How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks

arXiv:2606.28242v1 Announce Type: new 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 regimes and for online SGD. Here, we instead study how generalization scales with the number of trainable parameters and the number of samples in a feature-learning model. We analyze $\ell_2$-regularized empirical test error minimization in a quadratic two-layer network in a finit

Source: arXiv cs.LG — read the full report at the original publisher.

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