# Stabilising priors for robust Bayesian deep learning

@article{Mcgregor2019StabilisingPF, title={Stabilising priors for robust Bayesian deep learning}, author={Felix Mcgregor and Arnu Pretorius and Johan A. du Preez and Steve Kroon}, journal={ArXiv}, year={2019}, volume={abs/1910.10386} }

Bayesian neural networks (BNNs) have developed into useful tools for probabilistic modelling due to recent advances in variational inference enabling large scale BNNs. However, BNNs remain brittle and hard to train, especially: (1) when using deep architectures consisting of many hidden layers and (2) in situations with large weight variances. We use signal propagation theory to quantify these challenges and propose self-stabilising priors. This is achieved by a reformulation of the ELBO to…

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