CSEN is a compact convolutional neural network trained to estimate sparse support sets directly from measurements, claiming state-of-the-art accuracy at lower computational cost than iterative methods.
Signal recovery from random mea- surements via orthogonal matching pursuit,
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A greedy algorithm for low-rank matrix recovery incorporates subspace prior information to achieve convergence under milder rank-restricted isometry conditions than standard methods without priors.
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Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing
CSEN is a compact convolutional neural network trained to estimate sparse support sets directly from measurements, claiming state-of-the-art accuracy at lower computational cost than iterative methods.
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A Greedy Algorithm for Matrix Recovery with Subspace Prior Information
A greedy algorithm for low-rank matrix recovery incorporates subspace prior information to achieve convergence under milder rank-restricted isometry conditions than standard methods without priors.