Generalization Bounds for Neural Networks with Sparse Connectivity
Nong Minh Hieu ⋅ Antoine Ledent
Abstract
Empirical evidence suggests that large neural networks rarely make effective use of all available parameters, with learned solutions exhibiting pronounced sparsity. Despite this ubiquity, existing generalization theory only partially explains how such sparsity influences statistical performance. In this paper, we derive non-asymptotic excess risk bounds for deep neural networks whose layer-wise weight matrices are restricted by $\ell_p$ quasi-norm ($0
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