Wan-Duo Kurt
Impact in
-
- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
-
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Neural Networks and Applications
- Machine Learning and Data Classification
- Machine Learning and ELM
Papers in
-
- Advanced Neural Network Applications 2
- Video Analysis and Summarization 1
- Advanced Vision and Imaging 1
- Generative Adversarial Networks and Image Synthesis 1
-
- Neural Networks and Applications 2
- Domain Adaptation and Few-Shot Learning 2
- Adversarial Robustness in Machine Learning 1
- Co-authors
- John Lewis (3 shared papers)W. Bastiaan Kleijn (3 shared papers)David Balduzzi (1 shared paper)Marcus Frean (1 shared paper)Brian McWilliams (1 shared paper)Thomas Leung (1 shared paper)
- Journals
- Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- New ZealandSwitzerlandUnited States
In The Last Decade
Wan-Duo Kurt
4 papers receiving 131 citations
Peers
Comparison fields: 5 of 55
- Computer Vision and Pattern Recognition 66
- Artificial Intelligence 68
- Media Technology 8
- Computer Graphics and Computer-Aided Design 3
- Signal Processing 9
Countries citing papers authored by Wan-Duo Kurt
This map shows the geographic impact of Wan-Duo Kurt'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 Wan-Duo Kurt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wan-Duo Kurt more than expected).
Fields of papers citing papers by Wan-Duo Kurt
This network shows the impact of papers produced by Wan-Duo Kurt. 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 Wan-Duo Kurt. The network helps show where Wan-Duo Kurt may publish in the future.
Co-authors
The 6 scholars most cited alongside Wan-Duo Kurt, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 62 | |
| 2 | 2017 | 43 | |
| 3 | 2024 | 17 | |
| 4 | 2024 | 9 |
About Wan-Duo Kurt
Wan-Duo Kurt is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience, Infectious Diseases and Organic Chemistry, having authored 4 papers that have together received 131 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Video Analysis and Summarization (1 paper), EEG and Brain-Computer Interfaces (1 paper), Adversarial Robustness in Machine Learning (1 paper), Advanced Vision and Imaging (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (66 citations), Artificial Intelligence (68 citations), Media Technology (8 citations), Computer Graphics and Computer-Aided Design (3 citations) and Signal Processing (9 citations). Wan-Duo Kurt has collaborated with scholars based in New Zealand, Switzerland and United States. Frequent co-authors include John Lewis, W. Bastiaan Kleijn, David Balduzzi, Marcus Frean, Brian McWilliams and Thomas Leung. Their work appears in journals such as Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell University).
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.