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Fast Predictive Uncertainty for Classification with Bayesian Deep Networks
[article]
2022
arXiv
pre-print
In Bayesian Deep Learning, distributions over the output of classification neural networks are often approximated by first constructing a Gaussian distribution over the weights, then sampling from it to receive a distribution over the softmax outputs. This is costly. We reconsider old work (Laplace Bridge) to construct a Dirichlet approximation of this softmax output distribution, which yields an analytic map between Gaussian distributions in logit space and Dirichlet distributions (the
arXiv:2003.01227v4
fatcat:xuzva4pzjrcgfj5zsmyorfx4hy