We introduce DFReg, a physics-inspired regularization method for deep neural networks that operates on the global distribution of weights. Drawing from Density Functional Theory (DFT), DFReg applies a functional penalty to encourage smooth, diverse, and well-distributed weight configurations. Unlike traditional techniques such as Dropout or L2 decay, DFReg imposes global structural regularity without architectural changes or stochastic perturbations.
Cite
@article{arxiv.2507.00101,
title = {DFReg: A Physics-Inspired Framework for Global Weight Distribution Regularization in Neural Networks},
author = {Giovanni Ruggieri},
journal= {arXiv preprint arXiv:2507.00101},
year = {2025}
}