Leonard Lausen
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Global and Planetary Change
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Topics
- Big Data and Business Intelligence (1 paper)Software Engineering Research (1 paper)Software System Performance and Reliability (1 paper)
- Journals
- arXiv (Cornell University)Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language TechnologiesProceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- United StatesGermanyAustralia
In The Last Decade
Leonard Lausen
4 papers receiving 122 citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 61
- Computer Vision and Pattern Recognition 50
- Global and Planetary Change 11
- Electrical and Electronic Engineering 9
- Computer Networks and Communications 8
Countries citing papers authored by Leonard Lausen
This map shows the geographic impact of Leonard Lausen'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 Leonard Lausen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leonard Lausen more than expected).
Fields of papers citing papers by Leonard Lausen
This network shows the impact of papers produced by Leonard Lausen. 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 Leonard Lausen. The network helps show where Leonard Lausen may publish in the future.
Co-authorship network of co-authors of Leonard Lausen
This figure shows the co-authorship network connecting the top 25 collaborators of Leonard Lausen. A scholar is included among the top collaborators of Leonard Lausen 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 Leonard Lausen. Leonard Lausen 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 | 14 | |
| 4 | GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing | 109 |
| 5 | 1 |
About Leonard Lausen
Leonard Lausen is a scholar working on Computer Science Applications, Human-Computer Interaction and Management Information Systems, having authored 5 papers that have together received 127 indexed citations. Recurring topics across this work include Big Data and Business Intelligence (1 paper), Software Engineering Research (1 paper) and Software System Performance and Reliability (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (50 citations), Artificial Intelligence (61 citations) and Media Technology (8 citations). Leonard Lausen has collaborated with scholars based in United States, Germany and Australia. Frequent co-authors include Sheng Zha, Zhang Zhi, Yi Zhu, Jian Guo, Chenguang Wang, Haibin Lin, Mu Li, Junyuan Xie, Hang Zhang and Tong He. Their work appears in journals such as arXiv (Cornell University), Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 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.