Sylvester normalizing flows for variational inference

Rianne Van Den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck from planar flows, making a single transformation much more flexible. We compare the performance of Sylvester normalizing flows against planar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.

Original languageEnglish
Title of host publication34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
EditorsAmir Globerson, Amir Globerson, Ricardo Silva
PublisherAssociation For Uncertainty in Artificial Intelligence (AUAI)
Pages393-402
Number of pages10
ISBN (Electronic)9781510871601
Publication statusPublished - 1 Jan 2018
Externally publishedYes
Event34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 - Monterey, United States
Duration: 6 Aug 201810 Aug 2018

Publication series

Name34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
Volume1

Conference

Conference34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
CountryUnited States
CityMonterey
Period6/08/1810/08/18

Cite this

Van Den Berg, R., Hasenclever, L., Tomczak, J. M., & Welling, M. (2018). Sylvester normalizing flows for variational inference. In A. Globerson, A. Globerson, & R. Silva (Eds.), 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 (pp. 393-402). (34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018; Vol. 1). Association For Uncertainty in Artificial Intelligence (AUAI).