Kewei Tu
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Management Science and Operations Research top 10%
- Information Systems top 10%
- Molecular Biology
- Co-authors
- Yong JiangXinyu WangZhongqiang HuangNguyễn BáchFei HuangTao WangSonglin YangSong‐Chun Zhu
- Topics
- Topic Modeling (71 papers)Natural Language Processing Techniques (67 papers)Multimodal Machine Learning Applications (13 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionManagement Science and Operations Research
- Partner nations
- ChinaCayman IslandsUnited States
In The Last Decade
Kewei Tu
77 papers receiving 767 citations
Peers
Comparison fields: 5 of 66
- Artificial Intelligence 714
- Computer Vision and Pattern Recognition 205
- Management Science and Operations Research 78
- Information Systems 66
- Molecular Biology 38
Countries citing papers authored by Kewei Tu
This map shows the geographic impact of Kewei Tu'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 Kewei Tu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kewei Tu more than expected).
Fields of papers citing papers by Kewei Tu
This network shows the impact of papers produced by Kewei Tu. 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 Kewei Tu. The network helps show where Kewei Tu may publish in the future.
Co-authorship network of co-authors of Kewei Tu
This figure shows the co-authorship network connecting the top 25 collaborators of Kewei Tu. A scholar is included among the top collaborators of Kewei Tu 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 Kewei Tu. Kewei Tu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 9 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 20 | |
| 9 | 29 | |
| 10 | 3 | |
| 11 | 76 | |
| 12 | 77 | |
| 13 | 6 | |
| 14 | 11 | |
| 15 | 18 | |
| 16 | 1 | |
| 17 | Sequence Prediction Using Neural Network Classiers | 3 |
| 18 | 10 | |
| 19 | Curriculum Learning of Bayesian Network Structures | 3 |
| 20 | Unsupervised Structure Learning of Stochastic And-Or Grammars | 13 |
About Kewei Tu
Kewei Tu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 82 papers that have together received 787 indexed citations. Recurring topics across this work include Topic Modeling (71 papers), Natural Language Processing Techniques (67 papers) and Multimodal Machine Learning Applications (13 papers). The work is most often cited by research in Artificial Intelligence (714 citations), Computer Vision and Pattern Recognition (205 citations) and Management Science and Operations Research (78 citations). Kewei Tu has collaborated with scholars based in China, Cayman Islands and United States. Frequent co-authors include Yong Jiang, Xinyu Wang, Zhongqiang Huang, Nguyễn Bách, Fei Huang, Tao Wang, Songlin Yang, Yong Jiang, Song‐Chun Zhu and Vasant Honavar. Their work appears in journals such as IEEE Access, Neurocomputing and Neural Computing and Applications.
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.