Xin Bi
Impact in
- Artificial Intelligence top 5%
- Machine Learning and ELM
- Advanced Graph Neural Networks
- Topic Modeling
- Seismology and Earthquake Studies
- Domain Adaptation and Few-Shot Learning
Papers in
-
- Machine Learning and ELM 13
- Domain Adaptation and Few-Shot Learning 10
- Advanced Graph Neural Networks 8
- Topic Modeling 5
-
- Face and Expression Recognition 6
- Co-authors
- Xiangguo Zhao (23 shared papers)Guoren Wang (14 shared papers)Yongjiao Sun (13 shared papers)Zhibin Yao (6 shared papers)Lei Hu (6 shared papers)Xia‐Ting Feng (5 shared papers)Ye Yuan (7 shared papers)Wenjing Niu (4 shared papers)
- Journals
- SAE technical papers on CD-ROM/SAE technical paper series (8 papers)Neurocomputing (4 papers)Cognitive Computation (4 papers)World Wide Web (4 papers)IEEE Access (2 papers)
- Partner nations
- ChinaUnited States
In The Last Decade
Xin Bi
61 papers receiving 835 citations
Peers
Comparison fields: 5 of 95
- Artificial Intelligence 388
- Computer Vision and Pattern Recognition 153
- Industrial and Manufacturing Engineering 56
- Neurology 43
- Mechanics of Materials 113
Countries citing papers authored by Xin Bi
This map shows the geographic impact of Xin Bi'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 Xin Bi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xin Bi more than expected).
Fields of papers citing papers by Xin Bi
This network shows the impact of papers produced by Xin Bi. 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 Xin Bi. The network helps show where Xin Bi may publish in the future.
Co-authors
The 25 scholars most cited alongside Xin Bi, 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 66 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 70 | |
| 2 | 2022 | 61 | |
| 3 | 2022 | 56 | |
| 4 | 2021 | 53 | |
| 5 | 2011 | 53 | |
| 6 | 2020 | 37 | |
| 7 | 2021 | 37 | |
| 8 | 2014 | 37 | |
| 9 | 2019 | 35 | |
| 10 | 2009 | 32 | |
| 11 | 2022 | 24 | |
| 12 | 2023 | 22 | |
| 13 | 2017 | 19 | |
| 14 | 2016 | 17 | |
| 15 | 2023 | 15 | |
| 16 | 2015 | 15 | |
| 17 | 2021 | 14 | |
| 18 | 2021 | 14 | |
| 19 | 2024 | 14 | |
| 20 | 2015 | 14 |
About Xin Bi
Xin Bi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Aerospace Engineering, Computer Networks and Communications and Biomedical Engineering, having authored 66 papers that have together received 860 indexed citations. Recurring topics across this work include Machine Learning and ELM (13 papers), Domain Adaptation and Few-Shot Learning (10 papers), Advanced Graph Neural Networks (8 papers), Face and Expression Recognition (6 papers), Microwave Imaging and Scattering Analysis (5 papers), Rock Mechanics and Modeling (5 papers), Topic Modeling (5 papers) and Advanced SAR Imaging Techniques (5 papers). The work is most often cited by research in Artificial Intelligence (388 citations), Computer Vision and Pattern Recognition (153 citations), Industrial and Manufacturing Engineering (56 citations), Neurology (43 citations) and Mechanics of Materials (113 citations). Xin Bi has collaborated with scholars based in China and United States. Frequent co-authors include Xiangguo Zhao, Guoren Wang, Yongjiao Sun, Zhibin Yao, Lei Hu, Xia‐Ting Feng, Ye Yuan, Wenjing Niu, Deyang Chen and Hong Huang. Their work appears in journals such as SAE technical papers on CD-ROM/SAE technical paper series, Neurocomputing, Cognitive Computation, World Wide Web and IEEE Access.
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.