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Showing papers from University of Maryland College Park Show all papers

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NeurIPS 2026U Maryland, College ParkByteDanceCursorMetaMicrosoftImage recognition

LogT: Logically Think with Images for Visual Search

Yanjun Fu, Quanzeng You, Jiadong Guo, Yujie Lu and 5 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Maryland, College ParkU MarylandU Maryland - College ParkU Maryland College ParkQuantization

MPQ: A Message-Passing View of Post-Training Quantization

Yanshuo Chen, chenxi liu, Reza Shirkavand, Heng Huang

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Maryland, College ParkThe University of SydneyU Maryland College ParkInstruction tuning

Geometry-Aware Zeroth-Order Optimization for Fine-Tuning Quantized LLMs

Shaocong Ma, Weidong Cai, Heng Huang

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

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NeurIPS 2026U Maryland, College ParkU Maryland College ParkUnified multimodal models

XMNoise2Clean: Cross-Modal Denoising Under Sparse Data

Hirad Yazdankhah, Chris Metzler

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026U Maryland, College ParkU Texas at ArlingtonU Maryland College ParkFederated learning

Mitigating Data Heterogeneity Effect in Client-Reshuffling-Based Federated Learning

Su Zhang, Peiran Yu, Heng Huang

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

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NeurIPS 2026U Maryland, College ParkU Maryland College ParkAlignment & preference optimization

Label-Free Consistency Correction for Weak-to-Strong Generalization

Qi He, Heng Huang

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

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NeurIPS 2025WorkshopU Maryland, College ParkCapital OneUSCCapitalOneU Maryland College ParkRL for LLMs

Improving Consistency in Retrieval Augmented Systems With Group Similarity Rewards

A framework decomposes RAG consistency into retrieval, generation, and end-to-end components, and PS-GRPO group similarity rewards train Con-RAG to boost consistency and accuracy across paraphrased queries.

Faisal Hamman, Chenyang Zhu, Anoop Kumar, Xujun Peng and 3 more

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

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AI panel: 14 of 20 reviewers recommend it
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