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cs.LG2026
Functional Attention: From Pairwise Affinities to Functional Correspondences
Jiefang Xiao, Maolin Gao, Simon Weber +2
Learning mappings between infinite-dimensional function spaces, or operator learning, is essential for many machine learning applications. Although transformer-based operators are…
cs.LG2026
Graph Neural Networks Are Not Continuous Across Graph Resolutions
Christian Koke, Yuesong Shen, Abhishek Saroha +4
We show that contrary to conventional wisdom in the community, graph neural networks (GNNs) are not continuous with respect to all natural modes of graph convergence. As a result,…
cs.LG2026
Harnessing Data Asymmetry: Manifold Learning in the Finsler World
Thomas Dagès, Simon Weber, Daniel Cremers +1
Manifold learning is a fundamental task at the core of data analysis and visualisation. It aims to capture the simple underlying structure of complex high-dimensional data by prese…