Pin‐Yu Chen
- Artificial Intelligence top 0.5%
- Adversarial Robustness in Machine Learning 64
- Anomaly Detection Techniques and Applications 25
- Domain Adaptation and Few-Shot Learning 20
- Advanced Graph Neural Networks 14
- Signal Processing top 2%
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- Complex Network Analysis Techniques 27
- Opinion Dynamics and Social Influence 17
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- Opportunistic and Delay-Tolerant Networks 12
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- Advanced Neural Network Applications 17
- Co-authors
- Shin‐Ming ChengKwang‐Cheng ChenCho‐Jui HsiehAlfred O. HeroJinfeng YiHuan ZhangSijia LiuChao-Han Huck Yang
- Journals
- IEEE Communications Magazine (5 papers)IEEE Transactions on Signal Processing (5 papers)IEEE Internet of Things Journal (4 papers)
- Partner nations
- United StatesTaiwanChina
In The Last Decade
Pin‐Yu Chen
216 papers receiving 3.9k citations
Hit Papers
Peers
Comparison fields: 5 of 180
- Artificial Intelligence 2.4k
- Signal Processing 446
- Statistical and Nonlinear Physics 479
- Computer Networks and Communications 754
- Computer Vision and Pattern Recognition 546
Countries citing papers authored by Pin‐Yu Chen
This map shows the geographic impact of Pin‐Yu Chen'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 Pin‐Yu Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pin‐Yu Chen more than expected).
Fields of papers citing papers by Pin‐Yu Chen
This network shows the impact of papers produced by Pin‐Yu Chen. 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 Pin‐Yu Chen. The network helps show where Pin‐Yu Chen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pin‐Yu Chen, 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 | 2025 | 0 | |
| 2 | 2025 | 3 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 7 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 8 | |
| 8 | 2024 | 7 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 6 | |
| 11 | 2023 | 6 | |
| 12 | Adversarial Attack Generation Empowered by Min-Max Optimization | 2021 | 11 |
| 13 | Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations | 2021 | 9 |
| 14 | 2020 | 31 | |
| 15 | AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models | 2020 | 37 |
| 16 | 2019 | 10 | |
| 17 | 2019 | 4 | |
| 18 | Beyond Adversarial Training: Min-Max Optimization in Adversarial Attack and Defense. | 2019 | 2 |
| 19 | 2018 | 50 | |
| 20 | 2013 | 15 |
About Pin‐Yu Chen
Pin‐Yu Chen is a scholar working on Health Informatics, Artificial Intelligence and Computational Mathematics, having authored 230 papers that have together received 4.0k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (64 papers), Complex Network Analysis Techniques (27 papers), Anomaly Detection Techniques and Applications (25 papers), Domain Adaptation and Few-Shot Learning (20 papers), Advanced Neural Network Applications (17 papers), Opinion Dynamics and Social Influence (17 papers), Advanced Graph Neural Networks (14 papers) and Opportunistic and Delay-Tolerant Networks (12 papers). The work is most often cited by research in Artificial Intelligence (2.4k citations), Signal Processing (446 citations) and Statistical and Nonlinear Physics (479 citations). Pin‐Yu Chen has collaborated with scholars based in United States, Taiwan and China. Frequent co-authors include Shin‐Ming Cheng, Kwang‐Cheng Chen, Cho‐Jui Hsieh, Alfred O. Hero, Jinfeng Yi, Huan Zhang, Sijia Liu, Sijia Liu, Chao-Han Huck Yang and Yash Sharma. Their work appears in journals such as IEEE Communications Magazine, IEEE Transactions on Signal Processing, IEEE Internet of Things Journal, IEEE Access and IEEE Communications Letters.
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.