Structural Correspondence and Universal Approximation in Diagonal plus Low-Rank Neural Networks
arXiv:2605.05659v1 Announce Type: new
Abstract: The massive computational costs of scaling modern deep learning architectures have driven the widespread use of parameter-efficient low-rank structures, such as LoRA and low-rank factorization. However, …