Yi Dai
Impact in
-
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
-
- Multimodal Machine Learning Applications
Papers in
-
- Topic Modeling 5
- Sentiment Analysis and Opinion Mining 2
- Domain Adaptation and Few-Shot Learning 2
-
- Multimodal Machine Learning Applications 3
- Co-authors
- Xu Han (1 shared paper)Maosong Sun (1 shared paper)Zhiyuan Liu (1 shared paper)Tianyu Gao (1 shared paper)Yankai Lin (1 shared paper)Jie Zhou (1 shared paper)Peng Li (1 shared paper)Ling Feng (4 shared papers)
- Journals
- Artificial Intelligence in Medicine (1 paper)Scientific Reports (1 paper)International Journal of Computational Intelligence Systems (1 paper)Journal of Affective Disorders (1 paper)Bioinformatics (1 paper)
- Partner nations
- ChinaUnited StatesThailand
In The Last Decade
Yi Dai
25 papers receiving 162 citations
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 71
- Computer Vision and Pattern Recognition 39
- 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 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 57 | |
| 2 | 2020 | 27 | |
| 3 | 2008 | 19 | |
| 4 | 2021 | 11 | |
| 5 | 2021 | 8 | |
| 6 | 2022 | 5 | |
| 7 | 2022 | 4 | |
| 8 | 2024 | 4 | |
| 9 | 2022 | 4 | |
| 10 | 2022 | 3 | |
| 11 | 2023 | 3 | |
| 12 | 2023 | 3 | |
| 13 | 2024 | 3 | |
| 14 | 2023 | 3 | |
| 15 | 2025 | 2 | |
| 16 | 2019 | 2 | |
| 17 | 2023 | 2 | |
| 18 | 2021 | 2 | |
| 19 | 2023 | 1 | |
| 20 | 2023 | 1 |
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 34 papers that have together received 169 indexed citations. Recurring topics across this work include Mental Health via Writing (5 papers), Topic Modeling (5 papers), Emotion and Mood Recognition (3 papers), Multimodal Machine Learning Applications (3 papers), Sentiment Analysis and Opinion Mining (2 papers), IoT and Edge/Fog Computing (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Cloud Computing and Resource Management (2 papers). The work is most often cited by research in Artificial Intelligence (71 citations), Computer Vision and Pattern Recognition (39 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 Thailand. Frequent co-authors include Xu Han, Maosong Sun, Zhiyuan Liu, Tianyu Gao, Yankai Lin, Jie Zhou, Peng Li, Ling Feng, Mingzhe Liu and Shilu Zhang. Their work appears in journals such as Artificial Intelligence in Medicine, Scientific Reports, International Journal of Computational Intelligence Systems, Journal of Affective Disorders and Bioinformatics.
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.