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arXiv · 2109.08213

Reliable Neural Networks for Regression Uncertainty Estimation

Tony Tohme·Kevin Vanslette·Kamal Youcef-Toumi
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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.

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Tony Tohme, Kevin Vanslette, Kamal Youcef-Toumi. 2021-09-16. Reliable Neural Networks for Regression Uncertainty Estimation. https://doi.org/10.1016/j.ress.2022.108811

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