Shih-Yang Su
Impact in
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- Computer Graphics and Visualization Techniques
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- Advanced Vision and Imaging
- Advanced Image Processing Techniques
- Advanced Neural Network Applications
- Image Enhancement Techniques
- Generative Adversarial Networks and Image Synthesis
Papers in
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- Multimodal Machine Learning Applications 1
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- Reinforcement Learning in Robotics 2
- Evolutionary Algorithms and Applications 1
- Neural Networks and Applications 1
- Co-authors
- Jia‐Bin Huang (1 shared paper)Meng-Li Shih (1 shared paper)Johannes Kopf (1 shared paper)Zhang-Wei Hong (3 shared papers)Chun‐Yi Lee (3 shared papers)Helge Rhodin (2 shared papers)Yumin Chen (1 shared paper)Timur Bagautdinov (1 shared paper)
- Journals
- International Journal of Production Research (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)
In The Last Decade
Shih-Yang Su
8 papers receiving 259 citations
Peers
Comparison fields: 5 of 37
- Computer Graphics and Computer-Aided Design 94
- Computer Vision and Pattern Recognition 219
- Media Technology 20
- Computational Mechanics 37
- Artificial Intelligence 44
Countries citing papers authored by Shih-Yang Su
This map shows the geographic impact of Shih-Yang Su'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 Shih-Yang Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shih-Yang Su more than expected).
Fields of papers citing papers by Shih-Yang Su
This network shows the impact of papers produced by Shih-Yang Su. 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 Shih-Yang Su. The network helps show where Shih-Yang Su may publish in the future.
Co-authors
The 10 scholars most cited alongside Shih-Yang Su, 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 | 167 | |
| 2 | 2018 | 50 | |
| 3 | 2018 | 15 | |
| 4 | 2023 | 11 | |
| 5 | 1996 | 9 | |
| 6 | 2024 | 6 | |
| 7 | Diversity-Driven Exploration Strategy for Deep Reinforcement Learning | 2018 | 5 |
| 8 | 2017 | 3 |
About Shih-Yang Su
Shih-Yang Su is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Management Information Systems, Control and Systems Engineering and Signal Processing, having authored 8 papers that have together received 266 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (2 papers), Speech and Audio Processing (1 paper), Cell Image Analysis Techniques (1 paper), Advanced Queuing Theory Analysis (1 paper), 3D Shape Modeling and Analysis (1 paper), Evolutionary Algorithms and Applications (1 paper), Multimodal Machine Learning Applications (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Computer Graphics and Computer-Aided Design (94 citations), Computer Vision and Pattern Recognition (219 citations), Media Technology (20 citations), Computational Mechanics (37 citations) and Artificial Intelligence (44 citations). Shih-Yang Su has collaborated with scholars based in Taiwan, Canada and Israel. Frequent co-authors include Jia‐Bin Huang, Meng-Li Shih, Johannes Kopf, Zhang-Wei Hong, Chun‐Yi Lee, Helge Rhodin, Yumin Chen, Timur Bagautdinov, Yi‐Hsuan Yang and Li Su. Their work appears in journals such as International Journal of Production Research, arXiv (Cornell University) and Neural Information Processing Systems.
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.