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.

翻译：虽然深神经网络在一系列现实世界问题中表现良好并十分成功,但估计其预测不确定性仍是一项艰巨的任务。为了应对这一挑战,我们提议并采用共同学习方法,根据贝叶西亚校验Metric(BVM)框架实施回归不确定性估算损失功能。拟议的损失在有限情况下重复了最大的可能性估算。一系列关于分配数据实验表明,拟议方法与现有最新方法相比具有竞争力。关于分配外数据的实验表明,拟议方法对统计变化具有很强的活力,并显示出较高的预测能力。