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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.00101 |
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| _version_ | 1866918076663988224 |
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| author | Ruggieri, Giovanni |
| author_facet | Ruggieri, Giovanni |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DFReg: A Physics-Inspired Framework for Global Weight Distribution Regularization in Neural Networks Ruggieri, Giovanni Machine Learning 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. |
| title | DFReg: A Physics-Inspired Framework for Global Weight Distribution Regularization in Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.00101 |