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English(EN) Gradient Flow Dynamics and Implicit Bias of Diagonal Linear Networks under Infinitesimal Initialization

分析了对角线性网络的梯度流动力学

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员分析了无穷小初始化下对角线性网络在回归任务中的梯度流动力学和隐式偏差。他们的工作将先前的定理扩展到深度和两层对角线性网络，证明了训练轨迹可以通过特定算法来表征。该算法收敛于一个修改后的L1范数最小化问题，表明在无穷小初始化机制下，这些架构的隐式偏差对应于这个修改后的范数。该研究还确定了结构不变流形作为影响学习过程的关键几何结构。 AI

影响 为线性神经网络的训练动力学和隐式偏差提供了理论见解，可能为未来的模型设计提供信息。

排序理由 该集群包含一篇详细介绍机器学习模型理论研究的学术论文。

在 arXiv cs.LG 阅读 →

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分析了对角线性网络的梯度流动力学

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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Jiajie Zhao, Jianxing Wang, Junjie Yang, Zhiwei Bai, Yaoyu Zhang ·

    梯度流动力学与无穷小初始化下对角线性网络的隐式偏差

    arXiv:2607.12332v1 Announce Type: new Abstract: We study the gradient flow dynamics of diagonal linear networks for regression tasks under infinitesimal initialization. Extending Theorem 1 from Pesme & Flammarion (2023), we generalize the analysis to both deep diagonal linear net…

  2. arXiv cs.LG TIER_1 English(EN) · Yaoyu Zhang ·

    梯度流动力学与无穷小初始化下对角线性网络的隐式偏差

    We study the gradient flow dynamics of diagonal linear networks for regression tasks under infinitesimal initialization. Extending Theorem 1 from Pesme & Flammarion (2023), we generalize the analysis to both deep diagonal linear networks and a broader class of two-layer diagonal …

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

    梯度流动力学与无穷小初始化下对角线性网络的隐式偏差

    We study the gradient flow dynamics of diagonal linear networks for regression tasks under infinitesimal initialization. Extending Theorem 1 from Pesme & Flammarion (2023), we generalize the analysis to both deep diagonal linear networks and a broader class of two-layer diagonal …

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