Factored temporal sigmoid belief networks for sequence learning
Deep conditional generative models are developed to simultaneously learn the temporal dependencies of multiple sequences. The model is designed by introducing a three-way weight tensor to capture the multiplicative interactions between side information and sequences. The proposed model builds on the Temporal Sigmoid Belief Network (TSBN), a sequential stack of Sigmoid Belief Networks (SBNs). The transition matrices are further factored to reduce the number of parameters and improve generalization. When side information is not available, a general framework for semi-supervised learning based on the proposed model is constituted, allowing robust sequence classification. Experimental results show that the proposed approach achieves state-of-theart predictive and classification performance on sequential data, and has the capacity to synthesize sequences, with controlled style transitioning and blending.
Song, J; Gan, Z; Carin, L
33rd International Conference on Machine Learning, Icml 2016
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Start / End Page
International Standard Book Number 13 (ISBN-13)