Chun‐Chen Tu
Impact in
- Artificial Intelligence top 10%
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
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- Advanced Malware Detection Techniques
Papers in
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- Adversarial Robustness in Machine Learning 2
- Machine Learning and Data Classification 1
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- Opportunistic and Delay-Tolerant Networks 1
- Co-authors
- Pin‐Yu Chen (4 shared papers)Paishun Ting (3 shared papers)Shin‐Ming Cheng (2 shared papers)Jinfeng Yi (1 shared paper)Cho‐Jui Hsieh (1 shared paper)Huan Zhang (1 shared paper)Sijia Liu (1 shared paper)Naisyin Wang (2 shared papers)
- Journals
- IEEE Access (1 paper)IEEE Transactions on Signal and Information Processing over Networks (1 paper)Journal of the Royal Statistical Society Series C (Applied Statistics) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesTaiwanFrance
In The Last Decade
Chun‐Chen Tu
5 papers receiving 204 citations
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 188
- Signal Processing 39
- Hardware and Architecture 22
- Computer Vision and Pattern Recognition 51
- Toxicology 4
Countries citing papers authored by Chun‐Chen Tu
This map shows the geographic impact of Chun‐Chen Tu'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 Chun‐Chen Tu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chun‐Chen Tu more than expected).
Fields of papers citing papers by Chun‐Chen Tu
This network shows the impact of papers produced by Chun‐Chen Tu. 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 Chun‐Chen Tu. The network helps show where Chun‐Chen Tu may publish in the future.
Co-authors
The 12 scholars most cited alongside Chun‐Chen Tu, 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 | 2019 | 192 | |
| 2 | 2018 | 4 | |
| 3 | 2019 | 3 | |
| 4 | 2019 | 3 | |
| 5 | 2017 | 3 |
About Chun‐Chen Tu
Chun‐Chen Tu is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 5 papers that have together received 205 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (2 papers), Complex Network Analysis Techniques (1 paper), Parallel Computing and Optimization Techniques (1 paper), COVID-19 diagnosis using AI (1 paper), Opportunistic and Delay-Tolerant Networks (1 paper), Statistical Methods and Bayesian Inference (1 paper), Statistical Methods and Inference (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Artificial Intelligence (188 citations), Signal Processing (39 citations), Hardware and Architecture (22 citations), Computer Vision and Pattern Recognition (51 citations) and Toxicology (4 citations). Chun‐Chen Tu has collaborated with scholars based in United States, Taiwan and France. Frequent co-authors include Pin‐Yu Chen, Paishun Ting, Shin‐Ming Cheng, Jinfeng Yi, Cho‐Jui Hsieh, Huan Zhang, Sijia Liu, Naisyin Wang, Danai Koutra and Benjamin Lemasson. Their work appears in journals such as IEEE Access, IEEE Transactions on Signal and Information Processing over Networks, Journal of the Royal Statistical Society Series C (Applied Statistics) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.