Zheyan Shen
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Statistics and Probability top 10%
- Information Systems
- Computer Networks and Communications
- Topics
- Domain Adaptation and Few-Shot Learning (7 papers)Gaussian Processes and Bayesian Inference (3 papers)Machine Learning and ELM (2 papers)
- Journals
- Pattern RecognitionIEEE Transactions on Knowledge and Data Engineering2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Zheyan Shen
16 papers receiving 485 citations
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 359
- Computer Vision and Pattern Recognition 187
- Statistics and Probability 30
- Information Systems 24
- Computer Networks and Communications 23
Countries citing papers authored by Zheyan Shen
This map shows the geographic impact of Zheyan Shen'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 Zheyan Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zheyan Shen more than expected).
Fields of papers citing papers by Zheyan Shen
This network shows the impact of papers produced by Zheyan Shen. 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 Zheyan Shen. The network helps show where Zheyan Shen may publish in the future.
Co-authorship network of co-authors of Zheyan Shen
This figure shows the co-authorship network connecting the top 25 collaborators of Zheyan Shen. A scholar is included among the top collaborators of Zheyan Shen 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 Zheyan Shen. Zheyan Shen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 24 | |
| 3 | 24 | |
| 4 | 2 | |
| 5 | 6 | |
| 6 | 4 | |
| 7 | Integrated Latent Heterogeneity and Invariance Learning in Kernel Space | 0 |
| 8 | 13 | |
| 9 | 16 | |
| 10 | 163 | |
| 11 | Counterfactual Prediction for Bundle Treatment | 13 |
| 12 | 29 | |
| 13 | 74 | |
| 14 | 23 | |
| 15 | 97 | |
| 16 | NICO: A Dataset Towards Non-I.I.D. Image Classification. | 3 |
| 17 | On Image Classification: Correlation v.s. Causality. | 3 |
About Zheyan Shen
Zheyan Shen is a scholar working on Statistics and Probability, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 17 papers that have together received 499 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (7 papers), Gaussian Processes and Bayesian Inference (3 papers) and Machine Learning and ELM (2 papers). The work is most often cited by research in Artificial Intelligence (359 citations), Computer Vision and Pattern Recognition (187 citations) and Statistics and Probability (30 citations). Zheyan Shen has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Peng Cui, Yue He, Renzhe Xu, Xingxuan Zhang, Linjun Zhou, Tong Zhang, Yue He, Jiashuo Liu, Bo Li and Zhitang Chen. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Knowledge and Data Engineering and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.