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English(EN) Pair-Centric Graph Rewiring for Over-Squashing via Optimal Transport-Guided Communication Alignment

PairAlign框架解决了神经网络中的过度压缩问题

研究人员推出了一种新颖的框架PairAlign,旨在解决消息传递神经网络(MPNN)中的过度压缩问题。该方法侧重于识别和加强对跨越遥远图区域分布的任务相关信息至关重要的成对通信。PairAlign量化通信需求与拓扑支持,以查明通信短缺,并利用最优传输来指导重构决策,确保有限的边预算用于结构兼容性和短缺覆盖的有效利用。在标准图基准测试上的实验表明,PairAlign通过缓解过度压缩来提高MPNN性能。 AI

影响 引入了一种新方法,通过解决过度压缩问题来提高消息传递神经网络的性能。

排序理由 该集群包含一篇详细介绍用于提高神经网络性能的新框架的研究论文。[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 →

PairAlign框架解决了神经网络中的过度压缩问题

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该集群包含一篇详细介绍用于提高神经网络性能的新框架的研究论文。[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) · Yan Wang, Chuan-Xian Ren ·

    面向过压的以对为中心的图重构:通过最优传输引导的通信对齐

    arXiv:2608.10619v1 Announce Type: new Abstract: Message-passing neural networks (MPNNs) often struggle when task-relevant information is distributed across distant regions of a graph, since local propagation must compress remote signals through limited structural interfaces. Grap…