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English(EN) Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs

新的Manifold GCN层在图数据上表现优于最先进水平

研究人员开发了用于位于黎曼流形上的数据的新图神经网络层。这些层名为Manifold GCN,基于扩散方程和切线多层感知机,提供对节点排列和特征流形等距的等变性。在合成数据和使用右海马体三角形网格进行的真实世界阿尔茨海默病分类任务上的初步应用表明,这些层优于现有的最先进网络,同时适用于更广泛的问题。 AI

影响 引入了具有更广泛应用潜力的新型图神经网络层,可用于复杂数据结构。

排序理由 一篇详细介绍新型图神经网络层的学术论文的发表。[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Manifold GCN层在图数据上表现优于最先进水平

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一篇详细介绍新型图神经网络层的学术论文的发表。[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) · Martin Hanik, Gabriele Steidl, Christoph von Tycowicz ·

    Manifold GCN:用于流形值图的基于扩散的卷积神经网络

    arXiv:2401.14381v3 Announce Type: replace Abstract: We propose two graph neural network layers for graphs with features in a Riemannian manifold. First, based on a manifold-valued graph diffusion equation, we construct a diffusion layer that can be applied to an arbitrary number …