Yuanmeng Yan
- Artificial Intelligence top 2%
- Topic Modeling 17
- Natural Language Processing Techniques 12
- Adversarial Robustness in Machine Learning 5
- Speech and dialogue systems 4
- Domain Adaptation and Few-Shot Learning 4
- Speech Recognition and Synthesis 3
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- Multimodal Machine Learning Applications 6
- Information Systems top 10%
- Software Engineering Research 2
- Co-authors
- Weiran XuRumei LiWei WuFuzheng ZhangSirui WangKeqing HeHong XuZijun Liu
- Journals
- IEEE Access (1 paper)Neurocomputing (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2 papers)
- Partner nations
- ChinaUnited States
In The Last Decade
Yuanmeng Yan
23 papers receiving 541 citations
Hit Papers
Peers
Comparison fields: 5 of 54
- Artificial Intelligence 511
- Computer Vision and Pattern Recognition 118
- Information Systems 61
- Signal Processing 23
- Computer Networks and Communications 26
Countries citing papers authored by Yuanmeng Yan
This map shows the geographic impact of Yuanmeng Yan'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 Yuanmeng Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuanmeng Yan more than expected).
Fields of papers citing papers by Yuanmeng Yan
This network shows the impact of papers produced by Yuanmeng Yan. 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 Yuanmeng Yan. The network helps show where Yuanmeng Yan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yuanmeng Yan, 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 | 2023 | 1 | |
| 2 | 2022 | 1 | |
| 3 | 2022 | 11 | |
| 4 | ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transferbreakdown → | 2021 | 303 |
| 5 | 2021 | 18 | |
| 6 | 2021 | 8 | |
| 7 | 2021 | 5 | |
| 8 | 2021 | 36 | |
| 9 | 2021 | 4 | |
| 10 | 2021 | 2 | |
| 11 | 2021 | 7 | |
| 12 | 2021 | 19 | |
| 13 | 2021 | 14 | |
| 14 | 2021 | 9 | |
| 15 | 2020 | 13 | |
| 16 | 2020 | 7 | |
| 17 | 2020 | 28 | |
| 18 | 2020 | 4 | |
| 19 | 2020 | 4 | |
| 20 | 2020 | 15 |
About Yuanmeng Yan
Yuanmeng Yan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Safety Research, having authored 23 papers that have together received 556 indexed citations. Recurring topics across this work include Topic Modeling (17 papers), Natural Language Processing Techniques (12 papers), Multimodal Machine Learning Applications (6 papers), Adversarial Robustness in Machine Learning (5 papers), Speech and dialogue systems (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Speech Recognition and Synthesis (3 papers) and Software Engineering Research (2 papers). The work is most often cited by research in Artificial Intelligence (511 citations), Computer Vision and Pattern Recognition (118 citations) and Information Systems (61 citations). Yuanmeng Yan has collaborated with scholars based in China and United States. Frequent co-authors include Weiran Xu, Rumei Li, Wei Wu, Fuzheng Zhang, Sirui Wang, Keqing He, Hong Xu, Zijun Liu, Sihong Liu and Jie Zhou. Their work appears in journals such as IEEE Access, Neurocomputing and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.