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20232026
most citedHardness of Learning Neural Networks under the Manifold Hypothesis

2 citations · 5 across the 10 of their papers we have counts for

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cs.LG2025

Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing

Lukas Fesser, Melanie Weber

Graph Neural Networks have emerged as the most popular architecture for graph-level learning, including graph classification and regression tasks, which frequently arise in areas s…

cs.LG2025

Enhancing the Utility of Higher-Order Information in Relational Learning

Raphael Pellegrin, Lukas Fesser, Melanie Weber

Higher-order information is crucial for relational learning in many domains where relationships extend beyond pairwise interactions. Hypergraphs provide a natural framework for mod…