#phase-diagrams News & Analysis

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

Researchers present a theoretical framework analyzing scaling laws for shallow neural networks in the feature learning regime, deriving phase diagrams that connect sample complexity and weight decay to risk exponents. The work bridges empirical observations in deep learning with rigorous mathematical analysis, establishing links between weight spectrum properties and generalization performance through matrix compressed sensing and LASSO theory.