Wenling Shang
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Radiation
- Signal Processing
- Co-authors
- Kihyuk SohnHonglak LeeJustin ChiuManmohan ChandrakerYu XiangC. Lawrence ZitnickYuandong TianYuxin Wu
- Topics
- Domain Adaptation and Few-Shot Learning (3 papers)vaccines and immunoinformatics approaches (2 papers)Generative Adversarial Networks and Image Synthesis (2 papers)
- Partner nations
- United StatesChinaIsrael
In The Last Decade
Wenling Shang
9 papers receiving 185 citations
Peers
Comparison fields: 5 of 65
- Computer Vision and Pattern Recognition 97
- Artificial Intelligence 94
- Radiology, Nuclear Medicine and Imaging 26
- Radiation 20
- Signal Processing 17
Countries citing papers authored by Wenling Shang
This map shows the geographic impact of Wenling Shang'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 Wenling Shang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wenling Shang more than expected).
Fields of papers citing papers by Wenling Shang
This network shows the impact of papers produced by Wenling Shang. 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 Wenling Shang. The network helps show where Wenling Shang may publish in the future.
Co-authorship network of co-authors of Wenling Shang
This figure shows the co-authorship network connecting the top 25 collaborators of Wenling Shang. A scholar is included among the top collaborators of Wenling Shang 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 Wenling Shang. Wenling Shang 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 | 3 | |
| 3 | 7 | |
| 4 | 1 | |
| 5 | Unsupervised Domain Adaptation for Distance Metric Learning | 22 |
| 6 | ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games | 25 |
| 7 | Channel-Recurrent Variational Autoencoders. | 1 |
| 8 | 17 | |
| 9 | Improved Multimodal Deep Learning with Variation of Information | 94 |
| 10 | 19 |
About Wenling Shang
Wenling Shang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiation, having authored 10 papers that have together received 194 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (3 papers), vaccines and immunoinformatics approaches (2 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (97 citations), Artificial Intelligence (94 citations) and Radiation (20 citations). Wenling Shang has collaborated with scholars based in United States, China and Israel. Frequent co-authors include Kihyuk Sohn, Honglak Lee, Justin Chiu, Manmohan Chandraker, Yu Xiang, C. Lawrence Zitnick, Yuandong Tian, Yuxin Wu, Masoud Zarepisheh and Steve Jiang. Their work appears in journals such as Frontiers in Immunology, Physics in Medicine and Biology and Drug Design Development and Therapy.
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.