Chen Wu
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
-
- Advanced Neural Network Applications
Papers in
-
- Topic Modeling 7
- Domain Adaptation and Few-Shot Learning 4
- Machine Learning and ELM 4
- Co-authors
- Lei He (3 shared papers)Xueqi Cheng (5 shared papers)Yixing Fan (5 shared papers)Jiafeng Guo (5 shared papers)Ming Yan (2 shared papers)Hamed Zamani (1 shared paper)Qingyao Ai (1 shared paper)Yang Liu (1 shared paper)
- Journals
- Ceramics International (5 papers)physica status solidi (a) (2 papers)Journal of Alloys and Compounds (2 papers)ACM Transactions on Information Systems (2 papers)Journal of Materials Chemistry A (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Chen Wu
42 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 101
- Artificial Intelligence 581
- Computer Vision and Pattern Recognition 317
- Computational Mathematics 8
- Hardware and Architecture 87
- Information Systems 257
Countries citing papers authored by Chen Wu
This map shows the geographic impact of Chen Wu'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 Chen Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chen Wu more than expected).
Fields of papers citing papers by Chen Wu
This network shows the impact of papers produced by Chen Wu. 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 Chen Wu. The network helps show where Chen Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Chen Wu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 233 | |
| 2 | 2022 | 143 | |
| 3 | 2019 | 116 | |
| 4 | 2015 | 72 | |
| 5 | 2021 | 59 | |
| 6 | 2017 | 46 | |
| 7 | 2018 | 44 | |
| 8 | 2022 | 42 | |
| 9 | 2021 | 35 | |
| 10 | 2023 | 33 | |
| 11 | 2022 | 32 | |
| 12 | 2022 | 31 | |
| 13 | 2022 | 30 | |
| 14 | 2015 | 29 | |
| 15 | 2020 | 29 | |
| 16 | 2021 | 27 | |
| 17 | 2023 | 25 | |
| 18 | 2019 | 24 | |
| 19 | 2016 | 20 | |
| 20 | 2023 | 20 |
About Chen Wu
Chen Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Materials Chemistry, Information Systems and Software, having authored 45 papers that have together received 1.2k indexed citations. Recurring topics across this work include Ferroelectric and Piezoelectric Materials (12 papers), Microwave Dielectric Ceramics Synthesis (9 papers), Topic Modeling (7 papers), Dielectric properties of ceramics (5 papers), Multiferroics and related materials (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Dielectric materials and actuators (4 papers) and Machine Learning and ELM (4 papers). The work is most often cited by research in Artificial Intelligence (581 citations), Computer Vision and Pattern Recognition (317 citations), Computational Mathematics (8 citations), Hardware and Architecture (87 citations) and Information Systems (257 citations). Chen Wu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Lei He, Xueqi Cheng, Yixing Fan, Jiafeng Guo, Ming Yan, Hamed Zamani, Qingyao Ai, Yang Liu, Liang Pang and W. Bruce Croft. Their work appears in journals such as Ceramics International, physica status solidi (a), Journal of Alloys and Compounds, ACM Transactions on Information Systems and Journal of Materials Chemistry A.
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.