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Deep Poisson gamma dynamical systems
Dandan Guo · Bo Chen · Hao Zhang · Mingyuan Zhou

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #60

We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially observed multivariate count data, improving previously proposed models by not only mining deep hierarchical latent structure from the data, but also capturing both first-order and long-range temporal dependencies. Using sophisticated but simple-to-implement data augmentation techniques, we derived closed-form Gibbs sampling update equations by first backward and upward propagating auxiliary latent counts, and then forward and downward sampling latent variables. Moreover, we develop stochastic gradient MCMC inference that is scalable to very long multivariate count time series. Experiments on both synthetic and a variety of real-world data demonstrate that the proposed model not only has excellent predictive performance, but also provides highly interpretable multilayer latent structure to represent hierarchical and temporal information propagation.

Author Information

Dandan Guo (Xidian University)
Bo Chen (Xidian University)
Hao Zhang (Xidian University)
Mingyuan Zhou (University of Texas at Austin)

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