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Hui-Ling Zhen

Hui-Ling Zhen has 2 papers in our library, in 2026, most of them on Efficient models & compression; often with Mingxuan Yuan, Xianzhi Yu, Pengxiang Zhao.

2 papers in our library · 2026

OpenAlex · h-index 2 · 26 works · 32 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #11175

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

🏢Huawei TechnologiesShenzhen · China2023–2026 · 14 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksComputer Vision and Pattern Recognition · 2 worksArts and Humanities · 1 workEngineering · 1 workArtificial Intelligence · 1 workComputational Theory and Mathematics · 1 workElectrical and Electronic Engineering · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis (2026 · 1 citation)
    Most inference compute in large language models goes to the feed-forward (FFN) blocks. Converting a dense model into a Mixture-of-Experts (MoE) can cut that cost, but earlier conversion methods needed continual training on billions of tokens. Pei et al. instead restructure FFNs after training, in a single analytical…
  2. Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026 · 1 citation)
    The paper asks whether Huawei's HiFloat number formats, built for Ascend NPUs, work well for post-training quantization (PTQ) of large language models.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Analytical FFN-to-MoE Restructuring via Activation Pattern AnalysisAnnual Meeting of the Association for Computational Linguistics (ACL)Zehua Pei, Hui-Ling Zhen, Lancheng Zou, et al.2026 · 1 citation
  2. Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat FormatsAnnual Meeting of the Association for Computational Linguistics (ACL)Pengxiang Zhao, Hui-Ling Zhen, Xing Li, et al.2026 · 1 citation
  3. Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetManyi Zhang et al.2026

 Co-authors here

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

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read).

Updated

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