Wenyuan Dai
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 1%
- Information Systems top 2%
- Electrical and Electronic Engineering
- Signal Processing top 5%
- Topics
- Text and Document Classification Technologies (8 papers)Domain Adaptation and Few-Shot Learning (7 papers)Topic Modeling (6 papers)
In The Last Decade
Wenyuan Dai
29 papers receiving 2.8k citations
Hit Papers
Peers
Comparison fields: 5 of 158
- Artificial Intelligence 2.0k
- Computer Vision and Pattern Recognition 822
- Information Systems 370
- Electrical and Electronic Engineering 164
- Signal Processing 155
Countries citing papers authored by Wenyuan Dai
This map shows the geographic impact of Wenyuan Dai'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 Wenyuan Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wenyuan Dai more than expected).
Fields of papers citing papers by Wenyuan Dai
This network shows the impact of papers produced by Wenyuan Dai. 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 Wenyuan Dai. The network helps show where Wenyuan Dai may publish in the future.
Co-authorship network of co-authors of Wenyuan Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Wenyuan Dai. A scholar is included among the top collaborators of Wenyuan Dai 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 Wenyuan Dai. Wenyuan Dai 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 | 3 | |
| 3 | 4 | |
| 4 | 51 | |
| 5 | Foundations of Transfer Learning | 1 |
| 6 | 21 | |
| 7 | 53 | |
| 8 | Privacy-preserving Transfer Learning for Knowledge Sharing. | 3 |
| 9 | 16 | |
| 10 | 14 | |
| 11 | 4 | |
| 12 | 38 | |
| 13 | 76 | |
| 14 | 65 | |
| 15 | Can Chinese web pages be classified with english data | 1 |
| 16 | Translated Learning: Transfer Learning across Different Feature Spaces | 190 |
| 17 | 131 | |
| 18 | Transferring naive bayes classifiers for text classification | 210 |
| 19 | 231 | |
| 20 | Boosting for transfer learningbreakdown → | 1191 |
About Wenyuan Dai
Wenyuan Dai is a scholar working on Computational Mathematics, Artificial Intelligence and Transportation, having authored 30 papers that have together received 2.9k indexed citations. Recurring topics across this work include Text and Document Classification Technologies (8 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Topic Modeling (6 papers). The work is most often cited by research in Artificial Intelligence (2.0k citations), Computer Vision and Pattern Recognition (822 citations) and Computational Mathematics (11 citations). Wenyuan Dai has collaborated with scholars based in China, Hong Kong and Singapore. Frequent co-authors include Qiang Yang, Gui-Rong Xue, Yong Yu, Yuqiang Chen, Sinno Jialin Pan, Yu Zhang, Ling Xiao, Quanming Yao, Mingxuan Yuan and Yongqi Zhang. Their work appears in journals such as Advanced Science, Proceedings of the VLDB Endowment and ACS Applied Nano Materials.
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.