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Uncertainty propagation for dropout-based Bayesian neural networks.

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第一作者： Yuki,Mae

第一单位： DENSO CORPORATION, 1-1, Showa-cho, Kariya, Aichi, 448-8661, Japan. Electronic address: yuki.mae.j5w@jp.denso.com.

作者： Yuki,Mae ^[1] ; Wataru,Kumagai ^[2] ; Takafumi,Kanamori ^[3]

作者单位： DENSO CORPORATION, 1-1, Showa-cho, Kariya, Aichi, 448-8661, Japan. Electronic address: yuki.mae.j5w@jp.denso.com. ^[1] Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan; Center for Advanced Intelligence Project, RIKEN, Nihonbashi 1-chome Mitsui Building, 15th floor,1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan. Electronic address: kumagai@weblab.t.u-tokyo.ac.jp. ^[2] Department of Mathematical and Computing Science, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8552, Japan; Center for Advanced Intelligence Project, RIKEN, Nihonbashi 1-chome Mitsui Building, 15th floor,1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan. Electronic address: kanamori@c.titech.ac.jp. ^[3]

关键词 LSTM Out-of-distribution Sampling-free method Uncertainty evaluation Variance propagation

医学主题词贝叶斯定理(Bayes Theorem);蒙特卡罗法(Monte Carlo Method);结果可重复性(Reproducibility of Results);偏差(Uncertainty)

DOI 10.1016/j.neunet.2021.09.005

PMID 34562813

发布时间 2021-11-24

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