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Creators/Authors contains: "Ryu, Jongha"

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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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