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Lianghui Zhu

Lianghui Zhu has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Xinggang Wang, Xinlong Wang, Qian Zhang.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 9 · 43 works · 669 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 214 times in our library · ⭐ Paper Stars ranking: #3653

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Tsinghua Shenzhen International Graduate SchoolShenzhen · China2022–2026 · 22 paperscurrent🎓Tsinghua UniversityBeijing · China2023–2026 · 18 paperscurrent🎓Huazhong University of Science and TechnologyWuhan · China2021–2026 · 9 paperscurrent🏥Zhongnan Hospital of Wuhan UniversityWuhan · China2026 · 1 papercurrent🔬Jiang Xi Institute for Drug ControlNanchang · China2023–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model (2024 · 500 citations)
    Vision Transformers give each patch global, input-dependent context, but self-attention becomes slow and memory-hungry on high-resolution images with many tokens.
  2. JudgeLM: Fine-tuned Large Language Models are Scalable Judges (2023 · 145 citations)
    Open-ended outputs of large language models are hard to assess. Classic metrics such as BLEU, ROUGE or BERTScore miss answer quality. Human arenas are reliable but slow and costly. GPT-4 used as a grader raises reproducibility and privacy concerns. The authors respond by training open-source LLMs from the Vicuna…

 Co-authors here

4 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 214 times (of the 149,439 papers whose bibliographies we read); 38 citing papers with a page here →.

Updated

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