2 papers
cs.LG2026
How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks
Julius Girardin, Emanuele Troiani, Yizhou Xu +3
Understanding how performance scales jointly with model size and data is a central problem in modern machine learning. Existing theoretical works on scaling laws typically describe…
cs.LG2026
Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
Leonardo Defilippis, Yizhou Xu, Julius Girardin +6
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…