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Zhe Wang

Zhe Wang has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining; often with Qin Zhou, Arnaud Doucet, Jingyuan Chen.

3 papers in our library · 2024–2026

OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 69 times in our library · ⭐ Paper Stars ranking: #8121

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

🔬Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of EducationShanghai · China2026 · 2 paperscurrent🎓East China University of Science and TechnologyShanghai · China2026 · 1 papercurrent

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 · 3 worksComputer Vision and Pattern Recognition · 3 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workHealth Informatics · 1 workLanguage and Linguistics · 1 workMedicine · 1 workRadiology, Nuclear Medicine and Imaging · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20262 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Simplified and Generalized Masked Diffusion for Discrete Data (2024 · 90 citations)
    Masked (absorbing-state) diffusion is a possible alternative to autoregressive generation for discrete data such as text and pixels.
  2. Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning (2026)
    The paper addresses two weaknesses of reinforcement-learning approaches to automatic radiology report generation (RRG).
  3. Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning (2026)
    Parameter-efficient fine-tuning (PEFT) makes adapting large language models affordable, but its small trainable space limits expressiveness.

 Co-authors here

3 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 69 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated

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