Work

Stochastic Normalizing Flows

Year: 2020

Type: preprint

Abstract: We introduce stochastic normalizing flows, an extension of continuous normalizing flows for maximum likelihood estimation and variational inference (VI) using stochastic differential equations (SDEs). Using the theory of rough paths, the underlying Brownian motion is treated as a latent variable and approximated, enabling efficient training of neural SDEs as random neural ordinary differential equations. These SDEs can be used for constructing efficient Markov chains to sample from the underlying distribution of a given dataset. Furthermore, by considering families of targeted SDEs with prescribed stationary distribution, we can apply VI to the optimization of hyperparameters in stochastic MCMC.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2002.09547

Authors: Hodgkinson, Liam, van der Heide, Chris, Roosta, Fred, Mahoney, Michael W.

Institutions: University of California, Berkeley, International Computer Science Institute, The University of Queensland

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Defense Advanced Research Projects Agency, Office of Naval Research

OpenAlex ID: W3007848206

Cites: 39

Cited by: 63

Related to: 10