A comprehensive artificial intelligence system needs to not only perceive the environment with different “senses” (e.g., seeing and hearing) but also infer the world’s conditional (or even causal) relations and corresponding uncertainty. The past decade has seen major advances in many perception tasks, such as visual object recognition and speech recognition, using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. In recent years, Bayesian deep learning has emerged as a unified probabilistic framework to tightly integrate deep learning and Bayesian models. 1 In this general framework, the perception of text or images using deep learning can boost the performance of higher-level inference and, in turn, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a comprehensive introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, control, and so on. We also discuss the relationship and differences between Bayesian deep learning and other related topics, such as Bayesian treatment of neural networks.

中文翻译：

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贝叶斯深度学习综述

一个综合的人工智能系统不仅需要以不同的“感官”（例如，视觉和听觉）感知环境，还需要推断世界的条件（甚至因果）关系和相应的不确定性。在过去的十年中，使用深度学习模型在许多感知任务中取得了重大进展，例如视觉对象识别和语音识别。然而，对于更高层次的推理，具有贝叶斯性质的概率图模型仍然更加强大和灵活。最近几年，贝叶斯深度学习已经成为一个统一的概率框架，以紧密集成深度学习和贝叶斯模型。1在这个通用框架中，使用深度学习对文本或图像的感知可以提高更高级别推理的性能，反过来，推理过程的反馈能够增强对文本或图像的感知。本次调查全面介绍了贝叶斯深度学习并回顾了它最近在推荐系统、主题模型、控制等方面的应用。我们还讨论了贝叶斯深度学习与其他相关主题之间的关系和差异，例如神经网络的贝叶斯处理。