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English(EN) A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport

Apple研究人员发布更快的最优传输方法

作者 PulseAugur 编辑部 · [1 个来源] ·

Apple Machine Learning Research 发表了一篇论文，详细介绍了一种用于基于核的最优传输的新型半光滑牛顿法。该方法旨在克服现有估计器的计算限制，这些限制在样本量较大时会变得难以处理。所提出的方法在合成和真实数据集上都比以前的方法有显著的加速，实现了 O(1/√k) 的全局收敛率和局部二次收敛。 AI

影响 这种新方法可以实现最优传输在机器学习应用中更高效的使用，特别是在高维数据分析中。

排序理由 该集群包含一篇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 Apple Machine Learning Research 阅读 →

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

Apple研究人员发布更快的最优传输方法

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该集群包含一篇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. Apple Machine Learning Research TIER_1 English(EN) ·

    面向基于核的最优传输的专用半光滑牛顿法

    Kernel-based optimal transport (OT) estimators offer an alternative, functional estimation procedure to address OT problems from samples. Recent works suggest that these estimators are more statistically efficient than plug-in (linear programming-based) OT estimators when compari…

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