Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning-Reference-Cited by-同舟云学术

Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning

Published:2023-06-22 Issue:7 Volume:5 Page:714-723
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Lin Yudeng,Zhang Qingtian,Gao Bin^ORCID,Tang Jianshi^ORCID,Yao Peng,Li Chongxuan,Huang Shiyu^ORCID,Liu Zhengwu^ORCID,Zhou Ying,Liu Yuyi,Zhang Wenqiang,Zhu Jun,Qian He,Wu Huaqiang^ORCID

Funder

Ministry of Science and Technology of the People’s Republic of China

National Natural Science Foundation of China

XPLORER Prize,and Beijing Advanced Innovation Center for Integrated Circuits

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

https://www.nature.com/articles/s42256-023-00680-y.pdf

Reference41 articles.

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3. Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 521, 452–459 (2015).

4. Abdar, M. et al. A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf. Fusion 76, 243–297 (2021).

5. Wang, H. & Yeung, D.-Y. Towards Bayesian deep learning: a framework and some existing methods. IEEE Trans. Knowl. Data Eng. 28, 3395–3408 (2016).

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