Neural Adaptive Sequential Monte Carlo

by   Shixiang Gu, et al.

Sequential Monte Carlo (SMC), or particle filtering, is a popular class of methods for sampling from an intractable target distribution using a sequence of simpler intermediate distributions. Like other importance sampling-based methods, performance is critically dependent on the proposal distribution: a bad proposal can lead to arbitrarily inaccurate estimates of the target distribution. This paper presents a new method for automatically adapting the proposal using an approximation of the Kullback-Leibler divergence between the true posterior and the proposal distribution. The method is very flexible, applicable to any parameterized proposal distribution and it supports online and batch variants. We use the new framework to adapt powerful proposal distributions with rich parameterizations based upon neural networks leading to Neural Adaptive Sequential Monte Carlo (NASMC). Experiments indicate that NASMC significantly improves inference in a non-linear state space model outperforming adaptive proposal methods including the Extended Kalman and Unscented Particle Filters. Experiments also indicate that improved inference translates into improved parameter learning when NASMC is used as a subroutine of Particle Marginal Metropolis Hastings. Finally we show that NASMC is able to train a latent variable recurrent neural network (LV-RNN) achieving results that compete with the state-of-the-art for polymorphic music modelling. NASMC can be seen as bridging the gap between adaptive SMC methods and the recent work in scalable, black-box variational inference.


page 1

page 2

page 3

page 4


High-dimensional Filtering using Nested Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods comprise one of the most successful...

Optimized Auxiliary Particle Filters

Auxiliary particle filters (APFs) are a class of sequential Monte Carlo ...

Variational Rejection Particle Filtering

We present a variational inference (VI) framework that unifies and lever...

Adaptive sequential Monte Carlo by means of mixture of experts

Appropriately designing the proposal kernel of particle filters is an is...

Inference Trees: Adaptive Inference with Exploration

We introduce inference trees (ITs), a new class of inference methods tha...

Neural Particle Smoothing for Sampling from Conditional Sequence Models

We introduce neural particle smoothing, a sequential Monte Carlo method ...

Generative Particle Variational Inference via Estimation of Functional Gradients

Recently, particle-based variational inference (ParVI) methods have gain...

Please sign up or login with your details

Forgot password? Click here to reset