Norm Based Generalization Bound

Norm-based generalization bounds aim to mathematically explain why deep neural networks, despite their vast capacity, generalize well to unseen data. Current research focuses on improving the tightness and applicability of these bounds across various architectures, including transformers, ResNets, and sparsely connected networks, often leveraging techniques like path-norms and sparse matrix sketching to achieve this. These advancements are significant because tighter bounds provide a more accurate understanding of generalization, potentially leading to improved model design and training strategies. Ultimately, this research contributes to a more robust theoretical foundation for deep learning.