Jun Shu
Impact in
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- Product Development and Customization
- Artificial Intelligence top 5%
- Machine Learning and Data Classification
- Domain Adaptation and Few-Shot Learning
- Text and Document Classification Technologies
Papers in
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- Machine Learning and Data Classification 8
- Domain Adaptation and Few-Shot Learning 5
- Machine Learning and Algorithms 4
- Anomaly Detection Techniques and Applications 3
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- Advanced Neural Network Applications 4
- Co-authors
- Pravin Varaiya (3 shared papers)Deyu Meng (12 shared papers)Yong Liang (3 shared papers)Haihui Huang (2 shared papers)Zongben Xu (5 shared papers)Xindong Peng (1 shared paper)Naiqi Wu (1 shared paper)Qian Zhao (4 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (3 papers)IEEE Transactions on Neural Networks and Learning Systems (3 papers)National Science Review (2 papers)Gene (1 paper)Journal of Computational Science (1 paper)
- Partner nations
- ChinaUnited StatesMacao
In The Last Decade
Jun Shu
30 papers receiving 540 citations
Peers
Comparison fields: 5 of 111
- Management of Technology and Innovation 54
- Artificial Intelligence 204
- Computer Vision and Pattern Recognition 107
- Marketing 44
- Health Information Management 20
Countries citing papers authored by Jun Shu
This map shows the geographic impact of Jun Shu'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 Jun Shu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Shu more than expected).
Fields of papers citing papers by Jun Shu
This network shows the impact of papers produced by Jun Shu. 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 Jun Shu. The network helps show where Jun Shu may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Shu, 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 36 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 93 | |
| 2 | 2003 | 66 | |
| 3 | 2010 | 59 | |
| 4 | Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting | 2019 | 54 |
| 5 | 2021 | 41 | |
| 6 | 2007 | 30 | |
| 7 | 2023 | 28 | |
| 8 | 2024 | 27 | |
| 9 | 2022 | 27 | |
| 10 | 2022 | 18 | |
| 11 | 2021 | 17 | |
| 12 | 2019 | 15 | |
| 13 | 2021 | 10 | |
| 14 | 2021 | 9 | |
| 15 | 2020 | 9 | |
| 16 | 2018 | 7 | |
| 17 | 2022 | 7 | |
| 18 | 2023 | 5 | |
| 19 | 2003 | 5 | |
| 20 | 2022 | 4 |
About Jun Shu
Jun Shu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Molecular Biology and Civil and Structural Engineering, having authored 36 papers that have together received 557 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (8 papers), Domain Adaptation and Few-Shot Learning (5 papers), Advanced Neural Network Applications (4 papers), Machine Learning and Algorithms (4 papers), Anomaly Detection Techniques and Applications (3 papers), Gene expression and cancer classification (3 papers), Quality Function Deployment in Product Design (3 papers) and Bioinformatics and Genomic Networks (3 papers). The work is most often cited by research in Management of Technology and Innovation (54 citations), Artificial Intelligence (204 citations), Computer Vision and Pattern Recognition (107 citations), Marketing (44 citations) and Health Information Management (20 citations). Jun Shu has collaborated with scholars based in China, United States and Macao. Frequent co-authors include Pravin Varaiya, Deyu Meng, Yong Liang, Haihui Huang, Zongben Xu, Xindong Peng, Naiqi Wu, Qian Zhao, Seung Ki Moon and Qi Xie. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems, National Science Review, Gene and Journal of Computational Science.
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.