Deep learning-based medical image segmentation models suffer from performance
degradation when deployed to a new healthcare center. To address this issue …

The main premise of federated learning (FL) is that machine learning model updates are
computed locally to preserve user data privacy. This approach avoids by design user data to …

Abstract Training Deep Neural Networks (DNNs) with adversarial examples often results in
poor generalization to test-time adversarial data. This paper investigates this issue, known …

Deep neural networks have been found to be vulnerable to adversarial noise. Recent works
show that exploring the impact of adversarial noise on intrinsic components of data can help …

Transformer-based models, such as BERT and GPT, have been widely adopted in natural
language processing (NLP) due to their exceptional performance. However, recent studies …

Adversarial training (AT) is a canonical method for enhancing the robustness of deep neural
networks (DNNs). However, recent studies empirically demonstrated that it suffers from …

Abstract Machine learning (ML) and artificial intelligence (AI) approaches are often criticized
for their inherent bias and for their lack of control, accountability, and transparency …

Adversarial attacks pose significant threats to deploying state-of-the-art classifiers in safety-
critical applications. Two classes of methods have emerged to address this issue: empirical …

Automatic harmonic analysis of symbolic music is an important and useful task for both
composers and listeners. The task consists of two components: recognizing harmony labels …

The main premise of federated learning is that machine learning model updates are
computed locally, in particular to preserve user data privacy, as those never leave the …