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Zhenhua Dong

Zhenhua Dong has 2 papers in our library, in 2026, most of them on Efficient models & compression; often with Hui-Ling Zhen, Xianzhi Yu, Haoli Bai.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 30 · 199 works · 3,766 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 21 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #13243

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 · China2016–2026 · 125 paperscurrent🎓Hong Kong Polytechnic UniversityHong Kong · China2012–2017 · 19 papers🎓Nankai UniversityTianjin · China2008–2012 · 6 papers🏥First Hospital of Jilin UniversityChangchun · China2024–2025 · 5 papers🎓University of Electronic Science and Technology of ChinaChengdu · China2017–2018 · 4 papers🎓Shandong First Medical UniversityJinan · China2023–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 10 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 8The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksEngineering · 1 workComputational Theory and Mathematics · 2 worksComputer Vision and Pattern Recognition · 2 worksElectrical and Electronic Engineering · 1 workArtificial Intelligence · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 2 works

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. 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.
  2. Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats (2026)
    Most post-training quantization (PTQ) methods for LLMs were built for integer formats. It is unclear how they behave under Microscaling Floating-Point (MXFP), a block-scaled format with growing hardware support. The authors benchmark PTQ under MXFP8 and MXFP4, simulated with Microsoft's microxcaling library. They…

All works

4 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. A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetDugang Liu et al.2020 · 194 citations
  2. MINER: Multi-Interest Matching Network for News Recommendation ↗Findings of the Association for Computational Linguistics: ACL 2022 · no summary yetJian Li et al.2022 · 82 citations
  3. 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
  4. Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point FormatsAnnual Meeting of the Association for Computational Linguistics (ACL)Manyi Zhang, Ji-Fu Li, Zhongao Sun, et al.2026

 Co-authors here

8 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 21 times (of the 149,439 papers whose bibliographies we read).

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