神经网络的黑箱特性严重阻碍了人们关于网络决策的直观分析与理解. 尽管文献报道了多种基于
神经元贡献度分配的决策解释方法, 但是现有方法的解释一致性难以保证, 鲁棒性更是有待改进 …

Deep learning models are favored in many research and industry areas and have reached
the accuracy of approximating or even surpassing human level. However they've long been …

A substantial amount of research has been carried out in Explainable Artificial Intelligence
(XAI) models, especially in those which explain the deep architectures of neural networks. A …

The lack of transparency of neural networks stays a major break for their use. The Layerwise
Relevance Propagation technique builds heat-maps representing the relevance of each …

Network interpretation in context of explainable AI continues to gather interest not only
because of the need to explain algorithm decisions, but also because of potential …

Interpretability in deep learning is one of the largest obstacles to more widespread adoption
of deep learning in critical applications. A variety of methods have been introduced to …

This paper aims to explain deep neural networks (DNNs) from the perspective of multivariate
interactions. In this paper, we define and quantify the significance of interactions among …

Understanding the results of deep neural networks is an essential step towards wider
acceptance of deep learning algorithms. Many approaches address the issue of interpreting …

Many efforts have been made for revealing the decision-making process of black-box
learning machines such as deep neural networks, resulting in useful local and global …

Neural network based deep learning techniques have shown great success for numerous
applications. While it is expected to understand their intrinsic decision-making processes …