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Gaussian state space models have been used for decades as generative modelsof sequential data. They admit an intuitive probabilistic interpretation, havea simple functional form, and enjoy widespread adoption. We introduce a unifiedalgorithm to efficiently learn a broad class of linear and non-linear statespace models, including variants where the emission and transitiondistributions are modeled by deep neural networks. Our learning algorithmsimultaneously learns a compiled inference network and the generative model,leveraging a structured variational approximation parameterized by recurrentneural networks to mimic the posterior distribution. We apply the learningalgorithm to both synthetic and real-world datasets, demonstrating itsscalability and versatility. We find that using the structured approximation tothe posterior results in models with significantly higher held-out likelihood.

Structured Inference Networks for Nonlinear State Space Models