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English(EN) Compute Efficiency and Serial Runtime Tradeoffs for Stochastic Momentum Methods

研究论文分析动量方法的计算效率和运行时间权衡

一篇新研究论文探讨了重球法(HB)和加速SGD(ASGD)等随机动量方法的串行运行时间和计算效率之间的权衡。该研究证明了批量大小权衡的有限维下界,表明HB对于任意谱而言,在计算效率上并不比标准SGD有本质提升。相反,HB在更大的批量大小窗口内保持了SGD级别的效率,从而能够缩短串行运行时间。ASGD的性能取决于谱,对于快速衰减的谱,它提供了改进的小批量计算效率,但随着批量大小的增加,它会牺牲串行运行时间。 AI

影响 这项研究为优化大规模机器学习模型的训练效率提供了理论见解。

排序理由 该集群包含详细介绍随机动量方法研究成果的学术论文。

在 arXiv cs.AI 阅读 →

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Depen Morwani, Alexandru Meterez, Pranav Nair, Sham Kakade ·

    Compute Efficiency and Serial Runtime Tradeoffs for Stochastic Momentum Methods

    arXiv:2606.19179v1 Announce Type: cross Abstract: Stochastic momentum methods such as heavy ball (HB), Nesterov momentum, and variants of Accelerated SGD (ASGD) [Kidambi et al., 2018] are widely used in modern training, but their stochastic benefits depend on two distinct quantit…

  2. arXiv stat.ML TIER_1 English(EN) · Sham Kakade ·

    随机动量法的计算效率与串行运行时间权衡

    Stochastic momentum methods such as heavy ball (HB), Nesterov momentum, and variants of Accelerated SGD (ASGD) [Kidambi et al., 2018] are widely used in modern training, but their stochastic benefits depend on two distinct quantities: serial runtime, the number of iterations need…