Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge. Existing solutions rely on modified loss functions or architectural changes. We propose to compensate for the lack of built-in uncertainty estimates by supplementing any network, retrospectively, with a subsequent vine copula model, in an overall compound we call Vine-Copula Neural Network (VCNN). Through synthetic and real-data experiments, we show that VCNNs could be task (regression/classification) and architecture (recurrent, fully connected) agnostic while providing reliable and better-calibrated uncertainty estimates, comparable to state-of-the-art built-in uncertainty solutions.

翻译：尽管深度模型作为学习机器取得了重大进展，但不确定性估计仍是一项主要挑战。现有解决方案依赖于修改损失函数或改变网络架构。我们提出通过在任何网络之后，回顾性地补充一个后续的藤蔓连接函数模型，来弥补其缺乏内置不确定性估计的不足，形成的复合整体称为藤蔓连接函数神经网络。通过合成数据和真实数据实验，我们展示了藤蔓连接函数神经网络可以独立于任务（回归/分类）和架构（循环网络、全连接网络），同时提供可靠且校准更优的不确定性估计，与最先进的内置不确定性解决方案相当。