M. Gabrié

Leveraging generative models to assist Monte Carlo sampling

A new paradigm that has recently emerged at the interface of machine learning and computational statistical physics: the use of generative models as tools for sampling through flexible probabilistic models that can assist the sampling of distributions known only up to a normalization constant is explored.