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English(EN) Quantifying Depth Sufficiency in Residual Neural Networks: A First-Order Criterion

新判据量化神经网络的深度充足性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种一阶判据，用于确定残差神经网络是否已达到足够的深度。该判据基于残差非退化概念，证明只有当条件激活梯度投影到可容许的残差切线空间时，额外的深度才是有价值的。研究表明，激活梯度幅度可以作为跨越各种模型架构（包括 ResNets、GPT-2 和 Pythia 检查点）的残差深度的剩余经验价值的可靠诊断。此外，保持函数不变的增长实现了与从头开始训练相媲美的性能。 AI

影响 为优化神经网络架构和训练效率提供了理论框架。

排序理由 学术论文，详细介绍了一种新的神经网络深度理论判据。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新判据量化神经网络的深度充足性

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学术论文，详细介绍了一种新的神经网络深度理论判据。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zeyu Liu, Jinhao Zhang, Yunquan Zhang, Guangming Tan, Xiang Gao, Fangming Liu, Daning Cheng ·

    残差神经网络中深度充足性的量化：一个一阶判据

    arXiv:2608.14664v1 Announce Type: new Abstract: How can we determine whether a trained neural network is already deep enough? We study this under a fixed function-preserving residual-growth protocol specifying insertion locations, residual families, zero-output initializations, a…

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