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Showing papers from Duke University, Duke University Show all papers

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NeurIPS 2026Duke University, DukeDukeFlow matching

Co-Evolving Interpolants and Flows via Path-Flow Alignment

Zeyu M Li, William X Chen, Xiang Cheng

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

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NeurIPS 2026U PennsylvaniaDuke University, DukeU Pennsylvania, University of PeU Illinois at Urbana-ChampaignAudio understanding

What Sound Tells You About the Room

Yiduo Hao, Yiwei Tang, Jiayang Li, Zitong Lan and 3 more

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

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72%Highly rated
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NeurIPS 2026Duke UnversityDuke University, DukeDukeDiffusion models

Variational Trajectory Optimization of Anisotropic Diffusion Schedules

A variational framework learns matrix-valued anisotropic diffusion schedules and improves EDM across CIFAR-10, AFHQv2, FFHQ, and ImageNet-64.

Pengxi Liu, Zeyu M Li, Xiang Cheng

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

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AI panel: 8 of 20 reviewers recommend it
lenient 2/5
medium 5/10
strict 1/5
92%Must read
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NeurIPS 2026DukeDepartment of Computer Science, City University of Hong KongCarnegie MellonDuke University, DukeEfficient inference & serving

LowRankArena: A Standardized Evaluation Platform for SVD-Based LLM Compression

LowRankArena standardizes SVD-based LLM compression evaluation and reveals that method rankings and speedups depend heavily on backbone and workload under aligned protocols.

Zishan Shao, Lixun Zhang, Kangning Cui, Wenhao Wu and 9 more

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

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AI panel: 19 of 20 reviewers recommend it
lenient 5/5
medium 10/10
strict 4/5
74%Highly rated
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NeurIPS 2026Duke University, DukeCapitalOneArgonne National LaboratoryDukeLLM pretraining & scaling laws

Swimba: Switch Mamba Model Scales State Space Models

Swimba routes expert SSM streams via parameter-space MoE to scale selective state space model capacity without increasing recurrent state update costs. Under matched FLOPs, it achieves slightly better average performance with minor latency and throughput trade-offs.

Zhixu Du, Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath and 2 more

Atlanta Poster Session 3, Thu, Dec 10, 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 3/5
medium 5/10
strict 1/5