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NeurIPS 2026DoshishaRiceU BaselU HelsinkiDeep learning theory

Function graph transformers universally approximate operators between function spaces

Function graph transformers lift functions to graph measures to universally approximate nonlinear operators between function spaces via standard attention and MLPs.

Takashi Furuya, S D Mis, Ivan Dokmanić, Maarten V. de Hoop and 1 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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strict 2/5
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NeurIPS 2026U Roma "Tor Vergata"U BaselU Rome Tor VergataBocconiUiT the Arctic University of NorGraph neural networks

Hyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry–Task Alignment

Hyperbolic graph neural networks outperform Euclidean models only when tasks align with hyperbolic geometry, not merely when graphs are tree-like.

Dionisia Naddeo, Jonas Linkerhägner, Nicola Toschi, Geri Skenderi and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 1/5