2 citations · 2 across the 4 of their papers we have counts for
4 papers
Obtaining Example-Based Explanations from Deep Neural Networks
Genghua Dong, Henrik Boström, Michalis Vazirgiannis +1
Most techniques for explainable machine learning focus on feature attribution, i.e., values are assigned to the features such that their sum equals the prediction. Example attribut…
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer +2
Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in various graph representation learning tasks. Recently, studies revealed their vulnerability to advers…
A Simple and Yet Fairly Effective Defense for Graph Neural Networks
Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer +2
Graph Neural Networks (GNNs) have emerged as the dominant approach for machine learning on graph-structured data. However, concerns have arisen regarding the vulnerability of GNNs…
Image Keypoint Matching using Graph Neural Networks
Nancy Xu, Giannis Nikolentzos, Michalis Vazirgiannis +1
Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images. When…