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English(EN) Error Bound Analysis for the Regularized Loss of Deep Linear Neural Networks

深度线性神经网络：正则化损失的误差界分析

作者 PulseAugur 编辑部 · [1 个来源] ·

本文深入探讨了深度线性神经网络的优化原理，这是近期备受关注的一个主题。研究人员分析了临界点周围正则化平方损失的局部几何形状，并给出了封闭形式的表征。该研究在特定条件下为正则化损失建立了误差界，根据梯度范数量化了到临界点集的距离。该误差界支持了一阶方法的线性收敛推导，这一发现通过数值实验得到证实，实验显示梯度下降法线性收敛到临界点。 AI

影响 为深度线性神经网络的优化提供了理论见解，可能有助于开发更有效的训练算法。

排序理由 关于神经网络优化理论分析的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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深度线性神经网络：正则化损失的误差界分析

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关于神经网络优化理论分析的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Po Chen, Rujun Jiang, Peng Wang ·

    深度线性神经网络正则化损失的误差界分析

    arXiv:2502.11152v4 Announce Type: replace-cross Abstract: The optimization foundations of deep linear networks have recently received significant attention. However, due to their inherent non-convexity and hierarchical structure, analyzing the loss functions of deep linear networ…

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