Deep temporal sigmoid belief networks for sequence modeling

Published

Journal Article

Deep dynamic generative models are developed to learn sequential dependencies in time-series data. The multi-layered model is designed by constructing a hierarchy of temporal sigmoid belief networks (TSBNs), defined as a sequential stack of sigmoid belief networks (SBNs). Each SBN has a contextual hidden state, inherited from the previous SBNs in the sequence, and is used to regulate its hidden bias. Scalable learning and inference algorithms are derived by introducing a recognition model that yields fast sampling from the variational posterior. This recognition model is trained jointly with the generative model, by maximizing its variational lower bound on the log-likelihood. Experimental results on bouncing balls, polyphonic music, motion capture, and text streams show that the proposed approach achieves state-of-the-art predictive performance, and has the capacity to synthesize various sequences.

Duke Authors

Cited Authors

  • Gan, Z; Li, C; Henao, R; Carlson, D; Carin, L

Published Date

  • January 1, 2015

Published In

Volume / Issue

  • 2015-January /

Start / End Page

  • 2467 - 2475

International Standard Serial Number (ISSN)

  • 1049-5258

Citation Source

  • Scopus