Damai Dai
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Computer Networks and Communications
- Signal Processing
- Topics
- Topic Modeling (11 papers)Natural Language Processing Techniques (5 papers)Multimodal Machine Learning Applications (4 papers)
- Journals
- arXiv (Cornell University)Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)Proceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- ChinaIndiaUnited States
In The Last Decade
Damai Dai
17 papers receiving 514 citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 359
- Computer Vision and Pattern Recognition 125
- Information Systems 67
- Computer Networks and Communications 27
- Signal Processing 26
Countries citing papers authored by Damai Dai
This map shows the geographic impact of Damai 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 Damai Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Damai Dai more than expected).
Fields of papers citing papers by Damai Dai
This network shows the impact of papers produced by Damai 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 Damai Dai. The network helps show where Damai Dai may publish in the future.
Co-authorship network of co-authors of Damai Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Damai Dai. A scholar is included among the top collaborators of Damai Dai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Damai Dai. Damai Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelsbreakdown → | 51 |
| 5 | A Survey on In-context Learningbreakdown → | 173 |
| 6 | 69 | |
| 7 | 10 | |
| 8 | 4 | |
| 9 | 17 | |
| 10 | 14 | |
| 11 | 68 | |
| 12 | 3 | |
| 13 | 6 | |
| 14 | 3 | |
| 15 | 7 | |
| 16 | 29 | |
| 17 | Preliminary Study on the Construction of Chinese Medical Knowledge Graph | 36 |
| 18 | 32 |
About Damai Dai
Damai Dai is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Hardware and Architecture, having authored 18 papers that have together received 530 indexed citations. Recurring topics across this work include Topic Modeling (11 papers), Natural Language Processing Techniques (5 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Health Informatics (20 citations), Artificial Intelligence (359 citations) and Computer Vision and Pattern Recognition (125 citations). Damai Dai has collaborated with scholars based in China, India and United States. Frequent co-authors include Zhifang Sui, Baobao Chang, Furu Wei, Yaru Hao, Shuming Ma, Qingxiu Dong, Jingjing Xu, Xu Sun, Zhiyong Wu and Heming Xia. Their work appears in journals such as arXiv (Cornell University), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.