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NeurIPS 2026UiT - The Arctic University of NU TromsøUiT The Arctic University of NorUniversité Bretagne SudNorwegian Computing CenterRepresentation & contrastive learning

NOFE – Neural Operator Function Embedding

NOFE introduces a continuous neural operator framework for function dimensionality reduction that outperforms PCA, t-SNE, and UMAP in local structure preservation and sampling-independent embeddings.

Lars Uebbing, Harald Lykke Joakimsen, Siyan Chen, Georgios Leontidis and 5 more

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

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lenient 4/5
medium 6/10
strict 0/5
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NeurIPS 2026CNRSUiT the Arctic University of NorU TromsøGraph neural networks

A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks

<|message_model|><|content_text|>The Tikhonov layer is an interpretable graph neural network layer whose learnable parameters directly reveal which node features and topological aspects drive predictions. Its closed-form propagation solves a generalized graph Tikhonov problem, yielding built-in expl

Nicolas Tremblay, Filippo Maria Bianchi, Benjamin Ricaud

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 0/5
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NeurIPS 2026U OsloU TromsøWestern Norway University of AppTime series foundation models

Progressive Memory Transformer: Memory-Aware Attention for Time-Series

Progressive Memory Transformer adds window-aligned memory to transformers, enforcing local, mid-range, and global time-series objectives for strong low-label classification and forecasting.

Tord S Stangeland, Andreas Köhler, Steffen Maeland, Adín Ramírez Rivera

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

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 2/5