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Showing papers from Shanghai University Show all papers

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NeurIPS 2026TsinghuaShanghaiXi’an JiaotongTsinghua University, TsinghuaXi'an JiaotongVision backbones

Vision Correlators: Correlation-Driven Visual Understanding with Hypergraphs

Mengqi Lei, Guohuan Xie, Siqi Li, Shihui Ying and 4 more

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

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NeurIPS 2026ShanghaiBayesian & probabilistic methods

Serialization Tax in Shared-Latent Exchangeable Decisions

Siming Zhang, Zhehui Shen, Shijie Chen, Xinle Gu and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026ShanghaiProbing & representation analysis

Boundary Mass in Feature Space as a Label-Budget Diagnostic for Representations

Siming Zhang, Zhehui Shen, Shijie Chen, Yansen Yu and 1 more

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

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57%Worth a look
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NeurIPS 2026National Key Laboratory of Maritshanghai universityShanghai3D vision & reconstruction

MSR-3D: Multi-Mode Semantic Representation for Open-Vocabulary 3D Scene Understanding

Jianan Fu, ting Wu, Rui Yang, Jiacheng Sun and 4 more

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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78%Highly rated
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NeurIPS 2026PekingTsinghuaZhejiangEast China NormalHuazhong AgriculturalKnowledge graphs

Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists

Intern-Atlas builds a methodological evolution graph from over one million AI papers to model how methods emerge and adapt, enabling automated idea evaluation and generation.

Yujun Wu, Dongxu Zhang, Xinchen Li, Jinhang Xu and 10 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 47 on Hugging Face

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
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78%Highly rated
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NeurIPS 2026Beijing Institute of TechnologyCity University of Hong KongSun Yat-SenWuhanShanghaiMixture of experts

LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing

LightMoE replaces redundant MoE experts with parameter-efficient modules to cut memory use 30, 50% while matching or beating existing compression methods.

Jiawei Hao, Zhiwei Hao, Jianyuan Guo, Li Shen and 3 more

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

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