Neural Adaptive Smoothing via TwistingDownload PDF

Published: 20 Jun 2023, Last Modified: 18 Jul 2023AABI 2023Readers: Everyone
Keywords: inference, sequential latent variables, sequential monte carlo, discrete latent variables, reweighted wake sleep, variational inference
TL;DR: We combine smoothing sequential Monte Carlo and reweighted wake sleep to derive a method for model learning and inference in sequential latent variable models.
Abstract: We propose NAS-X, a method for sequential latent variable model learning and inference that uses smoothing sequential Monte Carlo (SMC) in a reweighted wake sleep (RWS) framework. Our method works with both discrete and continuous latent variables, and successfully fits a wider range of models than filtering SMC-based methods. We evaluate NAS-T on several tasks and find that it substantially outperforms existing methods in both inference and parameter recovery.
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