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Entropic Optimal Transport applied to large-scale simulations and active learning

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore the application of entropic optimal transport (EOT) and Sinkhorn divergence in different AI contexts. The first paper introduces FlashSinkhorn 2 (FS2), a GPU-based solver for large-scale discrete EOT problems, capable of handling massive datasets from simulations. The second paper utilizes Sinkhorn divergence for low-budget active learning, demonstrating its effectiveness in selecting crucial data points for model training, particularly in domains like medical imaging. AI

IMPACT These methods offer new computational efficiencies for large-scale AI tasks and improved data selection strategies for model training.

RANK_REASON Two academic papers published on arXiv detailing novel applications of entropic optimal transport and Sinkhorn divergence.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Entropic Optimal Transport applied to large-scale simulations and active learning

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COVERAGE [2]

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

    FlashSinkhorn 2: Block-Sparse Entropic Optimal Transport

    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 ·

    Low-Budget Active Learning through Entropic Optimal Transport

    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 …

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