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arXiv · 1905.12885

Particle Filter Recurrent Neural Networks

Xiao Ma·Peter Karkus·David Hsu·Wee Sun Lee
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Abstract

Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data. To tackle highly variable and noisy real-world data, we introduce Particle Filter Recurrent Neural Networks (PF-RNNs), a new RNN family that explicitly models uncertainty in its internal structure: while an RNN relies on a long, deterministic latent state vector, a PF-RNN maintains a latent state distribution, approximated as a set of particles. For effective learning, we provide a fully differentiable particle filter algorithm that updates the PF-RNN latent state distribution according to the Bayes rule. Experiments demonstrate that the proposed PF-RNNs outperform the corresponding standard gated RNNs on a synthetic robot localization dataset and 10 real-world sequence prediction datasets for text classification, stock price prediction, etc.

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Xiao Ma, Peter Karkus, David Hsu, Wee Sun Lee. 2019-05-30. Particle Filter Recurrent Neural Networks. https://arxiv.org/abs/1905.12885

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