Jiangchao Yao
- Organic Chemistry top 5%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Molecular Biology
- Information Systems top 5%
- Topics
- Domain Adaptation and Few-Shot Learning (10 papers)Machine Learning and Data Classification (8 papers)Topic Modeling (7 papers)
- Journals
- Journal of the American Chemical SocietyIEEE Transactions on Pattern Analysis and Machine IntelligenceJournal of Medicinal Chemistry
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Jiangchao Yao
51 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 114
- Organic Chemistry 548
- Artificial Intelligence 424
- Computer Vision and Pattern Recognition 210
- Molecular Biology 201
- Information Systems 170
Countries citing papers authored by Jiangchao Yao
This map shows the geographic impact of Jiangchao Yao'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 Jiangchao Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiangchao Yao more than expected).
Fields of papers citing papers by Jiangchao Yao
This network shows the impact of papers produced by Jiangchao Yao. 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 Jiangchao Yao. The network helps show where Jiangchao Yao may publish in the future.
Co-authorship network of co-authors of Jiangchao Yao
This figure shows the co-authorship network connecting the top 25 collaborators of Jiangchao Yao. A scholar is included among the top collaborators of Jiangchao Yao 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 Jiangchao Yao. Jiangchao Yao 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 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 0 | |
| 9 | 2 | |
| 10 | 1 | |
| 11 | 0 | |
| 12 | 1 | |
| 13 | 8 | |
| 14 | 3 | |
| 15 | 8 | |
| 16 | Contrastive Conditional Transport for Representation Learning. | 1 |
| 17 | How Does Disagreement Benefit Co-teaching? | 13 |
| 18 | 27 | |
| 19 | Masking: A New Perspective of Noisy Supervision | 33 |
| 20 | Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels | 8 |
About Jiangchao Yao
Jiangchao Yao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 64 papers that have together received 1.3k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (10 papers), Machine Learning and Data Classification (8 papers) and Topic Modeling (7 papers). The work is most often cited by research in Organic Chemistry (548 citations), Artificial Intelligence (424 citations) and Computer Vision and Pattern Recognition (210 citations). Jiangchao Yao has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Dawei Ma, Yongda Zhang, Fenggang Tao, Shi‐Hui Wu, Ya Zhang, Ivor W. Tsang, Hongxia Yang, Jingren Zhou, Alan R. Katritzky and Jun Sun. Their work appears in journals such as Journal of the American Chemical Society, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Medicinal Chemistry.
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.