A General Method for Amortizing Variational Filtering

We introduce a general-purpose, theoretically-grounded, and simple method for performing filtering variational inference in dynamical latent variable models, which we refer to as the variational filtering EM algorithm. The algorithm is derived from the variational objective in the filtering setting and naturally consists of a Bayesian prediction-update loop, with updates performed using any desired optimization method. A computationally efficient implementation of the algorithm is provided, using iterative amortized inference models to perform inference optimization. Through empirical evaluations with several deep dynamical latent variable models on a variety of sequence data sets, we demonstrate that this simple filtering scheme compares favorably against previously proposed filtering methods in terms of inference performance, thereby improving model quality.