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Creators/Authors contains: "Zheng, Lizhong"

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  1. ; ; ; ; ; ( , Proc. NeurIPS 2023 Workshop on Machine Learning and the Physical Sciences (ML4PS))
    This paper proposes an optimization-based method to learn the singular value decomposition (SVD) of a compact operator with ordered singular functions. The proposed objective function is based on Schmidt’s low-rank approximation theorem (1907) that characterizes a truncated SVD as a solution minimizing the mean squared error, accompanied with a technique called nesting to learn the ordered structure. When the optimization space is parameterized by neural networks, we refer to the proposed method as NeuralSVD. The implementation does not require sophisticated optimization tricks unlike existing approaches. 
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    Free, publicly-accessible full text available December 15, 2024
  2. ; ( , ISIT 2023)
    Free, publicly-accessible full text available June 22, 2024
  3. ; ; ( , ISIT 2023)
    Free, publicly-accessible full text available June 22, 2024
  4. ; ; ; ( , IEEE Wireless Communications Letters)
    Free, publicly-accessible full text available May 1, 2024
  5. ; ; ; ( , IEEE Transactions on Wireless Communications)
    Free, publicly-accessible full text available April 26, 2024
  6. ; ; ; ; ( , IEEE Transactions on Wireless Communications)
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  7. ; ; ; ( , Entropy)
    With the unprecedented performance achieved by deep learning, it is commonly believed that deep neural networks (DNNs) attempt to extract informative features for learning tasks. To formalize this intuition, we apply the local information geometric analysis and establish an information-theoretic framework for feature selection, which demonstrates the information-theoretic optimality of DNN features. Moreover, we conduct a quantitative analysis to characterize the impact of network structure on the feature extraction process of DNNs. Our investigation naturally leads to a performance metric for evaluating the effectiveness of extracted features, called the H-score, which illustrates the connection between the practical training process of DNNs and the information-theoretic framework. Finally, we validate our theoretical results by experimental designs on synthesized data and the ImageNet dataset. 
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  8. ; ; ; ( , 2020 54th Asilomar Conference on Signals, Systems, and Computers)
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  9. ; ; ( , IEEE Journal on Selected Areas in Information Theory)
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  10. ; ; ; ( , 2020 IEEE International Symposium on Information Theory (ISIT))
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