While the choice of prior is one of the most critical parts of the Bayesian inference workflow, recent Bayesian deep learning models have often fallen back on vague priors, such as standard Gaussians. In this review, we highlight the importance of prior choices for Bayesian deep learning and present an overview of different priors that have been proposed for (deep) Gaussian processes, variational autoencoders and Bayesian neural networks. We also outline different methods of learning priors for these models from data. We hope to motivate practitioners in Bayesian deep learning to think more carefully about the prior specification for their models and to provide them with some inspiration in this regard.

中文翻译：

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贝叶斯深度学习的先验：回顾

虽然先验的选择是贝叶斯推理工作流程中最关键的部分之一，但最近的贝叶斯深度学习模型经常依赖于模糊的先验，例如标准高斯。在这篇综述中，我们强调了先验选择对贝叶斯深度学习的重要性，并概述了针对（深度）高斯过程、变分自编码器和贝叶斯神经网络提出的不同先验。我们还概述了从数据中为这些模型学习先验的不同方法。我们希望激励贝叶斯深度学习的从业者更仔细地思考他们模型的先验规范，并在这方面为他们提供一些启发。