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English(EN) Information Allocation Dynamics in Neural Network Optimization

新研究将神经网络优化器偏差视为信息分配动力学

作者 PulseAugur 编辑部 · [2 个来源] ·

本文从新的视角介绍了理解神经网络训练中优化器的隐式偏差。它提出了一种“信息分配动力学”方法，将偏差视为训练信号在类权重和类偏置参数路径之间的相对分布。这种分配可以通过连续的“预条件指数p”来控制，影响残差信号的保留和更新方式。该研究将优化器偏差的分析从最终解决方案的几何形状转移到训练过程中的动态更新过程，强调了其对参数轨迹和泛化能力的影响。 AI

影响 这项研究为理解和潜在地操纵神经网络训练动力学提供了一个新颖的理论视角，这可能导致更有效和更强大的模型开发。

排序理由 该集群包含一篇在arXiv上发表的学术论文，详细介绍了理解神经网络优化新理论框架。

在 arXiv cs.LG 阅读 →

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新研究将神经网络优化器偏差视为信息分配动力学

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该集群包含一篇在arXiv上发表的学术论文，详细介绍了理解神经网络优化新理论框架。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zhang Gongyue, Liu Donghan, Ren Weihong, Sheng Yixuan, Wang Zhiyong, Liu Honghai ·

    神经网络优化中的信息分配动力学

    arXiv:2607.07156v1 Announce Type: new Abstract: Different optimizers have different update biases, but these biases are usually implicit. Existing studies mainly analyze or control such biases from the geometry of the final solution. However, how optimizer bias forms during train…

  2. arXiv cs.LG TIER_1 English(EN) · Liu Honghai ·

    神经网络优化中的信息分配动力学

    Different optimizers have different update biases, but these biases are usually implicit. Existing studies mainly analyze or control such biases from the geometry of the final solution. However, how optimizer bias forms during training still lacks a clear internal mechanism. This…

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