Blind calibration for compressed sensing: state evolution and an online algorithm

  title={Blind calibration for compressed sensing: state evolution and an online algorithm},
  author={Marylou Gabri{\'e} and Jean Barbier and Florent Krzakala and Lenka Zdeborov{\'a}},
  journal={Journal of Physics A: Mathematical and Theoretical},
Compressed sensing allows for the acquisition of compressible signals with a small number of measurements. In experimental settings, the sensing process corresponding to the hardware implementation is not always perfectly known and may require a calibration. To this end, blind calibration proposes to perform at the same time the calibration and the compressed sensing. Schülke and collaborators suggested an approach based on approximate message passing for blind calibration (cal-AMP) in (Schülke… 
1 Citations

Mean-field inference methods for neural networks

  • Marylou Gabri'e
  • Computer Science
    Journal of Physics A: Mathematical and Theoretical
  • 2020
A selection of classical mean-field methods and recent progress relevant for inference in neural networks are reviewed, and the principles of derivations of high-temperature expansions, the replica method and message passing algorithms are reminded, highlighting their equivalences and complementarities.



Blind Calibration in Compressed Sensing using Message Passing Algorithms

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Inference from correlated patterns: a unified theory for perceptron learning and linear vector channels

A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The