* Our pionering paper on a new (and powerful)
approach to compressed sensing is in open access on
Phys. Rev. X 2, 021005 (2012) .
* A longer version, with many more details and computation, is now published on J. Stat. Mech. (2012) P08009 (and still avaliable on the arxiv).
* We have also investigated compressed sensing of signal which are only approximatly sparse . This was presented at the 50th Allerton conference. * Another interesting case is when the matrix (and the signal) are binary. This has application in the Non-adaptive pooling strategies for detection of rare faulty items .
* Our more recent works deal with matrix uncertaintly and dictionnary learning and calibration.
Our new algorithm for Blind Sensor Calibration can be downloaded here in matlab. The corresponding article has been presented at NIPS 2013.
The Most recent matlab implementation of our algorithm for compressed sensing can be access on Github: BPCS (Belief Propagation for Compressed Sensing.
The original MATLAB implementaion can be download here. We would be more than happy to receive comments and suggestions.
We have also other older implementations: here is a c++ implementation . We have also a a python implementation if you prefer. The fastest implementation is the MATLAB one. Our solver has also been implemented in the KL1p library.
We have been asked (between other things) to give the data on
the spinodal transition that marks the limit of the performance of
the EM-BP algorithm for Gauss-Bernoulli signals: here they are. When using a
(non-structured) random matrix and a Gauss-Bernoulli signal, this
marks the limit of efficiency of sampling algorithm such as
Belief-Propagation, and it may be a limit for many other approaches
as well. This limit, however, is already beyond the Donoho-Tanner
transition. Of course, our
seeding strategy allows to break this limit. For reproducible
research these are the data and command
file we have used in our study, which we give if you want to
reproduce our Figure.1 using the c++ code.
The most recent implementation in MATLAB deals with error-correcting code: download the MATLAB implementation for real-values Error Correcting Code.
* Igor Carron mentioned our work a couple of times in his blog "Nuit
stunning development in breaking the Donoho-Tanner phase transition ?
ASPICS Matlab Implementation.
* An interview by the French vulgarization Journal, La Recherche Fevrier 2012.
This is aspics.krzakala.org.
This page is maintained by Florent Krzakala
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