Continuous-time flows for efficient inference and density estimation

Published

Conference Paper

© 2018 by the Authors. All rights reserved. Two fundamental problems in unsupervised learning are efficient inference for latent-variable models and robust density estimation based on large amounts of unlabeled data. Algorithms for the two tasks, such as normalizing flows and generative adversarial networks (GANs), are often developed independently. In this paper, we propose the concept of continuous-time flows (CTFs), a family of diffusion-based methods that are able to asymptotically approach a target distribution. Distinct from normalizing flows and GANs, CTFs can be adopted to achieve the above two goals in one framework, with theoretical guarantees. Our framework includes distilling knowledge from a CTF for efficient inference, and learning an explicit energy-based distribution with CTFs for density estimation. Both tasks rely on a new technique for distribution matching within amortized learning. Experiments on various tasks demonstrate promising performance of the proposed CTF framework, compared to related techniques.

Duke Authors

Cited Authors

  • Chen, C; Li, C; Chen, L; Wang, W; Pu, Y; Carin, L

Published Date

  • January 1, 2018

Published In

  • 35th International Conference on Machine Learning, Icml 2018

Volume / Issue

  • 2 /

Start / End Page

  • 1285 - 1304

International Standard Book Number 13 (ISBN-13)

  • 9781510867963

Citation Source

  • Scopus