Chih‐Hong Cheng
- Artificial Intelligence top 10%
- Software top 5%
- Computer Vision and Pattern Recognition
- Automotive Engineering
- Control and Systems Engineering
- Co-authors
- Georg NührenbergHirotoshi YasuokaHarald RueßRongjie YanJustin DauwelsJavier Ibañez‐GuzmánGuofa LiPu Wang
- Topics
- Adversarial Robustness in Machine Learning (11 papers)Formal Methods in Verification (8 papers)Advanced Neural Network Applications (7 papers)
- Journals
- IEEE Sensors JournalLecture notes in computer sciencearXiv (Cornell University)
- Partner nations
- GermanyChinaUnited States
In The Last Decade
Chih‐Hong Cheng
27 papers receiving 296 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 124
- Software 59
- Computer Vision and Pattern Recognition 47
- Automotive Engineering 45
- Control and Systems Engineering 41
Countries citing papers authored by Chih‐Hong Cheng
This map shows the geographic impact of Chih‐Hong Cheng'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‐Hong Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chih‐Hong Cheng more than expected).
Fields of papers citing papers by Chih‐Hong Cheng
This network shows the impact of papers produced by Chih‐Hong Cheng. 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‐Hong Cheng. The network helps show where Chih‐Hong Cheng may publish in the future.
Co-authorship network of co-authors of Chih‐Hong Cheng
This figure shows the co-authorship network connecting the top 25 collaborators of Chih‐Hong Cheng. A scholar is included among the top collaborators of Chih‐Hong Cheng 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‐Hong Cheng. Chih‐Hong Cheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 4 | |
| 6 | 3 | |
| 7 | 8 | |
| 8 | 3 | |
| 9 | 6 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 1 | |
| 13 | 19 | |
| 14 | 17 | |
| 15 | Verification of Binarized Neural Networks. | 7 |
| 16 | 12 | |
| 17 | 12 | |
| 18 | 8 | |
| 19 | 6 | |
| 20 | Applied Verification: The Ptolemy Approach | 6 |
About Chih‐Hong Cheng
Chih‐Hong Cheng is a scholar working on Software, Hardware and Architecture and Artificial Intelligence, having authored 31 papers that have together received 311 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (11 papers), Formal Methods in Verification (8 papers) and Advanced Neural Network Applications (7 papers). The work is most often cited by research in Software (59 citations), Hardware and Architecture (27 citations) and Artificial Intelligence (124 citations). Chih‐Hong Cheng has collaborated with scholars based in Germany, China and United States. Frequent co-authors include Georg Nührenberg, Hirotoshi Yasuoka, Harald Rueß, Rongjie Yan, Justin Dauwels, Javier Ibañez‐Guzmán, Guofa Li, Pu Wang, Avik Santra and Sevgi Zübeyde Gürbüz. Their work appears in journals such as IEEE Sensors Journal, Lecture notes in computer science 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.