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New theory explains double descent in neural networks as shrinkage

By PulseAugur Editorial · [1 sources] ·

A new paper explores the mathematical theory behind double descent in multilayer neural networks, proposing that the phenomenon is driven by shrinkage rather than overfitting. The research provides non-asymptotic generalization theory for fully connected networks with path regularization, applicable to various loss functions and network architectures without width or depth limits. The study identifies that the error increases and then decreases as network width grows, with the ascent attributed to regularization-induced shrinkage, not variance from overfitting. AI

IMPACT Provides a deeper theoretical understanding of neural network behavior, potentially informing future model design and training strategies.

RANK_REASON Academic paper on a theoretical aspect of neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New theory explains double descent in neural networks as shrinkage

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Academic paper on a theoretical aspect of neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hao Yu ·

    The Mathematical Theory of Double Descent as Shrinkage, not Overfitting: Matching Bounds for Path-Regularized Multilayer Neural Networks

    arXiv:2503.02129v4 Announce Type: replace Abstract: We give a nonasymptotic generalization theory for multilayer fully connected neural networks trained with path regularization, for general Lipschitz losses and without assuming the loss bounded, the architecture sparse, or any w…

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