Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime 事件

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime arXiv:2509.24882v2 Announce Type: replace-cross Abstract: Neural scaling laws underlie many of the recent advances in deep learning, yet their theoretical understanding remains largely confined to linear models. In this work, we present a systematic analysis of scaling laws for quadratic and diagonal neural networks in the feature learning regime. Leveraging connections with matrix compressed sensing and LASSO, w