Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks
An input may activate few hidden units even when different inputs collectively use an entire network. We study the statistical complexity of this input-dependent sparsity in the one-hidden-layer ReLU model of Awasthi et al. (COLT 2024). For width $s$, at most $k$ active units per input, and effective weight and bias bounds $W,B$, every size-$m$ sample in the class's fixed radiu
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An input may activate few hidden units even when different inputs collectively use an entire network. We study the statistical complexity of this input-dependent sparsity in the one-hidden-layer ReLU model of Awasthi et al. (COLT 2024). For width $s$, at most $k$ active units per input, and effective weight and bias bounds $W,B$, every size-$m$ sample in the class's fixed radiu
This paper provides tighter theoretical bounds on the statistical complexity of sparsely activated neural networks, which are increasingly relevant for efficient model deployment and understanding generalization. For ML engineers, understanding these bounds can inform architectural choices and regularization strategies for models with sparse activation patterns, potentially leading to more robust and efficient designs.