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新的AdaR模型为图学习实现自适应测试时计算

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了AdaR，一种自适应循环图模型，旨在克服将基础模型应用于图学习的局限性。AdaR通过在其循环更新中显式编码归一化的步长信息和表示-目标关系，实现了对各种下游任务的灵活测试时计算，而无需更改模型参数。该模型通过基于梯度的监督信号确保收敛，这些信号在循环过程中指导表示更新，在归纳和传导设置中均表现出优于现有基线模型的性能。 AI

影响 能够更灵活、更高效地将基础模型应用于基于图的人工智能任务。

排序理由 该集群包含一篇在arXiv上发表的研究论文，详细介绍了一个新模型。[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 →

新的AdaR模型为图学习实现自适应测试时计算

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该集群包含一篇在arXiv上发表的研究论文，详细介绍了一个新模型。[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) · Shuhui Wang ·

    用于图上测试时间计算的自适应循环消息传递

    Pre-trained foundation models have demonstrated remarkable success in many domains, enabling a unified backbone to generalize across diverse downstream tasks. However, extending this paradigm to graph learning remains challenging due to the intrinsic mismatch between graph data a…

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