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Pingyi Zhou

Pingyi Zhou has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining, Code & programming languages; often with Fei Mi, Hao Wu, Irina Piontkovskaya.

2 papers in our library · 2022–2023

OpenAlex · ORCID · Wikidata · h-index 10 · 22 works · 402 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 75 times in our library · ⭐ Paper Stars ranking: #8333

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 · China2021–2024 · 11 paperscurrent🎓Wuhan UniversityWuhan · China2015–2019 · 7 papers🔬State Key Laboratory of Software EngineeringHubei2015–2018 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workComputer Science Applications · 1 workInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Code & programming languages · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20231 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Compilable Neural Code Generation with Compiler Feedback (2022 · 63 citations)
    Neural code generators, including pre-trained models such as CodeGPT, PLBART and CodeT5, treat programs as ordinary text and rarely check whether the output compiles.
  2. PanGu-{\Sigma}: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing (2023 · 40 citations)
    PanGu-Σ is a Huawei technical report on training a trillion-parameter language model on limited hardware. Sparse Mixture-of-Experts models usually suffer from unbalanced expert load and costly all-to-all communication, and offloading work to the host CPU tends to reduce throughput. The authors start from PanGu-α…

 Co-authors here

12 of 13 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 75 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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