A square root form of the second-order covariance update is presented for the first time, improving numerical accuracy and efficiency in recursive estimation algorithms.
Analysis of individual differ- ences in multidimensional scaling via an n-way generalization of “Eckart-Young
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A tensor train method computes the Koopman generator via operator logarithm while preserving low-rank structure for scalable identification of high-dimensional nonlinear dynamics.
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
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Covariance Square Root Second-Order Mapping
A square root form of the second-order covariance update is presented for the first time, improving numerical accuracy and efficiency in recursive estimation algorithms.
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Tensor-based computation of the Koopman generator via operator logarithm
A tensor train method computes the Koopman generator via operator logarithm while preserving low-rank structure for scalable identification of high-dimensional nonlinear dynamics.
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Quantum-inspired tensor networks in machine learning models
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.