新方法优化Adam优化器的内存参数\beta

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了一种优化Adam优化器中内存参数\beta的新颖方法。该技术涉及一个简短的试点训练阶段来选择最佳的\beta值，然后将其固定用于完整的训练过程。通过平衡采样变异性与平均过去梯度的延迟，该方法提出了一个立方内存规则，其系数从梯度探针中估计。对视觉和语言任务的回顾性评估表明，与标准的网格搜索和最佳拟合常数\beta值相比，验证差距显著减小。 AI

影响这项研究通过优化Adam优化器的内存参数，有望实现更高效、更有效的AI模型训练。

排序理由该集群包含一篇研究论文，详细介绍了一种优化AI模型训练过程的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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$新方法优化Adam优化器的内存参数\beta$

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Alberto Fern\'andez-Hern\'andez, Cristian P\'erez-Corral, Jose I. Mestre, Manuel F. Dolz, Enrique S. Quintana-Ort\'i · 2026-10-07 04:00

Early Memory Selection for Balanced Adam

arXiv:2610.08624v1 Announce Type: cross Abstract: We propose a method for choosing the shared memory parameter $\beta_1=\beta_2=\beta$ in Adam from a short pilot training. The selected $\beta$ remains fixed during the subsequent full training. A local model of Adam's normalized d…

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Early Memory Selection for Balanced Adam

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