新方法使用最优传输进行监督图预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种新的监督图预测（SGP）方法，该方法解决了具有任意节点顺序的预测图和目标图的比较难题。该方法利用最优传输松弛，特别是Gromov-Wasserstein目标函数，该函数已被确定为最适合SGP。为了提高效率，该方法通过使用基于Sinkhorn算法的参数化匹配器来摊销图匹配问题，从而允许图预测模块和匹配器联合学习。 AI

影响这项研究可能会提高基于图的机器学习任务的准确性和效率，特别是在分子结构预测等领域。

排序理由该集群包含一篇学术论文，详细介绍了一种新的监督图预测方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Federico M\'endez, Paul Krzakala, Gabriel Melo, Charlotte Laclau, R\'emi Flamary, Florence d'Alch\'e-Buc · 2026-09-15 04:00

Graph Matching Relaxations and Amortization for Supervised Graph Prediction

arXiv:2609.15437v1 Announce Type: cross Abstract: End-to-end Supervised Graph Prediction (SGP) requires a permutation-invariant loss to compare predicted and target graphs with arbitrary node orderings. Such losses typically involve a costly graph-matching problem. We first study…

本文如何被排名

报道来源 [1]

Graph Matching Relaxations and Amortization for Supervised Graph Prediction

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