The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Towards Bayesian Deep Learning: A Framework and Some Existing Methods
Open accessAbstract
While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, 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 paper proposes a general framework for Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this paper, we also discuss the relationship and differences between Bayesian deep learning and other related topics such as the Bayesian treatment of neural networks.
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Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
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Self-citationDeep learning in cancer diagnosis, prognosis and treatment selection
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Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
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Metadata
- Authors
- Wang, H., & Yeung, D.-Y.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2016-12-01
- Volume
- 28
- Issue
- 12
- Pages
- 3395-3408
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2963058055
- Cited by
- 253
- References
- 74
- FWCI
- 16.04
- Type
- journal article
- Language
- en
- Open access
- green
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