Cheng-Yu Hsieh
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Mechanical Engineering
- Computational Mechanics
- Computer Vision and Pattern Recognition
- Co-authors
- Cristina H. AmonS. C. YaoRanjay KrishnaChih‐Kuan YehYasuhisa FujiiAlex RatnerTomas PfisterHootan Nakhost
- Topics
- Topic Modeling (3 papers)Advanced Image and Video Retrieval Techniques (3 papers)Machine Learning and Algorithms (2 papers)
- Partner nations
- TaiwanUnited StatesUnited Kingdom
In The Last Decade
Cheng-Yu Hsieh
29 papers receiving 271 citations
Hit Papers
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 95
- Electrical and Electronic Engineering 59
- Mechanical Engineering 41
- Computational Mechanics 37
- Computer Vision and Pattern Recognition 36
Countries citing papers authored by Cheng-Yu Hsieh
This map shows the geographic impact of Cheng-Yu Hsieh'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 Cheng-Yu Hsieh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cheng-Yu Hsieh more than expected).
Fields of papers citing papers by Cheng-Yu Hsieh
This network shows the impact of papers produced by Cheng-Yu Hsieh. 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 Cheng-Yu Hsieh. The network helps show where Cheng-Yu Hsieh may publish in the future.
Co-authorship network of co-authors of Cheng-Yu Hsieh
This figure shows the co-authorship network connecting the top 25 collaborators of Cheng-Yu Hsieh. A scholar is included among the top collaborators of Cheng-Yu Hsieh 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 Cheng-Yu Hsieh. Cheng-Yu Hsieh 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 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 3 | |
| 7 | 3 | |
| 8 | Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizesbreakdown → | 108 |
| 9 | 2 | |
| 10 | 4 | |
| 11 | 6 | |
| 12 | 3 | |
| 13 | 3 | |
| 14 | 7 | |
| 15 | 0 | |
| 16 | 2 | |
| 17 | 5 | |
| 18 | Monolithically Integrated Flexible Artificial Retina Microsystems Technology and In Vitro Characterization | 3 |
| 19 | 47 | |
| 20 | 0 |
About Cheng-Yu Hsieh
Cheng-Yu Hsieh is a scholar working on Computer Vision and Pattern Recognition, Surfaces, Coatings and Films and Artificial Intelligence, having authored 38 papers that have together received 280 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Health Informatics (7 citations), Artificial Intelligence (95 citations) and Computational Mechanics (37 citations). Cheng-Yu Hsieh has collaborated with scholars based in Taiwan, United States and United Kingdom. Frequent co-authors include Cristina H. Amon, S. C. Yao, Ranjay Krishna, Chih‐Kuan Yeh, Yasuhisa Fujii, Alex Ratner, Tomas Pfister, Hootan Nakhost, Chunliang Li and Wanjiun Liao. Their work appears in journals such as PLoS ONE, Optics Express and Journal of Experimental Psychology Learning Memory and Cognition.
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.