Orateur
Description
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 invert the logic and train the VAE to fit a simple and tractable
distribution, on the assumption of a complex and intractable latent distribution,
specified up to normalization. This procedure constructs approximations without
the use of training data or Markov chain Monte Carlo sampling. We illustrate our
method on three examples: the Ising model, graph clustering, and ranking."
