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English(EN) FlashSinkhorn 2: Block-Sparse Entropic Optimal Transport

熵最优传输应用于大规模模拟和主动学习

两篇新研究论文探讨了熵最优传输(EOT)和Sinkhorn散度在不同AI场景中的应用。第一篇论文介绍了FlashSinkhorn 2 (FS2),一个基于GPU的大规模离散EOT问题求解器,能够处理来自模拟的海量数据集。第二篇论文利用Sinkhorn散度进行低预算主动学习,证明了其在为模型训练选择关键数据点方面的有效性,特别是在医学成像等领域。 AI

影响 这些方法为大规模AI任务提供了新的计算效率,并改进了模型训练的数据选择策略。

排序理由 两篇在arXiv上发表的学术论文,详细介绍了熵最优传输和Sinkhorn散度的新颖应用。

在 arXiv cs.AI 阅读 →

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熵最优传输应用于大规模模拟和主动学习

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两篇在arXiv上发表的学术论文,详细介绍了熵最优传输和Sinkhorn散度的新颖应用。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Felix X. -F. Ye, Yu Chin Fabian Lim, Naigang Wang, Davis Wertheimer ·

    FlashSinkhorn 2: 块稀疏熵最优传输

    arXiv:2610.02395v1 Announce Type: new Abstract: Streaming GPU solvers for entropic optimal transport (EOT), such as FlashSinkhorn, avoid storing the dense kernel but still evaluate all $n\times m$ point pairs in every Sinkhorn iteration. We present \textbf{FlashSinkhorn~2} (FS2),…

  2. arXiv cs.LG TIER_1 English(EN) · Rim Hajal, Mathieu Besan\c{c}on, J\'er\^ome Malick ·

    低成本主动学习通过熵最优传输

    arXiv:2610.01199v1 Announce Type: new Abstract: We consider low-budget active learning, which consists of selecting a limited number of points, the coreset, such that a model can be trained to high accuracy on the selection only. This problem is particularly relevant in contexts …