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Cited by 247Gaussian Processes and Bayesian InferenceBayesian Modeling and Causal InferenceTopic Modeling

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A Survey on Bayesian Deep Learning

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Wang, Hao, Yeung, Dit-Yan·2021·ACM Computing Surveys
DOI 10.1145/3409383
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Abstract

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.

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Metadata

Authors
Wang, H., & Yeung, D.-Y.
Affiliations
  • Massachusetts Institute of Technology (US)
  • Hong Kong University of Science and Technology (HK)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2020-09-28
Volume
53
Issue
5
Pages
1-37
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3409383
OpenAlex
W3089611792
Cited by
247
References
52
Citations by year
2026: 23 · 2025: 54 · 2024: 50 · 2023: 37 · 2022: 43 · 2021: 28 · 2020: 6
FWCI
11.20
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Computing Surveys (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
Bayesian deep learning, uncertainty quantification, probabilistic graphical models, Bayesian neural networks, recommender systems, topic modeling, Bayesian control

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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