The trustworthiness of machine learning has emerged as a critical topic in the field,
encompassing various applications and research areas such as robustness, security …

Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and
vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate …

In this paper, we propose an advanced method for adversarial training that focuses on
leveraging the underlying structure of adversarial perturbation distributions. Unlike …

Deep learning models have been found to be susceptible to adversarial attacks, which limits
their use in security-sensitive applications. One way to enhance the resilience of these …

Abstract Capsule Networks (CapsNets) have gained significant attention in recent years due
to their potential for improved representation learning and robustness. However, their …

Методам машинного обучения уделялось большое внимание со стороны научного
сообщества в последнее время, и благодаря им удалось достичь значительных …