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English(EN) Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction

新算法 Rennala MVR 改进并行优化时间复杂度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的并行随机优化算法 Rennala MVR，旨在改善异构计算环境下的时间复杂度。该方法基于 Rennala SGD 算法，通过引入基于动量的方差缩减，以期在系统不稳定和网络延迟普遍存在的场景下提升性能。理论分析和基准测试的实验结果表明，Rennala MVR 可以在特定参数范围内，尤其是在光滑非凸优化任务中，显著提高时间复杂度。 AI

影响 为在分布式、异构环境中训练大规模机器学习模型带来了理论和实践上的改进。

排序理由 发布了一篇关于优化算法的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新算法 Rennala MVR 改进并行优化时间复杂度

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发布了一篇关于优化算法的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Peter Richtárik ·

    Rennala MVR：通过基于动量的方差缩减改进并行随机优化的时间复杂度

    Large-scale machine learning models are trained on clusters of machines that exhibit heterogeneous performance due to hardware variability, network delays, and system-level instabilities. In such environments, time complexity rather than iteration complexity becomes the relevant …

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