Collapsed amortized variational inference for switching nonlinear dynamical systemsDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • Abstract: We propose an efficient inference method for switching nonlinear dynamical systems. The key idea is to learn an inference network which can be used as a proposal distribution for the continuous latent variables, while performing exact marginalization of the discrete latent variables. This allows us to use the reparameterization trick, and apply end-to-end training with SGD. We show that this method can successfully segment time series data (including videos) into meaningful "regimes", due to the use of piece-wise nonlinear dynamics.
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