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.

本文提出了一种基于深度条件生成模型（DCGM）的通用框架，能够同时学习多个序列之间的时序依赖关系，并通过引入三维权重张量来捕捉附加信息和序列之间的乘法交互作用。当没有侧信息时，该模型可用于半监督学习，实现鲁棒的序列分类。实验结果表明该方法在序列数据的预测和分类性能方面取得了最先进的成果，并且具有控制风格转换和融合的能力。