Lijun Wu
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Molecular Biology
- Electronic, Optical and Magnetic Materials
- Co-authors
- Tao QinTie‐Yan LiuYuan LiuYingce XiaFei TianJianhuang LaiJinhua ZhuShufang Xie
- Topics
- Natural Language Processing Techniques (24 papers)Topic Modeling (23 papers)Multimodal Machine Learning Applications (9 papers)
- Journals
- SHILAP Revista de lepidopterologíaBioinformaticsIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Lijun Wu
59 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 611
- Computer Vision and Pattern Recognition 257
- Electrical and Electronic Engineering 150
- Molecular Biology 116
- Electronic, Optical and Magnetic Materials 112
Countries citing papers authored by Lijun Wu
This map shows the geographic impact of Lijun Wu'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 Lijun Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lijun Wu more than expected).
Fields of papers citing papers by Lijun Wu
This network shows the impact of papers produced by Lijun Wu. 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 Lijun Wu. The network helps show where Lijun Wu may publish in the future.
Co-authorship network of co-authors of Lijun Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Lijun Wu. A scholar is included among the top collaborators of Lijun Wu 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 Lijun Wu. Lijun Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 4 | |
| 4 | 45 | |
| 5 | 20 | |
| 6 | 5 | |
| 7 | 5 | |
| 8 | 28 | |
| 9 | 7 | |
| 10 | 4 | |
| 11 | 1 | |
| 12 | 4 | |
| 13 | 49 | |
| 14 | 11 | |
| 15 | 14 | |
| 16 | 18 | |
| 17 | 2 | |
| 18 | 82 | |
| 19 | Adversarial Neural Machine Translation | 35 |
| 20 | Learning to Teach with Dynamic Loss Functions | 16 |
About Lijun Wu
Lijun Wu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 63 papers that have together received 1.2k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (24 papers), Topic Modeling (23 papers) and Multimodal Machine Learning Applications (9 papers). The work is most often cited by research in Artificial Intelligence (611 citations), Computer Vision and Pattern Recognition (257 citations) and Signal Processing (79 citations). Lijun Wu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Tao Qin, Tie‐Yan Liu, Yuan Liu, Yingce Xia, Fei Tian, Jianhuang Lai, Jinhua Zhu, Tie‐Yan Liu, Shufang Xie and Bo Shao. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and IEEE Transactions on Pattern Analysis and Machine 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.