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Showing papers from Beijing University of Technology Show all papers

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NeurIPS 2026Beijing University of TechnologyZhejiang NormalFederated learning

Fed-AGA: An Anchor Graph Alignment Framework for Federated Unaligned Multi-view Clustering

Yichi Zhang, bohang sun, Hao Wei, Kai Di and 2 more

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

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57%Worth a look
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NeurIPS 2026U SydneyBeijing University of TechnologyIndependentU New South WalesU TwenteGraph neural networks

Stage-Aware Dual Alignment for Covariate Shift in Graph Domain Adaptation

Hongwei Wen, Can Zhang, Haoyu He, Xintao Zhao and 2 more

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

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57%Worth a look
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NeurIPS 2026Nanyang TechnologicalBeijing University of Post and TMonashBeijing Institute of Computer TeNational University of SingaporeLLM agents & planning

MMGraph-Agent: Agentic Multimodal RAG via Cache-Inspired Multimodal Knowledge HyperGraphs

Haoran Luo, Ziyue Zhu, Shangyang Wu, LINHAO LUO and 7 more

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

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NeurIPS 2026The Hong Kong University of ScieThe Hong Kong University of ScieHong Kong University of Science Beijing University of TechnologyShenzhen MSU-BITVision-language-action models

HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning

HumanoidArena benchmarks egocentric hierarchical whole-body learning via seven leg-critical tasks, finding policies solve diverse interactions but cross-tracker transfer remains fragile.

Taowen Wang, Zikang Xie, Bin Yang, Yunheng Wang and 12 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 18 on Hugging Face

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5
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NeurIPS 2026Renmin University of China- 中国人民Beijing University of TechnologyRenmin University of ChinaEfficient attention & state-space models

Flexformer: Flexible Linear Transformer with Learnable Attention Kernel

Flexformer learns attention kernels via trainable spectral frequencies in linear attention, outperforming baselines on long-sequence tasks and enabling efficient Transformer distillation.

Haoran Zhang, Zisu Dong, Feng Zhou

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 4/5
medium 7/10
strict 0/5