Xisen Jin
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Management Science and Operations Research top 10%
- Information Systems top 10%
- Statistical and Nonlinear Physics
- Topics
- Topic Modeling (5 papers)Natural Language Processing Techniques (4 papers)Speech and dialogue systems (2 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionManagement Science and Operations Research
- Journals
- National University of SingaporearXiv (Cornell University)Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Partner nations
- United StatesSingaporeCanada
In The Last Decade
Xisen Jin
7 papers receiving 415 citations
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 400
- Computer Vision and Pattern Recognition 89
- Management Science and Operations Research 50
- Information Systems 48
- Statistical and Nonlinear Physics 13
Countries citing papers authored by Xisen Jin
This map shows the geographic impact of Xisen Jin'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 Xisen Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xisen Jin more than expected).
Fields of papers citing papers by Xisen Jin
This network shows the impact of papers produced by Xisen Jin. 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 Xisen Jin. The network helps show where Xisen Jin may publish in the future.
Co-authorship network of co-authors of Xisen Jin
This figure shows the co-authorship network connecting the top 25 collaborators of Xisen Jin. A scholar is included among the top collaborators of Xisen Jin 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 Xisen Jin. Xisen Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 18 | |
| 3 | Refining Neural Networks with Compositional Explanations. | 3 |
| 4 | 27 | |
| 5 | 187 | |
| 6 | 5 | |
| 7 | 184 |
About Xisen Jin
Xisen Jin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology, having authored 7 papers that have together received 427 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Natural Language Processing Techniques (4 papers) and Speech and dialogue systems (2 papers). The work is most often cited by research in Artificial Intelligence (400 citations), Computer Vision and Pattern Recognition (89 citations) and Management Science and Operations Research (50 citations). Xisen Jin has collaborated with scholars based in United States, Singapore and Canada. Frequent co-authors include Xiang Ren, Woojeong Jin, Meng Qu, Wenqiang Lei, Zhaochun Ren, Min‐Yen Kan, Xiangnan He, Dawei Yin, Brendan Kennedy and Francesco Barbieri. Their work appears in journals such as National University of Singapore, arXiv (Cornell University) and Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
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.