Safety-critical decisions based on machine learning models require a clear understanding of the involved uncertainties to avoid hazardous or risky situations. While aleatoric uncertainty can be explicitly modeled given a parametric description, epistemic uncertainty rather describes the presence or absence of training data. This paper proposes a novel generic method for modeling epistemic uncertainty and shows its advantages over existing approaches for neural networks on various data sets. It can be directly combined with aleatoric uncertainty estimates and allows for prediction in real-time as the inference is sample-free. We exploit this property in a model-based quadcopter control setting and demonstrate how the controller benefits from a differentiation between aleatoric and epistemic uncertainty in online learning of thermal disturbances.
翻译:以机器学习模型为基础的安全关键决定要求明确理解所涉的不确定性以避免危险或危险情况。虽然根据参数描述可以明确模拟悬浮性不确定性,但缩写性不确定性却描述了培训数据的存在或缺乏。本文建议采用一种新的通用方法来模拟隐喻性不确定性,并表明它比各种数据集的神经网络的现有方法具有优势。它可以直接与悬浮性不确定性估计数相结合,并允许实时预测,因为推断是没有样本的。我们利用基于模型的四肢控制设置来利用这一属性,并展示控制器如何从网上学习热扰动时区分测距和感知不确定性中受益。