Jun Shu
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Molecular Biology
- Computer Networks and Communications top 10%
- Management of Technology and Innovation top 10%
- Topics
- Machine Learning and Data Classification (8 papers)Domain Adaptation and Few-Shot Learning (5 papers)Machine Learning and Algorithms (4 papers)
- Cited by
- Management of Technology and InnovationArtificial IntelligenceComputer Vision and Pattern Recognition
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Pattern Analysis and Machine IntelligenceScientific Reports
- Partner nations
- ChinaMacaoUnited States
In The Last Decade
Jun Shu
29 papers receiving 521 citations
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 205
- Computer Vision and Pattern Recognition 104
- Molecular Biology 95
- Computer Networks and Communications 74
- Management of Technology and Innovation 54
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-authorship network of co-authors of Jun Shu
This figure shows the co-authorship network connecting the top 25 collaborators of Jun Shu. A scholar is included among the top collaborators of Jun Shu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jun Shu. Jun Shu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 24 | |
| 6 | 1 | |
| 7 | 4 | |
| 8 | 88 | |
| 9 | 15 | |
| 10 | 3 | |
| 11 | 27 | |
| 12 | 17 | |
| 13 | 1 | |
| 14 | 1 | |
| 15 | 9 | |
| 16 | 1 | |
| 17 | 9 | |
| 18 | Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting | 54 |
| 19 | 15 | |
| 20 | 4 |
About Jun Shu
Jun Shu is a scholar working on Artificial Intelligence, Management of Technology and Innovation and Computer Vision and Pattern Recognition, having authored 35 papers that have together received 540 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (8 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Machine Learning and Algorithms (4 papers). The work is most often cited by research in Management of Technology and Innovation (54 citations), Artificial Intelligence (205 citations) and Computer Vision and Pattern Recognition (104 citations). Jun Shu has collaborated with scholars based in China, Macao and United States. Frequent co-authors include Pravin Varaiya, Deyu Meng, Yong Liang, Haihui Huang, Zongben Xu, Qian Zhao, Xindong Peng, Naiqi Wu, Soundar Kumara and Seung Ki Moon. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Scientific Reports.
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.