Neural networks have become increasingly popular. Nevertheless, understanding their
decision process turns out to be complicated. One vital method to explain a models' …

Feature attributions are a popular tool for explaining the behavior of Deep Neural Networks
(DNNs), but have recently been shown to be vulnerable to attacks that produce divergent …

Abstract Model attribution is a critical component of deep neural networks (DNNs) for its
interpretability to complex models. Recent studies bring up attention to the security of …

While the evaluation of explanations is an important step towards trustworthy models, it
needs to be done carefully, and the employed metrics need to be well-understood …

Deep neural networks are the default choice of learning models for computer vision tasks.
Extensive work has been carried out in recent years on explaining deep models for vision …

Modern deep learning methods have long been considered black boxes due to the lack of
insights into their decision-making process. However, recent advances in explainable …

Interpretability is an important area of research for safe deployment of machine learning
systems. One particular type of interpretability method attributes model decisions to input …

While enjoying the great achievements brought by deep learning (DL), people are also
worried about the decision made by DL models, since the high degree of non-linearity of DL …

While rule-based attribution methods have proven useful for providing local explanations for
Deep Neural Networks, explaining modern and more varied network architectures yields …

We present a novel method for reliably explaining the predictions of neural networks. We
consider an explanation reliable if it identifies input features relevant to the model output by …