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Publications [#339261] of Guillermo Sapiro

Papers Published

  1. Giryes, R; Eldar, YC; Bronstein, AM; Sapiro, G, The Learned Inexact Project Gradient Descent Algorithm, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018-April (September, 2018), pp. 6767-6771, IEEE, ISBN 9781538646588 [doi]
    (last updated on 2019/06/20)

    Abstract:
    © 2018 IEEE. Accelerating iterative algorithms for solving inverse problems using neural networks have become a very popular strategy in the recent years. In this work, we propose a theoretical analysis that may provide an explanation for its success. Our theory relies on the usage of inexact projections with the projected gradient descent (PGD) method. It is demonstrated in various problems including image super-resolution.

 

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Mathematics Department
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