Daxiang Dong
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Computer Networks and Communications
- Signal Processing
- Co-authors
- Hua WuDianhai YuHaifeng WangWei HeWayne Xin ZhaoXiangyang ZhouYing ChenJing Liu
- Topics
- Topic Modeling (9 papers)Natural Language Processing Techniques (5 papers)Recommender Systems and Techniques (4 papers)
- Journals
- Concurrency and Computation Practice and ExperienceNorth American Chapter of the Association for Computational LinguisticsInternational Conference on Learning Representations
In The Last Decade
Daxiang Dong
15 papers receiving 944 citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 906
- Computer Vision and Pattern Recognition 282
- Information Systems 173
- Computer Networks and Communications 47
- Signal Processing 41
Countries citing papers authored by Daxiang Dong
This map shows the geographic impact of Daxiang Dong'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 Daxiang Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daxiang Dong more than expected).
Fields of papers citing papers by Daxiang Dong
This network shows the impact of papers produced by Daxiang Dong. 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 Daxiang Dong. The network helps show where Daxiang Dong may publish in the future.
Co-authorship network of co-authors of Daxiang Dong
This figure shows the co-authorship network connecting the top 25 collaborators of Daxiang Dong. A scholar is included among the top collaborators of Daxiang Dong 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 Daxiang Dong. Daxiang Dong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 2 | |
| 3 | 8 | |
| 4 | 5 | |
| 5 | RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answeringbreakdown → | 204 |
| 6 | 13 | |
| 7 | 51 | |
| 8 | A New Method of Region Embedding for Text Classification | 31 |
| 9 | 184 | |
| 10 | 130 | |
| 11 | Multi-Task Learning for Multiple Language Translationbreakdown → | 316 |
| 12 | 7 | |
| 13 | Compound Embedding Features for Semi-supervised Learning | 17 |
| 14 | 15 | |
| 15 | 24 |
About Daxiang Dong
Daxiang Dong is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 15 papers that have together received 1.0k indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (5 papers) and Recommender Systems and Techniques (4 papers). The work is most often cited by research in Artificial Intelligence (906 citations), Computer Vision and Pattern Recognition (282 citations) and Information Systems (173 citations). Daxiang Dong has collaborated with scholars based in China, Hong Kong and France. Frequent co-authors include Hua Wu, Dianhai Yu, Haifeng Wang, Wei He, Wayne Xin Zhao, Xiangyang Zhou, Ying Chen, Jing Liu, Kai Liu and Li Lü. Their work appears in journals such as Concurrency and Computation Practice and Experience, North American Chapter of the Association for Computational Linguistics and International Conference on Learning Representations.
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.