  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-the-art
predictive and classification performance on sequential data, and has the
capacity to synthesize sequences, with controlled style transitioning and
blending.
