hámarksrobustness
Hámarksrobustness refers to the resilience of a system, algorithm, or model—particularly in the context of machine
Robustness in this context ensures that a system maintains its performance even when faced with noisy, incomplete,
Adversarial robustness is a specialized subset of hámarksrobustness, focusing on the model’s ability to resist deliberate
Beyond adversarial scenarios, robustness also addresses other challenges like data drift, where statistical properties of input
Research in hámarksrobustness spans multiple fields, including computer vision, natural language processing, and reinforcement learning. Frameworks