Yuanmeng Yan

940 citations
23 papers · 556 indexed · 1 hit paper · h-index 10
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

ConSERT: A Contrastive Framework for Self-Supervised Sent...3032021202620222024100200300

Peers

Yuanmeng Yan
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
Replace Ulrich Germann with:
Ulrich Germann United Kingdom
Muyun Yang China
Daniel Beck Australia
Kazuma Hashimoto Japan
Ferhan Türe United States
Jingzhou Liu United States
Panupong Pasupat United States
Gregory Druck United States
Yinhan Liu United States
Yuanmeng Yan relative to Ulrich Germann United Kingdom Ulrich Germann's profile →
Citations per field
00.5×10×20×30×35×
Ulrich Germann · 1×
Citations per year

Countries citing papers authored by Yuanmeng Yan

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Yuanmeng Yan Line = papers co-authored together Yuanmeng Yan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20231
2 20221
3 202211
4
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transferbreakdown →
2021303
5 202118
6 20218
7 20215
8 202136
9 20214
10 20212
11 20217
12 202119
13 202114
14 20219
15 202013
16 20207
17 202028
18 20204
19 20204
20 202015

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.

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