Wangding Zeng
Impact in
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- Artificial Intelligence in Healthcare and Education
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- Multimodal Machine Learning Applications
- Face recognition and analysis
- Face and Expression Recognition
Papers in
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- Machine Learning and Data Classification 1
- Data Stream Mining Techniques 1
- Domain Adaptation and Few-Shot Learning 1
- Topic Modeling 1
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- Face and Expression Recognition 1
- Face recognition and analysis 1
- Co-authors
- Damai Dai (2 shared papers)Zhenda Xie (2 shared papers)Huazuo Gao (2 shared papers)Jiashi Li (1 shared paper)Yang Shan Tang (1 shared paper)Yongle Wu (1 shared paper)Ruijian Xu (1 shared paper)Chenggang Zhao (1 shared paper)
- Journals
- Nature (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
- Partner nations
- ChinaCanadaUnited States
In The Last Decade
Wangding Zeng
4 papers receiving 396 citations
Wangding Zeng's Hit Papers
Peers
Comparison fields: 5 of 56
- Health Informatics 9
- Computer Vision and Pattern Recognition 46
- Artificial Intelligence 52
- Experimental and Cognitive Psychology 16
- Software 5
Countries citing papers authored by Wangding Zeng
This map shows the geographic impact of Wangding Zeng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Wangding Zeng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wangding Zeng more than expected).
Fields of papers citing papers by Wangding Zeng
This network shows the impact of papers produced by Wangding Zeng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Wangding Zeng. The network helps show where Wangding Zeng may publish in the future.
Co-authors
The 25 scholars most cited alongside Wangding Zeng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning Hit paper breakdown → | 2025 | 263 |
| 2 | DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models Hit paper breakdown → | 2024 | 108 |
| 3 | 2021 | 22 | |
| 4 | 2025 | 14 | |
| 5 | 2024 | 0 |
About Wangding Zeng
Wangding Zeng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Information Systems and Insect Science, having authored 5 papers that have together received 407 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (1 paper), Face and Expression Recognition (1 paper), Emotion and Mood Recognition (1 paper), Data Stream Mining Techniques (1 paper), Expert finding and Q&A systems (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Face recognition and analysis (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Health Informatics (9 citations), Computer Vision and Pattern Recognition (46 citations), Artificial Intelligence (52 citations), Experimental and Cognitive Psychology (16 citations) and Software (5 citations). Wangding Zeng has collaborated with scholars based in China, Canada and United States. Frequent co-authors include Damai Dai, Zhenda Xie, Huazuo Gao, Jiashi Li, Yang Shan Tang, Yongle Wu, Ruijian Xu, Chenggang Zhao, Deli Chen and Y. K. Li. Their work appears in journals such as Nature and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.