Yunyun Wang
- Water Science and Technology top 10%
- Environmental Engineering top 10%
-
- Face and Expression Recognition 11
- Multimodal Machine Learning Applications 5
- Human Pose and Action Recognition 3
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning 14
- Machine Learning and ELM 6
- Text and Document Classification Technologies 4
- Machine Learning and Data Classification 3
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- Speech and Audio Processing 3
- Cited by
- Water Science and TechnologyEnvironmental EngineeringComputer Vision and Pattern Recognition
- Journals
- Frontiers of Computer Science (4 papers)Neurocomputing (4 papers)Neural Networks (2 papers)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Yunyun Wang
29 papers receiving 443 citations
Peers
Comparison fields: 5 of 83
- Water Science and Technology 152
- Environmental Engineering 153
- Computer Vision and Pattern Recognition 130
- Artificial Intelligence 200
- Industrial and Manufacturing Engineering 52
Countries citing papers authored by Yunyun Wang
This map shows the geographic impact of Yunyun Wang'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 Yunyun Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yunyun Wang more than expected).
Fields of papers citing papers by Yunyun Wang
This network shows the impact of papers produced by Yunyun Wang. 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 Yunyun Wang. The network helps show where Yunyun Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yunyun Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 3 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 2 | |
| 5 | 2023 | 3 | |
| 6 | 2023 | 2 | |
| 7 | 2023 | 2 | |
| 8 | 2022 | 10 | |
| 9 | 2022 | 1 | |
| 10 | 2022 | 1 | |
| 11 | 2022 | 3 | |
| 12 | 2021 | 9 | |
| 13 | 2020 | 0 | |
| 14 | 2020 | 5 | |
| 15 | 2019 | 7 | |
| 16 | 2019 | 5 | |
| 17 | 2018 | 97 | |
| 18 | 2018 | 16 | |
| 19 | 2017 | 11 | |
| 20 | 2012 | 7 |
About Yunyun Wang
Yunyun Wang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Statistics and Probability and Environmental Engineering, having authored 32 papers that have together received 455 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (14 papers), Face and Expression Recognition (11 papers), Machine Learning and ELM (6 papers), Multimodal Machine Learning Applications (5 papers), Text and Document Classification Technologies (4 papers), Speech and Audio Processing (3 papers), Human Pose and Action Recognition (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Water Science and Technology (152 citations), Environmental Engineering (153 citations), Computer Vision and Pattern Recognition (130 citations), Artificial Intelligence (200 citations) and Industrial and Manufacturing Engineering (52 citations). Yunyun Wang has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Songcan Chen, Jian Zhou, Kejia Chen, Zhihua Zhou, Linfeng Liu, Yuanyuan Wang, Yuanyuan Wang, Lijuan Sun, Fu Xiao and Hui Xue. Their work appears in journals such as Frontiers of Computer Science, Neurocomputing, Neural Networks, IEEE Transactions on Neural Networks and Learning Systems and Neural Processing Letters.
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.