Probabilistic sampling for physics: finding needles in a field of high-dimensional haystacks

Session

Faraday discussion

14 sept. 2023, 14:00

Institut Pascal

29. Approximate sampling and estimation of partition functions using neural networks

Eiji Kawasaki (CEA)

14/09/2023 14:00

Discussion about the arXiv preprint 2209.10423

"We consider the closely related problems of sampling from a distribution known
up to a normalizing constant, and estimating said normalizing constant. We show
how variational autoencoders (VAEs) can be applied to this task. In their standard
applications, VAEs are trained to fit data drawn from an unknown and intractable
distribution. We...

41. Learning in Restricted Boltzmann Machines: The Critical Role of Sampling Processes

Aurélien DECELLE (Universidad Complutense de Madrid)

19/09/2023 10:00

Generative models aim to learn the empirical distribution of a given data set in order to build a probabilistic model capable of generating new samples that are statistically similar to the data set. One can also assume that one can obtain an approximately tractable analytical description of this distribution.

In this presentation, I will specifically consider the case of the so-called...