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. 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,
etc. Besides, we also discuss the relationship and differences between Bayesian
deep learning and other related topics such as Bayesian treatment of neural
networks. For a constantly updating project page, please refer to
this https URL

综合人工智能系统需要不仅能够用不同的 ` 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）关系及其对应的不确定性。本文综述了贝叶斯深度学习的基本原理及其在推荐系统、主题模型和控制等领域的应用，讨论了贝叶斯深度学习与神经网络等其他相关主题之间的关系和差异。