论文标题
预测间隔：将正常混合物从质量驱动的深层合奏中拆分
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles
论文作者
论文摘要
预测间隔是一种在回归分析中代表预测不确定性的机器和人解释的方法。在本文中，我们提出了一种生成预测间隔的方法以及来自神经网络合奏的点估计。我们提出了一个多目标损耗函数，融合了与预测间隔和点估计以及惩罚函数相关的质量度量，该函数可以实现结果的语义完整性并稳定神经网络的训练过程。将结合的预测间隔汇总为一种分裂的正常混合物，该混合物考虑了后验预测分布可能的多模态和不对称性，并导致预测间隔，以捕获核心和认知不确定性。我们的结果表明，我们的质量驱动损失函数和聚合方法都有助于良好的预测间隔和点估计。
Prediction intervals are a machine- and human-interpretable way to represent predictive uncertainty in a regression analysis. In this paper, we present a method for generating prediction intervals along with point estimates from an ensemble of neural networks. We propose a multi-objective loss function fusing quality measures related to prediction intervals and point estimates, and a penalty function, which enforces semantic integrity of the results and stabilizes the training process of the neural networks. The ensembled prediction intervals are aggregated as a split normal mixture accounting for possible multimodality and asymmetricity of the posterior predictive distribution, and resulting in prediction intervals that capture aleatoric and epistemic uncertainty. Our results show that both our quality-driven loss function and our aggregation method contribute to well-calibrated prediction intervals and point estimates.
