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New Rademacher Bounds for Sparsely Activated Neural Networks Unveiled

By PulseAugur Editorial · [2 sources] ·

Researchers have developed nearly tight Rademacher bounds for sparsely activated neural networks, specifically focusing on the one-hidden-layer ReLU model. This work refines the understanding of statistical complexity in models where each input activates only a few hidden units. The new bounds improve upon previous findings by removing explicit dimension factors and provide insights into how input domains and network biases affect complexity, with implications for agnostic minimax excess-risk bounds. AI

IMPACT This research refines theoretical understanding of neural network complexity, potentially influencing future model design and analysis.

RANK_REASON The cluster contains an academic paper detailing theoretical advancements in neural network complexity.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Rademacher Bounds for Sparsely Activated Neural Networks Unveiled

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The cluster contains an academic paper detailing theoretical advancements in neural network complexity.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Xiaoyu Li, Zhizhou Sha, Jiaojiao Jiang, Junbin Gao, Andi Han ·

    Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks

    arXiv:2609.09130v1 Announce Type: new Abstract: 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 20…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    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…

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