Overparametrization Bound

Overparametrization, the use of significantly more model parameters than theoretically needed, is a central phenomenon in modern deep learning, defying traditional generalization bounds. Current research focuses on understanding how overparametrization affects generalization performance, exploring the interplay between optimization algorithms (like SGD), model architectures (including deep neural networks and transformers), and the resulting learned features. This research aims to explain why overparameterized models often generalize well, despite their capacity to perfectly memorize training data, and to leverage this understanding to improve model efficiency and robustness. The findings have significant implications for both theoretical understanding of deep learning and practical applications, including model compression and the design of more efficient and reliable AI systems.