Practical Deep Learning with Bayesian Principles

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz E. Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota


Bayesian methods promise to fix many shortcomings of deep learning, but they are impractical and rarely match the performance of standard methods, let alone improve them. In this paper, we demonstrate practical training of deep networks with natural-gradient variational inference. By applying techniques such as batch normalisation, data augmentation, and distributed training, we achieve similar performance in about the same number of epochs as the Adam optimiser, even on large datasets such as ImageNet. Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated, uncertainties on out-of-distribution data are improved, and continual-learning performance is boosted. This work enables practical deep learning while preserving benefits of Bayesian principles. A PyTorch implementation is available as a plug-and-play optimiser.