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Showing papers from Technical University of Munich (TUM) Show all papers

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NeurIPS 2026CornellDelft University of TechnologyWeill Cornell Medicine, CornellTechnical University of MunichCornellAutoML & architecture search

MAdam: Metric-Aware Multi-Objective Adam

MAdam removes Adam's weighting and geometric mismatches in multi-objective optimization via a preference-conditioned curvature preconditioner, consistently improving results across tasks.

Fengbei Liu, Rachit Saluja, Sunwoo Kwak, Ruibo Wang and 4 more

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

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AI panel: 14 of 20 reviewers recommend it
lenient 3/5
medium 9/10
strict 2/5
80%Must read
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NeurIPS 2026Technical University of MunichTechnical University of MunichMercedes-BenzTU MunichGoogle, TUMSegmentation

Prompting Diffusion Models for Zero-Shot Instance Segmentation

Prompt2Seg conditions frozen diffusion segmentation models on spatial prompts for zero-shot interactive instance segmentation across diverse visual domains.

İrem Z Alagöz, Nils Morbitzer, Andrea Ramazzina, Nassir Navab and 2 more

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

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5
78%Highly rated
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NeurIPS 2026Technical University of MunichTechnical University of MunichTechnische Universität MünchenU TwentePhysics-informed ML & PDEs

Inpainting physics: self-supervised learning for context-driven fluid simulation

Steady CFD inference is reformulated as self-supervised inpainting with a local tokeniser, yielding reusable flow priors that outperform supervised surrogates under boundary shifts and enable local geometry editing.

Jonas Weidner, Yeray Martin-Ruisanchez, Daniel Rueckert, Benedikt Wiestler and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 0/5