Yue Yang
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Control and Systems Engineering
- Computer Networks and Communications
- Computational Theory and Mathematics top 10%
- Co-authors
- Shaocheng TongQihe ShanTieshan LiC. L. Philip ChenChris Callison-BurchMark YatskarDaniel JinHuichao Yang
- Topics
- Topic Modeling (5 papers)Multimodal Machine Learning Applications (4 papers)Advanced Multi-Objective Optimization Algorithms (4 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionComputational Theory and Mathematics
- Journals
- SHILAP Revista de lepidopterologíaChemical Engineering JournalInformation Sciences
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Yue Yang
39 papers receiving 400 citations
Peers
Comparison fields: 5 of 108
- Artificial Intelligence 141
- Computer Vision and Pattern Recognition 77
- Control and Systems Engineering 59
- Computer Networks and Communications 58
- Computational Theory and Mathematics 53
Countries citing papers authored by Yue Yang
This map shows the geographic impact of Yue Yang'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 Yue Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yue Yang more than expected).
Fields of papers citing papers by Yue Yang
This network shows the impact of papers produced by Yue Yang. 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 Yue Yang. The network helps show where Yue Yang may publish in the future.
Co-authorship network of co-authors of Yue Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Yue Yang. A scholar is included among the top collaborators of Yue Yang 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 Yue Yang. Yue Yang 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 | 5 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 10 | |
| 8 | 16 | |
| 9 | 17 | |
| 10 | 0 | |
| 11 | 10 | |
| 12 | 1 | |
| 13 | 0 | |
| 14 | 7 | |
| 15 | 26 | |
| 16 | 72 | |
| 17 | 16 | |
| 18 | Application of quantitative descriptive analysis (QDA) method in sensory evaluation of tea infusion taste. | 1 |
| 19 | 16 | |
| 20 | Secure and efficient group blind signature scheme | 1 |
About Yue Yang
Yue Yang is a scholar working on Human-Computer Interaction, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 49 papers that have together received 408 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Multimodal Machine Learning Applications (4 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). The work is most often cited by research in Artificial Intelligence (141 citations), Computer Vision and Pattern Recognition (77 citations) and Computational Theory and Mathematics (53 citations). Yue Yang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Shaocheng Tong, Qihe Shan, Tieshan Li, C. L. Philip Chen, Chris Callison-Burch, Mark Yatskar, Daniel Jin, Huichao Yang, Yongjie Ma and C. T. Chong. Their work appears in journals such as SHILAP Revista de lepidopterología, Chemical Engineering Journal and Information Sciences.
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.