DFReg: A Physics-Inspired Framework for Global Weight Distribution Regularization in Neural Networks
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Abstract (arXiv)
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.
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References · 2
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
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