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cs.LG2026
Diffeomorphism-Equivariant Neural Networks
Josephine Elisabeth Oettinger, Zakhar Shumaylov, Johannes Bostelmann +2
Incorporating group symmetries via equivariance into neural networks has emerged as a robust approach for overcoming the efficiency and data demands of modern deep learning. While…
cs.LG2026
SpectraKAN: Conditioning Spectral Operators
Chun-Wun Cheng, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero
Spectral neural operators, particularly Fourier Neural Operators (FNO), are a powerful framework for learning solution operators of partial differential equations (PDEs) due to the…
cs.LG2024
Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
Fabrizio Frasca, Fabian Jogl, Moshe Eliasof +4
To develop a preliminary understanding towards Graph Foundation Models, we study the extent to which pretrained Graph Neural Networks can be applied across datasets, an effort requ…