Dynamic Bayesian Neural Networks

• URL: http://arxiv.org/abs/2004.06963v2
• Date: Wed, 24 Jun 2020 14:29:17 GMT
• Title: Dynamic Bayesian Neural Networks
• Authors: Lorenzo Rimella and Nick Whiteley
• Abstract summary: We define an evolving in time neural network called a Hidden Markov neural network. Weights of a feed-forward neural network are modelled with the hidden states of a Hidden Markov model. A filtering algorithm is used to learn a variational approximation to the evolving in time posterior over the weights.
• Score: 2.28438857884398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We define an evolving in time Bayesian neural network called a Hidden Markov neural network. The weights of a feed-forward neural network are modelled with the hidden states of a Hidden Markov model, whose observed process is given by the available data. A filtering algorithm is used to learn a variational approximation to the evolving in time posterior over the weights. Training is pursued through a sequential version of Bayes by Backprop Blundell et al. 2015, which is enriched with a stronger regularization technique called variational DropConnect. The experiments test variational DropConnect on MNIST and display the performance of Hidden Markov neural networks on time series.

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