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. A series of experiments on in-distribution data show that the proposed method is competitive with existing state-of-the-art methods. In addition, experiments on out-of-distribution data show that the proposed method is robust to statistical change and exhibits superior predictive capability.

翻译：虽然深层神经网络在一系列现实世界问题中表现良好而且非常成功,但估计其预测性不确定性仍是一项艰巨的任务。为了应对这一挑战,我们提议并实施一项基于贝叶西亚校验Metric(BVM)框架的回归性不确定性估算损失功能,同时采用共同学习方法。一系列关于分配中数据的实验表明,拟议的方法与现有最新方法相比具有竞争力。此外,关于分配外数据的实验表明,拟议的方法对统计变化具有很强的说服力,并显示出超强的预测能力。