Structured Inference Networks for Nonlinear State Space Models. Krishnan, R. G., Shalit, U., & Sontag, D. In *Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence*, pages 2101-2109, 2017.

Paper abstract bibtex 3 downloads

Paper abstract bibtex 3 downloads

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. 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.

@inproceedings{KrishnanEtAl_aaai17, author = {Rahul G. Krishnan and Uri Shalit and David Sontag}, title = {Structured Inference Networks for Nonlinear State Space Models}, booktitle = {Proceedings of the Thirty-First {AAAI} Conference on Artificial Intelligence}, pages = {2101-2109}, year = {2017}, keywords = {Machine learning, Unsupervised learning, Deep learning, Health care, Approximate inference in graphical models}, url_Paper = {https://arxiv.org/pdf/1609.09869.pdf}, 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 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. 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.} }

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