Kevin J Liang
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging
- Media Technology
- Biomedical Engineering
- Co-authors
- Lawrence CarinTal HassnerYin LiMostafa El‐KhamyWeituo HaoJianyi ZhangChangyou ChenPraveen Krishnan
- Topics
- Domain Adaptation and Few-Shot Learning (7 papers)Adversarial Robustness in Machine Learning (4 papers)Anomaly Detection Techniques and Applications (4 papers)
- Journals
- IEEE Access2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)arXiv (Cornell University)
- Partner nations
- United StatesCanadaIsrael
In The Last Decade
Kevin J Liang
14 papers receiving 203 citations
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 139
- Computer Vision and Pattern Recognition 93
- Radiology, Nuclear Medicine and Imaging 21
- Media Technology 17
- Biomedical Engineering 15
Countries citing papers authored by Kevin J Liang
This map shows the geographic impact of Kevin J Liang'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 Kevin J Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin J Liang more than expected).
Fields of papers citing papers by Kevin J Liang
This network shows the impact of papers produced by Kevin J Liang. 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 Kevin J Liang. The network helps show where Kevin J Liang may publish in the future.
Co-authorship network of co-authors of Kevin J Liang
This figure shows the co-authorship network connecting the top 25 collaborators of Kevin J Liang. A scholar is included among the top collaborators of Kevin J Liang 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 Kevin J Liang. Kevin J Liang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 38 | |
| 8 | 8 | |
| 9 | 36 | |
| 10 | 57 | |
| 11 | 24 | |
| 12 | 4 | |
| 13 | Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability | 1 |
| 14 | 6 | |
| 15 | 26 |
About Kevin J Liang
Kevin J Liang is a scholar working on Artificial Intelligence, Computer Science Applications and Computer Vision and Pattern Recognition, having authored 15 papers that have together received 209 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (7 papers), Adversarial Robustness in Machine Learning (4 papers) and Anomaly Detection Techniques and Applications (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (93 citations), Artificial Intelligence (139 citations) and Health Informatics (4 citations). Kevin J Liang has collaborated with scholars based in United States, Canada and Israel. Frequent co-authors include Lawrence Carin, Tal Hassner, Yin Li, Mostafa El‐Khamy, Weituo Hao, Jianyi Zhang, Changyou Chen, Praveen Krishnan, Xi Yin and Guan Pang. Their work appears in journals such as IEEE Access, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and arXiv (Cornell University).
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.