Hua Wu
- Artificial Intelligence top 0.1%
- Computer Vision and Pattern Recognition top 0.5%
- Information Systems top 1%
- Molecular Biology
- Computational Theory and Mathematics top 2%
- Co-authors
- Haifeng WangHao TianYu SunDaxiang DongXinyan XiaoDianhai YuWei HeZhongjun He
- Topics
- Topic Modeling (103 papers)Natural Language Processing Techniques (92 papers)Multimodal Machine Learning Applications (25 papers)
- Partner nations
- ChinaJapanUnited States
In The Last Decade
Hua Wu
142 papers receiving 5.3k citations
Hit Papers
Peers
Comparison fields: 5 of 157
- Artificial Intelligence 4.6k
- Computer Vision and Pattern Recognition 1.4k
- Information Systems 481
- Molecular Biology 405
- Computational Theory and Mathematics 344
Countries citing papers authored by Hua Wu
This map shows the geographic impact of Hua 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 Hua Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hua Wu more than expected).
Fields of papers citing papers by Hua Wu
This network shows the impact of papers produced by Hua 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 Hua Wu. The network helps show where Hua Wu may publish in the future.
Co-authorship network of co-authors of Hua Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Hua Wu. A scholar is included among the top collaborators of Hua 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 Hua Wu. Hua 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 | 1 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 4 | |
| 5 | Geometry-enhanced molecular representation learning for property predictionbreakdown → | 337 |
| 6 | 6 | |
| 7 | 37 | |
| 8 | Unified Structure Generation for Universal Information Extractionbreakdown → | 215 |
| 9 | 17 | |
| 10 | 99 | |
| 11 | 159 | |
| 12 | 28 | |
| 13 | 16 | |
| 14 | 72 | |
| 15 | 80 | |
| 16 | Efficiently Reusing Natural Language Processing Models for Phenotype Identification in Free-text Electronic Medical Records: Methodological Study | 1 |
| 17 | 61 | |
| 18 | Multi-Task Learning for Multiple Language Translationbreakdown → | 316 |
| 19 | Research and application of Dagang Oilfield casing program optimization | 0 |
| 20 | Reordering with Source Language Collocations | 3 |
About Hua Wu
Hua Wu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Ocean Engineering, having authored 156 papers that have together received 5.7k indexed citations. Recurring topics across this work include Topic Modeling (103 papers), Natural Language Processing Techniques (92 papers) and Multimodal Machine Learning Applications (25 papers). The work is most often cited by research in Artificial Intelligence (4.6k citations), Computer Vision and Pattern Recognition (1.4k citations) and Health Informatics (39 citations). Hua Wu has collaborated with scholars based in China, Japan and United States. Frequent co-authors include Haifeng Wang, Hao Tian, Yu Sun, Daxiang Dong, Xinyan Xiao, Dianhai Yu, Wei He, Zhongjun He, Rui Yan and Shuohuan Wang. Their work appears in journals such as Bioinformatics, Artificial Intelligence and Remote Sensing.
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.