Sentao Chen
Impact in
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- Emotion and Mood Recognition
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
- Sentiment Analysis and Opinion Mining
Papers in
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- Domain Adaptation and Few-Shot Learning 19
- Machine Learning and ELM 8
- Advanced Graph Neural Networks 2
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- Cancer-related molecular mechanisms research 11
- Co-authors
- Lin Zheng (8 shared papers)Xiaowei Yang (7 shared papers)Cheng Liu (4 shared papers)Dazhi Jiang (3 shared papers)Hanrui Wu (4 shared papers)Dongmin Huang (1 shared paper)Mehrtash Tafazzoli Harandi (2 shared papers)Michael K. Ng (2 shared papers)
In The Last Decade
Sentao Chen
29 papers receiving 567 citations
Peers
Comparison fields: 5 of 79
- Experimental and Cognitive Psychology 138
- Artificial Intelligence 298
- Computer Vision and Pattern Recognition 172
- Cognitive Neuroscience 119
- Human-Computer Interaction 33
Countries citing papers authored by Sentao Chen
This map shows the geographic impact of Sentao Chen'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 Sentao Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sentao Chen more than expected).
Fields of papers citing papers by Sentao Chen
This network shows the impact of papers produced by Sentao Chen. 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 Sentao Chen. The network helps show where Sentao Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Sentao Chen, 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 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 129 | |
| 2 | 2020 | 49 | |
| 3 | 2020 | 45 | |
| 4 | 2021 | 42 | |
| 5 | 2021 | 34 | |
| 6 | 2023 | 31 | |
| 7 | 2022 | 29 | |
| 8 | 2023 | 27 | |
| 9 | 2020 | 26 | |
| 10 | 2022 | 20 | |
| 11 | 2021 | 20 | |
| 12 | 2020 | 19 | |
| 13 | 2024 | 13 | |
| 14 | 2022 | 12 | |
| 15 | 2023 | 11 | |
| 16 | 2012 | 11 | |
| 17 | 2023 | 9 | |
| 18 | 2024 | 7 | |
| 19 | 2023 | 7 | |
| 20 | 2018 | 6 |
About Sentao Chen
Sentao Chen is a scholar working on Artificial Intelligence, Cancer Research, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology and Geography, Planning and Development, having authored 31 papers that have together received 575 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (19 papers), Cancer-related molecular mechanisms research (11 papers), Multimodal Machine Learning Applications (10 papers), Machine Learning and ELM (8 papers), Advanced Graph Neural Networks (2 papers), Bioinformatics and Genomic Networks (2 papers), Recommender Systems and Techniques (2 papers) and Emotion and Mood Recognition (2 papers). The work is most often cited by research in Experimental and Cognitive Psychology (138 citations), Artificial Intelligence (298 citations), Computer Vision and Pattern Recognition (172 citations), Cognitive Neuroscience (119 citations) and Human-Computer Interaction (33 citations). Sentao Chen has collaborated with scholars based in China, Australia and Hong Kong. Frequent co-authors include Lin Zheng, Xiaowei Yang, Cheng Liu, Dazhi Jiang, Hanrui Wu, Dongmin Huang, Mehrtash Tafazzoli Harandi, Michael K. Ng, Le Han and Xiaolan Liu. Their work appears in journals such as Pattern Recognition, Knowledge-Based Systems, Neurocomputing, Journal of Chemical Information and Modeling and IEEE Transactions on Neural Networks and Learning Systems.
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.