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English(EN) Dual-guided Hierarchical Edge Localization for Large-scale Optimal Transport Across Dimensions

新的 HELLO 求解器大幅提升大规模最优传输性能

研究人员开发了 HELLO,一种新颖的分层求解器,旨在解决大规模最优传输(OT)问题。该方法将 OT 视为一个边缘定位任务,利用双势能进行初始化和精炼。与现有方法相比,HELLO 在数千维度的百万级数据点规模下,实现了显著的运行时间改进和更低传输目标。该框架还展示了在单个 NVIDIA H100 GPU 上扩展到 8192 维度的 128 万个样本的能力,内存使用可控,同时保持高精度。 AI

影响 引入了一种更有效的最优传输方法,可能加速依赖于分布比较和数据集对齐的机器学习研究和应用。

排序理由 详细介绍新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的 HELLO 求解器大幅提升大规模最优传输性能

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详细介绍新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wenzhou Xia, Qiaoqiao Ding, Jingwei Liang, Xiaoqun Zhang ·

    跨维度大规模最优传输的双引导分层边缘定位

    arXiv:2609.13010v1 Announce Type: new Abstract: Optimal transport (OT) compares distributions and aligns datasets in machine learning, yet unregularized discrete OT requires a linear program with quadratically many transport variables. We propose HELLO, a hierarchical solver that…