Inference Networks for Sequential Monte Carlo in Graphical Models
This addresses the challenge of efficient inference in complex probabilistic models, offering a domain-specific improvement for researchers and practitioners in machine learning and statistics.
The paper tackles the problem of amortizing inference in directed graphical models by learning heuristic approximations to stochastic inverses for use as proposal distributions in sequential Monte Carlo methods, resulting in automatically-learned high-quality proposals that accelerate inference across diverse settings.
We introduce a new approach for amortizing inference in directed graphical models by learning heuristic approximations to stochastic inverses, designed specifically for use as proposal distributions in sequential Monte Carlo methods. We describe a procedure for constructing and learning a structured neural network which represents an inverse factorization of the graphical model, resulting in a conditional density estimator that takes as input particular values of the observed random variables, and returns an approximation to the distribution of the latent variables. This recognition model can be learned offline, independent from any particular dataset, prior to performing inference. The output of these networks can be used as automatically-learned high-quality proposal distributions to accelerate sequential Monte Carlo across a diverse range of problem settings.