The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Rehearsal approaches enjoy immense popularity with Continual Learning (CL)
practitioners. These methods collect samples from previously encountered data distributions …

Although link prediction on graphs has achieved great success with the development of
graph neural networks (GNNs), the potential robustness under the edge noise is still less …

Deep neural networks continue to awe the world with their remarkable performance. Their
predictions, however, are prone to be corrupted by adversarial examples that are …

Abstract Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great
efforts made by the Explainable AI (XAI) community, explanations lack robustness …

Despite the fast progress of explanation techniques in modern Deep Neural Networks
(DNNs) where the main focus is handling" how to generate the explanations", advanced …

Popular approaches for quantifying predictive uncertainty in deep neural networks often
involve distributions over weights or multiple models, for instance via Markov Chain …

In the last years, the number of IoT devices deployed has suffered an undoubted explosion,
reaching the scale of billions. However, some new cybersecurity issues have appeared …