Yi Dai
Impact in
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- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
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- Multimodal Machine Learning Applications
Papers in
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- Topic Modeling 5
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- Multimodal Machine Learning Applications 3
- Human Pose and Action Recognition 2
- Co-authors
- Peng Li (1 shared paper)Yankai Lin (1 shared paper)Xu Han (1 shared paper)Jie Zhou (1 shared paper)Zhiyuan Liu (1 shared paper)Maosong Sun (1 shared paper)Tianyu Gao (1 shared paper)Ling Feng (4 shared papers)
- Journals
- Journal of Computational Biology (1 paper)Journal of Pain Research (1 paper)Frontiers in Environmental Science (1 paper)Journal of Affective Disorders (1 paper)Asian-Pacific Journal of Second and Foreign Language Education (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Yi Dai
27 papers receiving 176 citations
Peers
Comparison fields: 5 of 69
- Artificial Intelligence 78
- Computer Vision and Pattern Recognition 40
- Health, Toxicology and Mutagenesis 20
- Pollution 13
- Physiology 3
Countries citing papers authored by Yi Dai
This map shows the geographic impact of Yi Dai'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 Yi Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yi Dai more than expected).
Fields of papers citing papers by Yi Dai
This network shows the impact of papers produced by Yi Dai. 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 Yi Dai. The network helps show where Yi Dai may publish in the future.
Co-authors
The 25 scholars most cited alongside Yi Dai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 59 | |
| 2 | 2020 | 27 | |
| 3 | 2008 | 19 | |
| 4 | 2021 | 11 | |
| 5 | 2021 | 8 | |
| 6 | 2024 | 6 | |
| 7 | 2023 | 5 | |
| 8 | 2024 | 5 | |
| 9 | 2022 | 5 | |
| 10 | 2022 | 4 | |
| 11 | 2022 | 4 | |
| 12 | 2022 | 3 | |
| 13 | 2024 | 3 | |
| 14 | 2023 | 3 | |
| 15 | 2023 | 3 | |
| 16 | 2025 | 3 | |
| 17 | 2019 | 2 | |
| 18 | 2023 | 2 | |
| 19 | 2021 | 2 | |
| 20 | 2023 | 2 |
About Yi Dai
Yi Dai is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Computer Networks and Communications and Social Psychology, having authored 36 papers that have together received 184 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Mental Health via Writing (5 papers), Multimodal Machine Learning Applications (3 papers), Emotion and Mood Recognition (3 papers), Endometriosis Research and Treatment (2 papers), Blockchain Technology Applications and Security (2 papers), Human Pose and Action Recognition (2 papers) and Single-cell and spatial transcriptomics (2 papers). The work is most often cited by research in Artificial Intelligence (78 citations), Computer Vision and Pattern Recognition (40 citations), Health, Toxicology and Mutagenesis (20 citations), Pollution (13 citations) and Physiology (3 citations). Yi Dai has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Peng Li, Yankai Lin, Xu Han, Jie Zhou, Zhiyuan Liu, Maosong Sun, Tianyu Gao, Ling Feng, Mingzhe Liu and Yang Luo. Their work appears in journals such as Journal of Computational Biology, Journal of Pain Research, Frontiers in Environmental Science, Journal of Affective Disorders and Asian-Pacific Journal of Second and Foreign Language Education.
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.