Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems

ICML 2020

Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems

Jul 12, 2020
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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 stochastic gradient descent. We show that the proposed method can successfully segment time series data (including videos) into meaningful "regimes", by using the piece-wise nonlinear dynamics. Speakers: Zhe Dong, Bryan A. Seybol, Kevin P. Murphy, Hun H. Bui

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