English(EN) Randomized Momentum Transport with Cheap Predictors

新的随机动量传输方法提高了机器学习训练效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了一种名为随机动量传输的新方法，旨在提高机器学习中使用的优化算法的效率。该技术通过选择性地评估传输校正来降低动量方法的计算成本，在不需要完全评估时使用廉价预测。该方法在 MNIST1D 和 CIFAR-10 等基准数据集上显示出降低训练损失和提高测试准确度的潜力，有望实现更快、更有效的模型训练。 AI

影响该方法可以通过减少计算开销来提高机器学习模型的训练效率。

排序理由该集群包含一篇详细介绍机器学习优化新算法方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · El Mahdi Chayti, Martin Jaggi · 2026-10-12 04:00

带廉价预测器的随机动量传输

arXiv:2610.13789v1 Announce Type: new Abstract: Momentum methods such as STORM and second-order momentum track the gradient by adding a transport correction to momentum, at the cost of an extra oracle call per iteration. We introduce randomized momentum transport, which evaluates…

本文如何被排名

报道来源 [1]

带廉价预测器的随机动量传输

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