M
Mingzhe Wang
Researcher at Princeton University
Publications - 16
Citations - 7169
Mingzhe Wang is an academic researcher from Princeton University. The author has contributed to research in topics: Automated theorem proving & Embedding. The author has an hindex of 6, co-authored 14 publications receiving 5372 citations. Previous affiliations of Mingzhe Wang include University of Michigan & Peking University.
Papers
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Proceedings ArticleDOI
LINE: Large-scale Information Network Embedding
TL;DR: A novel network embedding method called the ``LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted, and optimizes a carefully designed objective function that preserves both the local and global network structures.
Proceedings ArticleDOI
LINE: Large-scale Information Network Embedding
TL;DR: LINE as discussed by the authors proposes a network embedding method called LINE, which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted, and optimizes a carefully designed objective function that preserves both the local and global network structures.
Book ChapterDOI
Structured Matching for Phrase Localization
TL;DR: A structured matching of phrases and regions that encourages the semantic relations between phrases to agree with the visual relations between regions that is formulated as a discrete optimization problem and relaxed to a linear program.
Proceedings Article
Premise Selection for Theorem Proving by Deep Graph Embedding
TL;DR: A deep learning-based approach to the problem of premise selection: selecting mathematical statements relevant for proving a given conjecture by representing a higher-order logic formula as a graph that is invariant to variable renaming but still fully preserves syntactic and semantic information.
Proceedings Article
Learning to Prove Theorems by Learning to Generate Theorems
Mingzhe Wang,Jia Deng +1 more
TL;DR: In this article, a neural generator that automatically synthesizes theorems and proofs for the purpose of training a theorem prover is proposed, and experiments on real-world tasks demonstrate that synthetic data from their approach improves the theorem provers and advances the state of the art of automated theorem proving.