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.
|Original language||English (US)|
|Title of host publication||33rd International Conference on Machine Learning, ICML 2016|
|Publisher||International Machine Learning Society (IMLS)email@example.com|
|Number of pages||10|
|State||Published - Jan 1 2016|