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English(EN) The Loss Does Not See the Basis, but Adam Does

Adam优化器在因子模型中与梯度下降不同

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文探讨了Adam和梯度下降等优化算法在应用于因子模型时行为的差异。研究表明，虽然梯度下降由于损失函数的规范对称性而隐式地偏向于低秩解，但Adam和类似的逐坐标优化器不具备这种偏向。这种差异归因于规范等变性，它对于从梯度流转移性质是必需的，但不足以恢复低秩。该研究根据恢复误差对九种更新规则进行了排序，发现Adam在Transformer中分离了规范等价的初始化，导致每个头的逆变器存在显著差异。 AI

影响 解释了Adam和梯度下降等优化器在因子模型上的行为差异，影响模型训练和恢复。

排序理由 研究论文，详细介绍了优化算法的新发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Adam优化器在因子模型中与梯度下降不同

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研究论文，详细介绍了优化算法的新发现。[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) · Devender Singh ·

    损失不看基础，但Adam看

    arXiv:2608.05136v1 Announce Type: new Abstract: Gradient descent on a factored model $W = UV^\top$ is implicitly biased toward low-rank solutions, while Adam, starting from the same small initialization, is not. We trace the difference to the gauge symmetry of the loss, its invar…

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