Guo-Xun Yuan
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Computational Mechanics top 10%
- Numerical Analysis top 10%
- Molecular Biology
- Co-authors
- Chih‐Jen LinChia-Hua HoCho‐Jui HsiehKai-Wei ChangKwan‐Liu MaYingcai WuChih‐Hsing ChuYong Zhuang
- Topics
- Sparse and Compressive Sensing Techniques (4 papers)Face and Expression Recognition (3 papers)Manufacturing Process and Optimization (2 papers)
- Journals
- Proceedings of the IEEEJournal of Machine Learning ResearchIEEE Transactions on Visualization and Computer Graphics
- Partner nations
- TaiwanUnited States
In The Last Decade
Guo-Xun Yuan
9 papers receiving 499 citations
Peers
Comparison fields: 5 of 97
- Artificial Intelligence 282
- Computer Vision and Pattern Recognition 205
- Computational Mechanics 134
- Numerical Analysis 51
- Molecular Biology 50
Countries citing papers authored by Guo-Xun Yuan
This map shows the geographic impact of Guo-Xun Yuan'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 Guo-Xun Yuan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guo-Xun Yuan more than expected).
Fields of papers citing papers by Guo-Xun Yuan
This network shows the impact of papers produced by Guo-Xun Yuan. 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 Guo-Xun Yuan. The network helps show where Guo-Xun Yuan may publish in the future.
Co-authorship network of co-authors of Guo-Xun Yuan
This figure shows the co-authorship network connecting the top 25 collaborators of Guo-Xun Yuan. A scholar is included among the top collaborators of Guo-Xun Yuan 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 Guo-Xun Yuan. Guo-Xun Yuan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | An improved GLMNET for L1-regularized logistic regression | 80 |
| 3 | 38 | |
| 4 | 5 | |
| 5 | 181 | |
| 6 | 66 | |
| 7 | A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification | 130 |
| 8 | 7 | |
| 9 | 1 |
About Guo-Xun Yuan
Guo-Xun Yuan is a scholar working on Computational Mechanics, Mathematical Physics and Industrial and Manufacturing Engineering, having authored 9 papers that have together received 516 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (4 papers), Face and Expression Recognition (3 papers) and Manufacturing Process and Optimization (2 papers). The work is most often cited by research in Computational Mathematics (6 citations), Computer Vision and Pattern Recognition (205 citations) and Numerical Analysis (51 citations). Guo-Xun Yuan has collaborated with scholars based in Taiwan and United States. Frequent co-authors include Chih‐Jen Lin, Chia-Hua Ho, Cho‐Jui Hsieh, Kai-Wei Chang, Kwan‐Liu Ma, Yingcai Wu, Chih‐Hsing Chu, Yong Zhuang and Yu-Chin Juan. Their work appears in journals such as Proceedings of the IEEE, Journal of Machine Learning Research and IEEE Transactions on Visualization and Computer Graphics.
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.