Yu-Feng Li
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 1%
- Information Systems top 5%
- Signal Processing top 5%
- Condensed Matter Physics top 10%
- Co-authors
- Zhi‐Hua ZhouYuyin SunJames T. KwokSheng-Jun HuangMin-Ling ZhangIvor W. TsangXiyu ZhuHai‐Hu Wen
- Topics
- Machine Learning and Data Classification (28 papers)Text and Document Classification Technologies (24 papers)Domain Adaptation and Few-Shot Learning (14 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Yu-Feng Li
73 papers receiving 2.1k citations
Peers
Comparison fields: 5 of 116
- Artificial Intelligence 1.4k
- Computer Vision and Pattern Recognition 955
- Information Systems 159
- Signal Processing 128
- Condensed Matter Physics 124
Countries citing papers authored by Yu-Feng Li
This map shows the geographic impact of Yu-Feng Li'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 Yu-Feng Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu-Feng Li more than expected).
Fields of papers citing papers by Yu-Feng Li
This network shows the impact of papers produced by Yu-Feng Li. 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 Yu-Feng Li. The network helps show where Yu-Feng Li may publish in the future.
Co-authorship network of co-authors of Yu-Feng Li
This figure shows the co-authorship network connecting the top 25 collaborators of Yu-Feng Li. A scholar is included among the top collaborators of Yu-Feng Li 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 Yu-Feng Li. Yu-Feng Li 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 | 1 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 10 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 8 | |
| 9 | STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data | 8 |
| 10 | 27 | |
| 11 | Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data | 51 |
| 12 | 43 | |
| 13 | 27 | |
| 14 | 63 | |
| 15 | Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison | 121 |
| 16 | 6 | |
| 17 | 35 | |
| 18 | Towards Making Unlabeled Data Never Hurt | 58 |
| 19 | S4VM: Safe Semi-Supervised Support Vector Machine | 8 |
| 20 | Tighter and convex maximum margin clustering | 84 |
About Yu-Feng Li
Yu-Feng Li is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 78 papers that have together received 2.2k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (28 papers), Text and Document Classification Technologies (24 papers) and Domain Adaptation and Few-Shot Learning (14 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (955 citations), Artificial Intelligence (1.4k citations) and Signal Processing (128 citations). Yu-Feng Li has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Zhi‐Hua Zhou, Yuyin Sun, James T. Kwok, Sheng-Jun Huang, Min-Ling Zhang, Ivor W. Tsang, Xiyu Zhu, Hai‐Hu Wen, Mehrdad Mahdavi and Rong Jin. Their work appears in journals such as Physical Review B, Scientific Reports and BMC Public Health.
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.