Shih-Yang Su
- Computer Vision and Pattern Recognition top 5%
- Computer Graphics and Computer-Aided Design top 2%
- Artificial Intelligence
- Computational Mechanics
- Media Technology
- Co-authors
- Jia‐Bin HuangMeng-Li ShihJohannes KopfZhang-Wei HongChun‐Yi LeeHelge RhodinTimur BagautdinovYumin Chen
- Topics
- Reinforcement Learning in Robotics (2 papers)Neural Networks and Applications (1 paper)Scheduling and Optimization Algorithms (1 paper)
- Cited by
- Computer Graphics and Computer-Aided DesignComputer Vision and Pattern RecognitionMedia Technology
- Journals
- International Journal of Production ResearcharXiv (Cornell University)Neural Information Processing Systems
In The Last Decade
Shih-Yang Su
8 papers receiving 259 citations
Peers
Comparison fields: 5 of 37
- Computer Vision and Pattern Recognition 219
- Computer Graphics and Computer-Aided Design 94
- Artificial Intelligence 44
- Computational Mechanics 37
- Media Technology 20
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-authorship network of co-authors of Shih-Yang Su
This figure shows the co-authorship network connecting the top 25 collaborators of Shih-Yang Su. A scholar is included among the top collaborators of Shih-Yang Su 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 Shih-Yang Su. Shih-Yang Su is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 11 | |
| 3 | 167 | |
| 4 | 15 | |
| 5 | Diversity-Driven Exploration Strategy for Deep Reinforcement Learning | 5 |
| 6 | 50 | |
| 7 | 3 | |
| 8 | 9 |
About Shih-Yang Su
Shih-Yang Su is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Biophysics, having authored 8 papers that have together received 266 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (2 papers), Neural Networks and Applications (1 paper) and Scheduling and Optimization Algorithms (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) and Media Technology (20 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, Timur Bagautdinov, Yumin Chen, Li Su and Yi‐Hsuan Yang. 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.