Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
Abstract
We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the exact conditional distributions learned by normalizing flows. As a conditional sampling method, PL-MCMC enables Monte Carlo Expectation Maximization (MC-EM) training of normalizing flows from incomplete data. Through experimental tests applying normalizing flows to missing data tasks for a variety of data sets, we demonstrate the efficacy of PL-MCMC for conditional sampling from normalizing flows.
Cite
Text
Cannella et al. "Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows." International Conference on Learning Representations, 2021.Markdown
[Cannella et al. "Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows." International Conference on Learning Representations, 2021.](https://mlanthology.org/iclr/2021/cannella2021iclr-projected/)BibTeX
@inproceedings{cannella2021iclr-projected,
title = {{Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows}},
author = {Cannella, Chris and Soltani, Mohammadreza and Tarokh, Vahid},
booktitle = {International Conference on Learning Representations},
year = {2021},
url = {https://mlanthology.org/iclr/2021/cannella2021iclr-projected/}
}