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Jun, 2019
贝叶斯神经网络推断的不确定性量化质量
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
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Jiayu Yao, Weiwei Pan, Soumya Ghosh, Finale Doshi-Velez
TL;DR
本研究通过对 10 种常见的推断方法在回归和分类任务中的预测不确定性估计结果进行实证比较，发现常用的指标可能会导致误导，并表明为了得到高质量的后验逼近并不一定需要具有捕获后验结构的推断创新。
Abstract
bayesian neural networks
(BNNs) place priors over the parameters in a neural network. Inference in BNNs, however, is difficult; all
inference methods
for BNNs are approximate. In this work, we empirically compare
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