Improved Generalization

Improved generalization in machine learning aims to create models that perform well on unseen data, a crucial aspect for reliable real-world applications. Current research focuses on enhancing model robustness through techniques like sharpness-aware minimization (SAM) and its variants, exploring diverse architectures such as vision transformers and graph neural networks, and leveraging self-supervised learning and transfer learning strategies. These advancements are significant because they address the limitations of models that overfit to training data, leading to more reliable and adaptable AI systems across various domains, including image recognition, natural language processing, and reinforcement learning.

Papers