# Fast Predictive Uncertainty for Classification with Bayesian Deep Networks

@article{Hobbhahn2020FastPU, title={Fast Predictive Uncertainty for Classification with Bayesian Deep Networks}, author={Marius Hobbhahn and Agustinus Kristiadi and Philipp Hennig}, journal={ArXiv}, year={2020}, volume={abs/2003.01227} }

In Bayesian Deep Learning, distributions over the output of classification neural networks are approximated by first constructing a Gaussian distribution over the weights, then sampling from it to receive a distribution over the categorical output distribution. This is costly. We reconsider old work to construct a Dirichlet approximation of this output distribution, which yields an analytic map between Gaussian distributions in logit space and Dirichlet distributions (the conjugate prior to the… CONTINUE READING

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