Despite the wide applications of neural networks, there have been increasing concerns
about their vulnerability issue. While numerous attack and defense techniques have been …

This paper proposes a new method called robust mode connectivity (RMC) to enhance the
adversarial robustness of neural networks (NNs) by exploring a wider range of parameter …

Despite the effectiveness of deep neural networks in numerous natural language processing
applications, recent findings have exposed the vulnerability of these language models when …

K-Nearest Neighbor (kNN)-based deep learning methods have been applied to many
applications due to their simplicity and geometric interpretability. However, the robustness of …

Adversarial robustness is a key concept in measuring the ability of neural networks to
defend against adversarial attacks during the inference phase. Recent studies have shown …

Recent works have theoretically and empirically shown that deep neural networks (DNNs)
have an inherent vulnerability to small perturbations. Applying the Deep k-Nearest …