标题： 神经网络中的不确定性：贝叶斯集成 显示英文标题

标题： Uncertainty in Neural Networks: Bayesian Ensembling

摘要： 理解神经网络（NN）预测的不确定性对于许多应用至关重要。贝叶斯框架为此提供了一种合理的方法，然而由于参数和数据数量庞大，将其应用于NNs具有挑战性。集成NNs提供了一种易于实现且可扩展的不确定性量化方法，然而它因不具有贝叶斯特性而受到批评。在本工作中，我们对通常的集成过程进行了一项修改，该修改确实产生了贝叶斯行为：对参数进行正则化，使其围绕从先验分布中抽取的值进行。我们为此程序提供了理论支持，并在回归、图像分类和强化学习问题上进行了实证评估。 显示英文摘要

摘要： Understanding the uncertainty of a neural network's (NN) predictions is essential for many applications. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to the large number of parameters and data. Ensembling NNs provides an easily implementable, scalable method for uncertainty quantification, however, it has been criticised for not being Bayesian. In this work we propose one modification to the usual ensembling process that does result in Bayesian behaviour: regularising parameters about values drawn from a prior distribution. We provide theoretical support for this procedure as well as empirical evaluations on regression, image classification, and reinforcement learning problems.