Hidden Markov Neural Networks

Lorenzo Rimella; Nick Whiteley

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arXiv · 2004.06963

Hidden Markov Neural Networks

Lorenzo Rimella·Nick Whiteley

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Abstract

We define an evolving in-time Bayesian neural network called a Hidden Markov Neural Network, which addresses the crucial challenge in time-series forecasting and continual learning: striking a balance between adapting to new data and appropriately forgetting outdated information. This is achieved by modelling the weights of a neural network as the hidden states of a Hidden Markov model, with the observed process defined by the available data. A filtering algorithm is employed to learn a variational approximation of the evolving-in-time posterior distribution over the weights. By leveraging a sequential variant of Bayes by Backprop, enriched with a stronger regularization technique called variational DropConnect, Hidden Markov Neural Networks achieve robust regularization and scalable inference. Experiments on MNIST, dynamic classification tasks, and next-frame forecasting in videos demonstrate that Hidden Markov Neural Networks provide strong predictive performance while enabling effective uncertainty quantification.

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Lorenzo Rimella, Nick Whiteley. 2020-04-15. Hidden Markov Neural Networks. https://arxiv.org/abs/2004.06963

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