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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 2026DoshishaU BathKyushu University RIKEN AIPDeep learning theory

Approximation of Maximally Monotone Operators : A Graph Convergence Perspective

Standard approximation fails for discontinuous operators; encoder-decoders approximate maximally monotone operators via graph convergence, with resolvent-based parameterizations preserving maximal monotonicity.

Takashi Furuya, Yury Korolev, Takaharu Yaguchi

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 7 of 20 reviewers recommend it
lenient 1/5
medium 4/10
strict 2/5