Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo
The task of recovering nonlinear dynamics and latent structure from a population recording is a challenging problem in statistical neuroscience motivating the development of novel techniques in time series analysis. Recent work has focused on connections between Variational Inference and Sequential Monte Carlo for performing inference and parameter estimation on sequential data. Inspired by this work, we present a framework to develop Smoothed Variational Objectives (SVOs) that condition proposal distributions on the full time-ordered sequence of observations. SVO maintains both expressiveness and tractability by sharing parameters of the transition function between the proposal and target. We apply the method to several dimensionality reduction/expansion tasks and examine the dynamics learned with a quantitative metric. SVO performs favorably against the state of the art.
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