Structured Inference Networks for Nonlinear State Space Models

Structured Inference Networks for Nonlinear State Space Models. Krishnan, R., G.; Shalit, U.; and Sontag, D.

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 tran-sition 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. We apply the learning algorithm to both synthetic and real-world datasets, demonstrating its scalability and versatility. We find that using the structured approximation to the posterior results in models with significantly higher held-out likelihood.

@article{
 title = {Structured Inference Networks for Nonlinear State Space Models},
 type = {article},
 id = {10b7ed7d-a5c8-35c5-9125-ea85d6c71a9f},
 created = {2018-02-14T12:53:37.892Z},
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 abstract = {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 tran-sition 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. We apply the learning algorithm to both synthetic and real-world datasets, demonstrating its scalability and versatility. We find that using the structured approximation to the posterior results in models with significantly higher held-out likelihood.},
 bibtype = {article},
 author = {Krishnan, Rahul G and Shalit, Uri and Sontag, David}
}

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