Abstract
While perception tasks such as visual object recognition and text
understanding play an important role in human intelligence, the subsequent
tasks that involve inference, reasoning and planning require an even higher
level of intelligence. The past few years have seen major advances in many
perception tasks using deep learning models. For higher-level inference,
however, probabilistic graphical models with their Bayesian nature are still
more powerful and flexible. To achieve integrated intelligence that involves
both perception and inference, it is naturally desirable to tightly integrate
deep learning and Bayesian models within a principled probabilistic framework,
which we call Bayesian deep learning. In this unified framework, the perception
of text or images using deep learning can boost the performance of higher-level
inference and in return, the feedback from the inference process is able to
enhance the perception of text or images. This survey provides a general
introduction to Bayesian deep learning and reviews its recent applications on
recommender systems, topic models, and control. In this survey, we also discuss
the relationship and differences between Bayesian deep learning and other
related topics like Bayesian treatment of neural networks.
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