# Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

@inproceedings{HernndezLobato2015ProbabilisticBF, title={Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks}, author={Jos{\'e} Miguel Hern{\'a}ndez-Lobato and Ryan P. Adams}, booktitle={ICML}, year={2015} }

Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems. [... ] Key Method Similar to classical backpropagation, PBP works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients. A series of experiments on ten real-world datasets show that PBP is significantly faster than other techniques, while offering competitive predictive abilities. Our experiments also show… Expand

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