, the laws that govern motion, can be used to build more efficient and intuitive generative models. By designing these models to follow classical dynamics and using neural networks only where they are most helpful, we can further reduce computational costs while making the models more stable and easier to interpret.
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