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Creators/Authors contains: "Wu, Lingfei"

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  1. ; ; ; ; (, Journal of machine learning research)
    Free, publicly-accessible full text available April 25, 2026
  2. ; ; ; ; (, Journal of machine learning research)
    Free, publicly-accessible full text available April 15, 2026
  3. ; ; ; ; ; ; ; ; ; (, IEEE transactions on pattern analysis and machine intelligence)
    Free, publicly-accessible full text available July 7, 2026
  4. ; ; ; ; (, Journal of Machine Learning Research (JMLR))
    Free, publicly-accessible full text available April 1, 2026
  5. ; ; ; ; ; ; ; ; ; (, IEEE Transactions on Pattern Analysis and Machine Intelligence)
    Free, publicly-accessible full text available June 1, 2026
  6. ; ; (, Scientometrics)
    Abstract ChatGPT has arrived in quantitative research evaluation. With the exploration in this Letter to the Editor, we would like to widen the spectrum of the possible use of ChatGPT in bibliometrics by applying it to identify disruptive papers. The identification of disruptive papers using publication and citation counts has become a popular topic in scientometrics. The disadvantage of the quantitative approach is its complexity in the computation. The use of ChatGPT might be an easy to use alternative. 
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  7. ; ; (, Nature)
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  8. ; ; ; ; ; ; ; ; ; (, ACM Computing Surveys)
    Deep learning’s performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most GNNs were created using distinct theories, direct comparisons are impossible. Prior research has primarily concentrated on categorizing existing models, with little attention paid to their intrinsic connections. The purpose of this study is to establish a unified framework that integrates GNNs based on spectral graph and approximation theory. The framework incorporates a strong integration between spatial- and spectral-based GNNs while tightly associating approaches that exist within each respective domain. 
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  9. ; ; ; ; (, Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining)
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  10. ; ; (, IEEE Transactions on Neural Networks and Learning Systems)
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