Structured Inference Networks for Nonlinear State Space Models

Understand

Gaussian state space models have been used for decades as generative models of sequential data.

• They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption.
• We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks.
• Our learning algorithm simultaneously learns a compiled inference network and the generative model, leveraging a structured variational approximation parameterized by recurrent neural networks to mimic the posterior distribution.