Yi-Ren Yeh
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Signal Processing top 5%
- Computer Networks and Communications top 10%
- Media Technology top 5%
- Co-authors
- Yu-Chiang Frank WangYao-Hung Hubert TsaiYuh‐Jye LeeChun-Hao P. HuangYu-Wei ChaoChia-Po WeiShinto EguchiSu‐Yun Huang
- Topics
- Domain Adaptation and Few-Shot Learning (12 papers)Multimodal Machine Learning Applications (10 papers)Face and Expression Recognition (8 papers)
- Partner nations
- TaiwanUnited StatesGermany
In The Last Decade
Yi-Ren Yeh
33 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 642
- Computer Vision and Pattern Recognition 549
- Signal Processing 147
- Computer Networks and Communications 118
- Media Technology 104
Countries citing papers authored by Yi-Ren Yeh
This map shows the geographic impact of Yi-Ren Yeh'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 Yi-Ren Yeh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yi-Ren Yeh more than expected).
Fields of papers citing papers by Yi-Ren Yeh
This network shows the impact of papers produced by Yi-Ren Yeh. 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 Yi-Ren Yeh. The network helps show where Yi-Ren Yeh may publish in the future.
Co-authorship network of co-authors of Yi-Ren Yeh
This figure shows the co-authorship network connecting the top 25 collaborators of Yi-Ren Yeh. A scholar is included among the top collaborators of Yi-Ren Yeh 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 Yi-Ren Yeh. Yi-Ren Yeh 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 | 3 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 95 | |
| 9 | 145 | |
| 10 | 3 | |
| 11 | 6 | |
| 12 | 2 | |
| 13 | 23 | |
| 14 | A Review of Reduced Kernel Trick in Machine Learning | 2 |
| 15 | Solving Nonlinear SVM in Linear Time? A Nystrom Approximated SVM with Applications to Image Classification ∗ | 0 |
| 16 | 2 | |
| 17 | 82 | |
| 18 | 14 | |
| 19 | 63 | |
| 20 | 56 |
About Yi-Ren Yeh
Yi-Ren Yeh is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 39 papers that have together received 1.1k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (12 papers), Multimodal Machine Learning Applications (10 papers) and Face and Expression Recognition (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (549 citations), Artificial Intelligence (642 citations) and Signal Processing (147 citations). Yi-Ren Yeh has collaborated with scholars based in Taiwan, United States and Germany. Frequent co-authors include Yu-Chiang Frank Wang, Yao-Hung Hubert Tsai, Yuh‐Jye Lee, Chun-Hao P. Huang, Yu-Wei Chao, Chia-Po Wei, Shinto Eguchi, Su‐Yun Huang, Wei-Cheng Lin and Yu‐Wen Chen. Their work appears in journals such as PLoS ONE, IEEE Transactions on Image Processing and Pattern Recognition.
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.