Deep Kalman Filters

Rahul G. Krishnan, Uri Shalit, David Sontag
11/16/2015
stat.MLcs.LG

Official code: clinicalml/structuredinference (277 stars, MIT, last commit 2017-08-29).

Abstract

Kalman Filters are one of the most influential models of time-varying phenomena. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption in a variety of disciplines. Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters. Of particular interest is the use of temporal generative models for counterfactual inference. We investigate the efficacy of such models for counterfactual inference, and to that end we introduce the "Healing MNIST" dataset where long-term structure, noise and actions are applied to sequences of digits. We show the efficacy of our method for modeling this dataset. We further show how our model can be used for counterfactual inference for patients, based on electronic health record data of 8,000 patients over 4.5 years.

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Code Implementations(1)

clinicalml/structuredinferenceOfficial100%
MIT

Structured Inference Networks for Nonlinear State Space Models

27765Shell, PythonMay 24, 20169 years agoMIT

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Cite this paper

@article{krishnan2015deep,
  title  = {Deep Kalman Filters},
  author = {Rahul G. Krishnan and Uri Shalit and David Sontag},
  year   = {2015},
  eprint = {1511.05121},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1511.05121}
}

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