This paper introduces a first implementation of a novel likelihood-ratio-based approach for constructing confidence intervals for neural networks. Our method, called DeepLR, offers several qualitative advantages: most notably, the ability to construct asymmetric intervals that expand in regions with a limited amount of data, and the inherent incorporation of factors such as the amount of training time, network architecture, and regularization techniques. While acknowledging that the current implementation of the method is prohibitively expensive for many deep-learning applications, the high cost may already be justified in specific fields like medical predictions or astrophysics, where a reliable uncertainty estimate for a single prediction is essential. This work highlights the significant potential of a likelihood-ratio-based uncertainty estimate and establishes a promising avenue for future research.

翻译：本文首次实现了一种基于似然比的新型方法，用于构建神经网络的置信区间。我们的方法名为DeepLR，具有若干定性优势：最显著的是能够构建非对称区间，该区间在数据量有限的区域会扩展，并内在地融入了训练时间、网络架构及正则化技术等因素。虽然承认该方法当前的实现对于许多深度学习应用而言成本过高，但在诸如医学预测或天体物理学等特定领域，这一高成本可能已是合理的，因为在这些领域中，对单个预测的可靠不确定性估计至关重要。本工作凸显了基于似然比的不确定性估计的巨大潜力，并为未来研究开辟了有前景的方向。