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Reliable neural networks for regression uncertainty estimation

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  • Tohme, Tony
  • Vanslette, Kevin
  • Youcef-Toumi, Kamal

Abstract

While deep neural networks are highly performant and successful in a wide range of real-world problems, estimating their predictive uncertainty remains a challenging task. To address this challenge, we propose and implement a loss function for regression uncertainty estimation based on the Bayesian Validation Metric (BVM) framework while using ensemble learning. The proposed loss reproduces maximum likelihood estimation in the limiting case. A series of experiments on in-distribution data show that the proposed method is competitive with existing state-of-the-art methods. Experiments on out-of-distribution data show that the proposed method is robust to statistical change and exhibits superior predictive capability.

Suggested Citation

  • Tohme, Tony & Vanslette, Kevin & Youcef-Toumi, Kamal, 2023. "Reliable neural networks for regression uncertainty estimation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
  • Handle: RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004306
    DOI: 10.1016/j.ress.2022.108811
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    References listed on IDEAS

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    Cited by:

    1. Ebrahimi, Mehrdad & Nobahar, Elnaz & Mohammadi, Reza Karami & Noroozinejad Farsangi, Ehsan & Noori, Mohammad & Li, Shaofan, 2023. "The influence of model and measurement uncertainties on damage detection of experimental structures through recursive algorithms," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    2. Zhao, Yunjie & Cheng, Xi & Zhang, Taihong & Wang, Lei & Shao, Wei & Wiart, Joe, 2023. "A global–local attention network for uncertainty analysis of ground penetrating radar modeling," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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