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, of AAAI'17, pages 2101–2109, 2017. AAAI Press. Place: San Francisco, California, USA Number of pages: 9
abstract   bibtex   
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{10.5555/3298483.3298543,
	series = {{AAAI}'17},
	title = {Structured inference networks for nonlinear state space models},
	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.},
	booktitle = {Proceedings of the thirty-first {AAAI} conference on artificial intelligence},
	publisher = {AAAI Press},
	author = {Krishnan, Rahul G. and Shalit, Uri and Sontag, David},
	year = {2017},
	note = {Place: San Francisco, California, USA
Number of pages: 9},
	keywords = {\#nosource, ⛔ No DOI found},
	pages = {2101--2109},
}
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