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Plain Transformers Show Power in Graph Learning Tasks

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员已经证明，标准的Transformer架构可以有效地应用于图学习任务。通过实现三个简单的修改——简化的L2注意力、自适应RMS归一化以及基于MLP的位置编码器——他们创建了Powerful Plain Graph Transformers (PPGT)。这种方法保持了Transformer的核心设计，可以更容易地整合其他领域的进步，并且在与更复杂的图神经网络架构的比较中表现出有竞争力的性能。 AI

影响 这项研究提出了一个统一的多模态学习方法，有可能简化跨语言、视觉和图领域的模型开发。

排序理由 该集群包含一篇学术论文，详细介绍了将Transformer架构应用于图学习的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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Plain Transformers Show Power in Graph Learning Tasks

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该集群包含一篇学术论文，详细介绍了将Transformer架构应用于图学习的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Philip H. S. Torr, Mark Coates ·

    普通Transformer也能成为强大的图学习者

    arXiv:2504.12588v4 Announce Type: replace-cross Abstract: Transformers have attained outstanding performance across various modalities, owing to their simple but powerful scaled-dot-product (SDP) attention mechanisms. Researchers have attempted to migrate Transformers to graph le…

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