98 citations · 338 across the 25 of their papers we have counts for
6 papers · 1 filter
Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines
Eric W. Tramel, Andre Manoel, Francesco Caltagirone +2
In this work, we consider compressed sensing reconstruction from measurements of -sparse structured signals which do not possess a writable correlation model. Assuming that…
Scampi: a robust approximate message-passing framework for compressive imaging
Jean Barbier, Eric W. Tramel, Florent Krzakala
Reconstruction of images from noisy linear measurements is a core problem in image processing, for which convex optimization methods based on total variation (TV) minimization have…
Approximate Message Passing with Restricted Boltzmann Machine Priors
Eric W. Tramel, Angélique Drémeau, Florent Krzakala
Approximate Message Passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problem. The AMP framework provides modularity i…
Statistical Estimation: From Denoising to Sparse Regression and Hidden Cliques
Eric W. Tramel, Santhosh Kumar, Andrei Giurgiu +1
These notes review six lectures given by Prof. Andrea Montanari on the topic of statistical estimation for linear models. The first two lectures cover the principles of signal reco…
Sparse Estimation with the Swept Approximated Message-Passing Algorithm
Andre Manoel, Florent Krzakala, Eric W. Tramel +1
Approximate Message Passing (AMP) has been shown to be a superior method for inference problems, such as the recovery of signals from sets of noisy, lower-dimensionality measuremen…
Variational Free Energies for Compressed Sensing
Florent Krzakala, Andre Manoel, Eric W. Tramel +1
We consider the variational free energy approach for compressed sensing. We first show that the naïve mean field approach performs remarkably well when coupled with a noise learnin…