Search Results for author:
Found 4 papers, 2 papers with code
Normalizing flow sampling with Langevin dynamics in the latent space
Normalizing flows (NF) use a continuous generator to map a simple latent (e. g. Gaussian) distribution, towards an empirical target distribution associated with a training data set.
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference
This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution.
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training
Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e. g., images) and their failing to detect out-of-distribution data.
Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows
Each of these functions is associated to one sub-flow of the network, whose output provides intermediate steps of the transport between the original and target measures.
