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Topological deep learning models struggle to generalize beyond data structure

Researchers have introduced a new evaluation protocol for topological deep learning models, extending the MANTRA benchmark with more diverse manifold triangulations. Their findings indicate that while graph neural networks and higher-order message passing methods can perform well, their success depends heavily on appropriate representation and feature assignment. The study reveals a significant gap, as current models struggle to generalize beyond the combinatorial structure of data, highlighting the need for models that can understand topological structure independently of scale. AI

影响 Highlights a research gap in topological deep learning, potentially guiding future model development towards understanding scale-independent topological structures.

排序理由 The cluster contains an academic paper detailing a new evaluation protocol and findings in topological deep learning.

在 arXiv cs.LG 阅读 →

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Topological deep learning models struggle to generalize beyond data structure

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Johannes S. Schmidt, Martin Carrasco, Ernst R\"oell, Guy Wolf, Nello Blaser, Bastian Rieck ·

    无表示则无三角剖分:拓扑深度学习中的泛化

    arXiv:2605.06467v1 Announce Type: new Abstract: Despite an ever-increasing interest in topological deep learning models that target higher-order datasets, there is no consensus on how to evaluate such models. This is exacerbated by the fact that topological objects permit operati…

  2. arXiv cs.LG TIER_1 English(EN) · Bastian Rieck ·

    无表示则无三角剖分:拓扑深度学习中的泛化

    Despite an ever-increasing interest in topological deep learning models that target higher-order datasets, there is no consensus on how to evaluate such models. This is exacerbated by the fact that topological objects permit operations, such as structural refinements, that are no…