Poster
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Zhe Dong · Bryan Seybold · Kevin Murphy · Hung Bui
Keywords: [ Approximate Inference ] [ Deep Sequence Models ] [ Generative Models ] [ Time Series and Sequence Models ] [ Sequential, Network, and Time-Series Modeling ]
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 and 3D human pose, into meaningful regimes'' by using the piece-wise nonlinear dynamics.