English(EN) Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks

新框架增强了复杂数据结构上神经网络的不确定性量化能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员推出了一种新颖的框架——共形单复形神经网络（CF-SNN），旨在为单复形神经网络（SNNs）提供严格的不确定性量化。SNNs 适用于分析结构化在单复形上的数据，单复形是图的更高阶泛化。所提出的 CF-SNN 框架可同时为多个目标生成预测集，确保真实标签以用户指定的概率包含在内。实验表明，CF-SNN 在保持所需覆盖水平的同时，提供了灵活性并生成了信息丰富的预测集。 AI

影响通过提供稳健的不确定性量化，增强了神经网络在高风险应用中的可靠性。

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arXiv stat.ML TIER_1 English(EN) · Lekun Wang, Jaidev Goel, Yulia R. Gel, Yao Xie · 2026-11-02 05:00

Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks

arXiv:2610.41166v1 Announce Type: new Abstract: Simplicial neural networks (SNNs) are powerful tools for prediction tasks on higher-order relational datasets stored on simplicial complexes (SCs), a higher-order generalization of graphs. However, SNNs usually lack rigorous uncerta…

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Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks

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