论文标题
在功能相关性不确定性上：蒙特卡洛辍学抽样方法
On Feature Relevance Uncertainty: A Monte Carlo Dropout Sampling Approach
论文作者
论文摘要
了解神经网络做出的决定是在现实世界应用中部署智能系统的关键。但是，这些系统的不透明决策过程是可解释性必不可少的缺点。在过去的几年中，已经在机器学习领域引入了许多基于功能的解释技术，以更好地了解神经网络做出的决策，并已成为验证其推理能力的重要组成部分。但是，现有方法不允许对有关该功能与预测相关的不确定性进行陈述。在本文中，我们引入了蒙特卡洛相关性传播（MCRP），以进行功能相关性不确定性估计。一种基于蒙特卡洛估计特征相关性分布的简单但功能强大的方法，以计算特征相关性不确定性得分，从而可以更深入地了解神经网络的感知和推理。
Understanding decisions made by neural networks is key for the deployment of intelligent systems in real world applications. However, the opaque decision making process of these systems is a disadvantage where interpretability is essential. Many feature-based explanation techniques have been introduced over the last few years in the field of machine learning to better understand decisions made by neural networks and have become an important component to verify their reasoning capabilities. However, existing methods do not allow statements to be made about the uncertainty regarding a feature's relevance for the prediction. In this paper, we introduce Monte Carlo Relevance Propagation (MCRP) for feature relevance uncertainty estimation. A simple but powerful method based on Monte Carlo estimation of the feature relevance distribution to compute feature relevance uncertainty scores that allow a deeper understanding of a neural network's perception and reasoning.
