Chih‐Kuan Yeh
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Signal Processing
- Molecular Biology
- Co-authors
- Yu-Chiang Frank WangWei-Jen KoShang‐Fu ChenYu-Chiang WangYi‐Chen ChenCheng-Yu HsiehTomas PfisterPradeep Ravikumar
- Topics
- Explainable Artificial Intelligence (XAI) (4 papers)Adversarial Robustness in Machine Learning (3 papers)Advanced Image and Video Retrieval Techniques (2 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on GamesarXiv (Cornell University)
- Partner nations
- United StatesTaiwan
In The Last Decade
Chih‐Kuan Yeh
9 papers receiving 433 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 348
- Computer Vision and Pattern Recognition 158
- Information Systems 53
- Signal Processing 33
- Molecular Biology 25
Countries citing papers authored by Chih‐Kuan Yeh
This map shows the geographic impact of Chih‐Kuan Yeh'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 Chih‐Kuan Yeh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chih‐Kuan Yeh more than expected).
Fields of papers citing papers by Chih‐Kuan Yeh
This network shows the impact of papers produced by Chih‐Kuan Yeh. 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 Chih‐Kuan Yeh. The network helps show where Chih‐Kuan Yeh may publish in the future.
Co-authorship network of co-authors of Chih‐Kuan Yeh
This figure shows the co-authorship network connecting the top 25 collaborators of Chih‐Kuan Yeh. A scholar is included among the top collaborators of Chih‐Kuan Yeh 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 Chih‐Kuan Yeh. Chih‐Kuan Yeh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizesbreakdown → | 108 |
| 2 | 7 | |
| 3 | 4 | |
| 4 | On Concept-Based Explanations in Deep Neural Networks | 6 |
| 5 | How Sensitive are Sensitivity-Based Explanations? | 5 |
| 6 | 28 | |
| 7 | 18 | |
| 8 | 102 | |
| 9 | 158 |
About Chih‐Kuan Yeh
Chih‐Kuan Yeh is a scholar working on Artificial Intelligence, History and Philosophy of Science and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 436 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (4 papers), Adversarial Robustness in Machine Learning (3 papers) and Advanced Image and Video Retrieval Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (348 citations), Health Informatics (14 citations) and Computer Vision and Pattern Recognition (158 citations). Chih‐Kuan Yeh has collaborated with scholars based in United States and Taiwan. Frequent co-authors include Yu-Chiang Frank Wang, Wei-Jen Ko, Shang‐Fu Chen, Yu-Chiang Wang, Yi‐Chen Chen, Cheng-Yu Hsieh, Tomas Pfister, Pradeep Ravikumar, Chunliang Li and Hootan Nakhost. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Games 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.