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English(EN) Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks

揭示稀疏激活神经网络的新Rademacher界限

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为稀疏激活神经网络开发了新的Rademacher界限，特别关注单隐藏层ReLU模型。这项研究发表在Hugging Face的一篇论文中，分析了与输入相关稀疏性相关的统计复杂性。研究结果确立了改进先前工作的界限，消除了显式维度因子，并展示了跨输入改变激活单元如何维持宽度依赖性，而输入域在网络复杂性中起着至关重要的作用。 AI

影响 这项研究推进了对神经网络复杂性的理论理解，可能导致更高效的模型架构。

排序理由 详细介绍神经网络复杂性理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

揭示稀疏激活神经网络的新Rademacher界限

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详细介绍神经网络复杂性理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    稀疏激活神经网络的近乎紧致的Rademacher界限

    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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