English(EN) Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors

图神经网络理论通过受分岔启发的激活函数打破过平滑

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-01 04:00

研究人员为图神经网络（GNN）开发了一个新的理论框架，解决了过平滑问题，即在深度网络中节点特征变得无法区分。通过分岔理论的视角分析过平滑，他们发现用特定的函数替换标准的ReLU激活函数可以破坏同质状态的稳定性。这一理论突破导致了稳定的、非同质模式的出现，这些模式能够抵抗过平滑，并具有经过验证的模式幅度缩放定律和在网络初始化中的实际应用。 AI

影响引入了一种理论方法，通过缓解过平滑来提高GNN性能，有可能增强其在复杂的基于图的任务中的应用。

排序理由学术论文，详细介绍了针对GNN中已知问题的 novel 理论方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Erkan Turan, Gaspard Abel, Maysam Behmanesh, Emery Pierson, Maks Ovsjanikov · 2026-06-01 04:00

超越ReLU：分叉、过平滑与拓扑先验

arXiv:2602.15634v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) learn node representations through iterative network-based message-passing. While powerful, deep GNNs suffer from oversmoothing, where node features converge to a homogeneous, non-informative state. …

本文如何被排名

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超越ReLU：分叉、过平滑与拓扑先验

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