English(EN) Mechanisms of Width Scaling in Normalized Residual Networks: The Effective Alignment Dimension

新指标预测神经网络宽度缩放带来的性能提升

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-29 04:00

研究人员引入了“有效对齐维度”来更好地理解神经网络宽度缩放如何影响模型在未见过数据上的性能。这一新指标量化了激活梯度的信噪几何，为失配概率提供了有限样本界限。使用 LLaMA 风格的 Transformer、Pythia 和 ResNet-20 模型进行的实验表明，更宽的网络通常具有更大的有效对齐维度，并且表现出更少的经验性失配，直接干预证实了该统计量对损失变化的预测能力。 AI

影响提供了一个新的理论工具，用于理解和预测神经网络宽度缩放的好处。

排序理由该集群包含一篇详细介绍神经网络缩放的新理论概念和实验验证的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jinhao Zhang, Zeyu Liu, Zicheng Yan, Yunquan Zhang, Guangming Tan, Fangming Liu, Daning Cheng · 2026-07-29 04:00

归一化残差网络中的宽度缩放机制：有效对齐维度

arXiv:2607.24887v1 Announce Type: cross Abstract: Existing theories of neural-network width characterize asymptotic limits, but provide limited guidance on whether an expansion direction identified from finite training data remains beneficial on unseen data. We study this problem…

本文如何被排名

报道来源 [1]

归一化残差网络中的宽度缩放机制：有效对齐维度

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