76 research outputs found
A General Method for Amortizing Variational Filtering
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and consists of an optimization procedure at each time step. By performing each inference optimization procedure with an iterative amortized inference model, we obtain a computationally efficient implementation of the algorithm, which we call amortized variational filtering. We present experiments demonstrating that this general-purpose method improves performance across several deep dynamical latent variable models
Importance sampling for online variational learning
This article addresses online variational estimation in state-space models.
We focus on learning the smoothing distribution, i.e. the joint distribution of
the latent states given the observations, using a variational approach together
with Monte Carlo importance sampling. We propose an efficient algorithm for
computing the gradient of the evidence lower bound (ELBO) in the context of
streaming data, where observations arrive sequentially. Our contributions
include a computationally efficient online ELBO estimator, demonstrated
performance in offline and true online settings, and adaptability for computing
general expectations under joint smoothing distributions
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