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Mingzhe Wang

Mingzhe Wang has 2 papers in our library, in 2017–2020, most of them on Reasoning; often with Jia Hao Deng, Jian Wang.

2 papers in our library · 2017–2020

OpenAlex · ORCID · h-index 19 · 58 works · 977 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 70 times in our library · ⭐ Paper Stars ranking: #7310

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 UniversityBeijing · China2018–2025 · 33 paperscurrent🎓Huazhong University of Science and TechnologyWuhan · China2001–2015 · 7 papers🔬China Academy of Railway SciencesBeijing · China2003–2024 · 2 papers🔬Beijing National Research Center for Information Science and TechnologyBeijing · China2019–2021 · 2 papers🎓Princeton UniversityPrinceton · United States2020 · 2 papers🎓Beijing University of Posts and TelecommunicationsBeijing · China2017 · 2 papers

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 3The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputational Theory and Mathematics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 2 works

 Works by year

2020s

20201 paper

2010s

20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Premise Selection for Theorem Proving by Deep Graph Embedding (2017 · 106 citations)
    Automated theorem provers struggle with large mathematical libraries. Premise selection is a filtering step that picks the few known statements likely to help prove a target conjecture. Earlier learning-based systems for this task processed formulas as strings of characters or tokens. This work instead turns each…
  2. Learning to Prove Theorems by Learning to Generate Theorems (2020 · 53 citations)
    Neural theorem provers usually learn by imitating human-written formal proofs. Such data is scarce because formalizing mathematics requires rare expertise and a lot of time. Wang and Deng address this bottleneck by adding machine-made training examples. They introduce MetaGen, a learnable generator for the Metamath…

 Co-authors here

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

 Cited by

Papers of our library cite their works 70 times (of the 149,439 papers whose bibliographies we read); 40 citing papers with a page here →.

Updated

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