Shen Ge
Impact in
- Artificial Intelligence top 2%
- Topic Modeling
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
- Health Informatics top 5%
Papers in
-
- Topic Modeling 11
- Domain Adaptation and Few-Shot Learning 4
- Natural Language Processing Techniques 3
- Machine Learning in Healthcare 3
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- Multimodal Machine Learning Applications 8
- Advanced Image and Video Retrieval Techniques 3
- Co-authors
- Xian Wu (18 shared papers)Fenglin Liu (9 shared papers)Yuexian Zou (7 shared papers)Wei Fan (6 shared papers)Nikos Mamoulis (4 shared papers)S. Kevin Zhou (2 shared papers)Shuxin Yang (2 shared papers)Li Xiao (2 shared papers)
In The Last Decade
Shen Ge
26 papers receiving 956 citations
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 673
- Health Informatics 25
- Computer Vision and Pattern Recognition 374
- Signal Processing 192
- Computer Networks and Communications 170
Countries citing papers authored by Shen Ge
This map shows the geographic impact of Shen Ge'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 Shen Ge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shen Ge more than expected).
Fields of papers citing papers by Shen Ge
This network shows the impact of papers produced by Shen Ge. 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 Shen Ge. The network helps show where Shen Ge may publish in the future.
Co-authors
The 25 scholars most cited alongside Shen Ge, 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 26 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 181 | |
| 2 | 2002 | 115 | |
| 3 | 2022 | 100 | |
| 4 | 2021 | 84 | |
| 5 | 2012 | 83 | |
| 6 | 2023 | 78 | |
| 7 | 2020 | 71 | |
| 8 | 2019 | 38 | |
| 9 | 2012 | 24 | |
| 10 | 2022 | 21 | |
| 11 | 2020 | 19 | |
| 12 | Prophet Attention: Predicting Attention with Future Attention | 2020 | 17 |
| 13 | 2021 | 17 | |
| 14 | 2015 | 16 | |
| 15 | 2021 | 14 | |
| 16 | 2020 | 13 | |
| 17 | 2020 | 13 | |
| 18 | 2020 | 11 | |
| 19 | 2025 | 11 | |
| 20 | 2013 | 11 |
About Shen Ge
Shen Ge is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Signal Processing and Information Systems, having authored 26 papers that have together received 974 indexed citations. Recurring topics across this work include Topic Modeling (11 papers), Multimodal Machine Learning Applications (8 papers), Domain Adaptation and Few-Shot Learning (4 papers), Data Management and Algorithms (3 papers), Natural Language Processing Techniques (3 papers), Advanced Image and Video Retrieval Techniques (3 papers), Advanced Database Systems and Queries (3 papers) and Machine Learning in Healthcare (3 papers). The work is most often cited by research in Artificial Intelligence (673 citations), Health Informatics (25 citations), Computer Vision and Pattern Recognition (374 citations), Signal Processing (192 citations) and Computer Networks and Communications (170 citations). Shen Ge has collaborated with scholars based in China, Hong Kong and Macao. Frequent co-authors include Xian Wu, Fenglin Liu, Yuexian Zou, Wei Fan, Nikos Mamoulis, S. Kevin Zhou, Shuxin Yang, Li Xiao, Panagiotis Bouros and Bin Yu. Their work appears in journals such as Medical Image Analysis, Knowledge and Information Systems, Environmental Science Nano, ACM Transactions on Knowledge Discovery from Data and Electronics Letters.
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.