Robust Version

Robustness in machine learning models is a crucial area of research focusing on improving the reliability and resilience of models against various forms of uncertainty, including noisy data, adversarial attacks, and environmental variations. Current research emphasizes developing novel algorithms and architectures, such as transformers, to enhance model performance under these challenging conditions, often incorporating techniques like knowledge distillation, data augmentation, and robust optimization. This work is significant because it directly addresses the limitations of existing models, leading to more reliable and trustworthy AI systems across diverse applications, from medical imaging and autonomous navigation to natural language processing and personalized pricing.