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Showing papers from University of Minnesota - Twin Cities Show all papers

45%Niche pick
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Ensemble Selective Classification

Sinian Zhang, Chongwei Chen, Guanchen Li, Ju Sun

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
67%Highly rated
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AnaDiffusion: Anatomically Compositional Latent Diffusion for Controllable 3D Brain MRI Generation

Tracy Han, Lulin Liu, Bangya Liu, Yuanhao Cai and 7 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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2/20 AI panelreviewers recommend it

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AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
67%Highly rated
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Rethinking in Spikes: Mitigating Hallucinations in MDLMs with Step-Aware Decoding

Zhongxing Xu, Zhonghua Wang, Zhe Qian, Shiyan Su and 8 more

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

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AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 1/10
strict 0/5
74%Highly rated
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Aligning Language Models with Selective Prediction

RLSR aligns language models with selective prediction metrics via reinforcement learning, substantially improving risk-coverage trade-offs over baselines.

Gaoxiang Luo, Yifan Wu, Sinian Zhang, Aryan Deshwal and 1 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 0/5
72%Highly rated
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DarkVGGT: Seeing Through Darkness Using Thermal Geometry without Daylight Tax

DarkVGGT uses physics-aware thermal modeling and geometry-shared routing to boost feed-forward 3D reconstruction in darkness without impairing daylight performance.

Minseong Kweon, Wenyuan Zhao, Nuo Chen, Lulin Liu and 5 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 0/5
76%Highly rated
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UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction

UDT is a U-Net diffusion transformer using data-adaptive token merging for downsampling and upsampling that outperforms existing U-Net DiTs, achieves comparable performance to REPA, and reaches FID 1.35 with faster convergence.

Junno Yun, Yasar Utku Alcalar, Mehmet Akcakaya

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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10/20 AI panelreviewers recommend it

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