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arXiv · 1509.07087

Deep Temporal Sigmoid Belief Networks for Sequence Modeling

Zhe Gan·Chunyuan Li·Ricardo Henao·David Carlson·Lawrence Carin
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Abstract

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.

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Zhe Gan, Chunyuan Li, Ricardo Henao, David Carlson, Lawrence Carin. 2015-09-23. Deep Temporal Sigmoid Belief Networks for Sequence Modeling. https://arxiv.org/abs/1509.07087

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