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Showing papers from University of Bath Show all papers

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NeurIPS 2026U BathInternational Institute of InforSegmentation

Cyclic Discrete Diffusion: A Novel Multi-Class Segmentation Refinement Technique

LIBO CHEN, Souvik Ghosh, Vinay Namboodiri

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

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NeurIPS 2026U BathH CompanyMax Planck Institute for Human DExplainable AI

Explanation Mechanism Influences Human Reliance on Reinforcement Learning Agents

Xuying Zhong, Daniel Beechey, Crescent Jicol, Janina A. Hoffmann 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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NeurIPS 2026U LiverpoolIT University of CopenhagenU BathVisual grounding

Shift-Aware Identity-Guided Latent Refinement for Referring Audio–Visual Segmentation

Kun Li, Sami S Brandt, Michael Yang

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U BathLawZero / McGillMax Planck Institute for Human DLLM agents & planning

MultiSTEVE-1s: A Model Zoo and Interpretability Suite for Instruction-Following Vision Agents

Karolis Jucys, George Adamopoulos, Özgür Şimşek

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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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
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