Reliable neural networks for regression uncertainty estimation. Tohme, T., Vanslette, K., & Youcef-Toumi, K. Reliability Engineering & System Safety, 229:108811, January, 2023. ![Reliable neural networks for regression uncertainty estimation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{tohme_reliable_2023,
title = {Reliable neural networks for regression uncertainty estimation},
volume = {229},
issn = {0951-8320},
url = {https://www.sciencedirect.com/science/article/pii/S0951832022004306},
doi = {10.1016/j.ress.2022.108811},
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.},
language = {en},
urldate = {2022-10-29},
journal = {Reliability Engineering \& System Safety},
author = {Tohme, Tony and Vanslette, Kevin and Youcef-Toumi, Kamal},
month = jan,
year = {2023},
keywords = {Neural networks, Predictive uncertainty estimation, Regression, Reliability},
pages = {108811},
}
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