Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation

arXiv:2212.06370v4 Announce Type: replace
Abstract: Accurate uncertainty quantification is necessary to enhance the reliability of deep learning models in real-world applications. In the case of regression tasks, prediction intervals (PIs) should be provided along with the deterministic predictions of deep learning models. Such PIs are useful or "high-quality" as long as they are sufficiently narrow and capture most of the probability density. In this paper, we present a method to learn prediction intervals for regression-based neural networks automatically in addition …

abstract accuracy applications arxiv case cs.lg deep learning interval network neural network prediction predictions quality quantification regression reliability stat.ml tasks type uncertainty world