Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study

ArXi:2410.00357v2 Announce Type: replace Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a complete theoretical framework for understanding these scaling laws remains underdeveloped. In this paper, we explore the neural scaling laws for deep operator networks, which involve learning mappings between function spaces, with a focus on the Chen and Chen style architecture.