Pin‐Yu Chen

189 papers and 3.3k indexed citations i.

About

Pin‐Yu Chen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Pin‐Yu Chen has authored 189 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 116 papers in Artificial Intelligence, 28 papers in Computer Vision and Pattern Recognition and 28 papers in Statistical and Nonlinear Physics. Recurrent topics in Pin‐Yu Chen’s work include Adversarial Robustness in Machine Learning (56 papers), Complex Network Analysis Techniques (24 papers) and Anomaly Detection Techniques and Applications (19 papers). Pin‐Yu Chen is often cited by papers focused on Adversarial Robustness in Machine Learning (56 papers), Complex Network Analysis Techniques (24 papers) and Anomaly Detection Techniques and Applications (19 papers). Pin‐Yu Chen collaborates with scholars based in United States, Taiwan and China. Pin‐Yu Chen's co-authors include Shin‐Ming Cheng, Kwang‐Cheng Chen, Cho‐Jui Hsieh, Alfred O. Hero, Jinfeng Yi, Huan Zhang, Chao-Han Huck Yang, Sijia Liu, Jun Qi and Sayak Paul and has published in prestigious journals such as Nature Communications, PLoS ONE and NeuroImage.

In The Last Decade

Co-authorship network of co-authors of Pin‐Yu Chen i

Fields of papers citing papers by Pin‐Yu Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Countries citing papers authored by Pin‐Yu Chen

Since Specialization
Citations

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).

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.

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