arXiv stat.ML
· Papers
How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks
arXiv:2606.28242v1 Announce Type: cross Abstract: 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 generalization as a function of data or compute, often in fixed-feature or infinite-width