Reliable predictive uncertainty estimation plays an important role in enabling the deployment of neural networks to safety-critical settings. A popular approach for estimating the predictive uncertainty of neural networks is to define a prior distribution over the network parameters, infer an approximate posterior distribution, and use it to make stochastic predictions. However, explicit inference over neural network parameters makes it difficult to incorporate meaningful prior information about the data-generating process into the model. In this paper, we pursue an alternative approach. Recognizing that the primary object of interest in most settings is the distribution over functions induced by the posterior distribution over neural network parameters, we frame Bayesian inference in neural networks explicitly as inferring a posterior distribution over functions and propose a scalable function-space variational inference method that allows incorporating prior information and results in reliable predictive uncertainty estimates. We show that the proposed method leads to state-of-the-art uncertainty estimation and predictive performance on a range of prediction tasks and demonstrate that it performs well on a challenging safety-critical medical diagnosis task in which reliable uncertainty estimation is essential.

翻译：可靠的预测不确定性估计在实现神经网络部署到安全关键场景中发挥着重要作用。一种常用的神经网络预测不确定性估计方法是定义网络参数上的先验分布，推断近似后验分布，并利用其进行随机预测。然而，对神经网络参数进行显式推断使得将关于数据生成过程的有意义先验信息纳入模型变得困难。本文探索了一种替代方法。我们认识到，大多数场景中关注的主要对象是由神经网络参数后验分布所诱导的函数分布，因此将神经网络中的贝叶斯推断明确表述为推断函数上的后验分布，并提出了一种可扩展的函数空间变分推断方法，该方法能够纳入先验信息并产生可靠的预测不确定性估计。我们证明，所提出的方法在一系列预测任务中实现了最先进的不确定性估计和预测性能，并展示了其在需要可靠不确定性估计的具有挑战性的安全关键医学诊断任务中表现出色。