125 citations · 279 across the 7 of their papers we have counts for
11 papers
SAND: One-Shot Feature Selection with Additive Noise Distortion
Pedram Pad, Hadi Hammoud, Mohamad Dia +2
Feature selection is a critical step in data-driven applications, reducing input dimensionality to enhance learning accuracy, computational efficiency, and interpretability. Existi…
Galaxy Image Simulation Using Progressive GANs
Mohamad Dia, Elodie Savary, Martin Melchior +1
In this work, we provide an efficient and realistic data-driven approach to simulate astronomical images using deep generative models from machine learning. Our solution is based o…
Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method
Jean Barbier, Mohamad Dia, Nicolas Macris +2
Factorizing low-rank matrices is a problem with many applications in machine learning and statistics, ranging from sparse PCA to community detection and sub-matrix localization. Fo…
A Compressed Sensing Approach for Distribution Matching
Mohamad Dia, Vahid Aref, Laurent Schmalen
In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem. Our proposed solution is inspired from the compressed sensing paradigm and the sp…
Universal Sparse Superposition Codes with Spatial Coupling and GAMP Decoding
Jean Barbier, Mohamad Dia, Nicolas Macris
Sparse superposition codes, or sparse regression codes, constitute a new class of codes which was first introduced for communication over the additive white Gaussian noise (AWGN) c…
Generalized Approximate Message-Passing Decoder for Universal Sparse Superposition Codes
Erdem Biyik, Jean Barbier, Mohamad Dia
Sparse superposition (SS) codes were originally proposed as a capacity-achieving communication scheme over the additive white Gaussian noise channel (AWGNC) [1]. Very recently, it…
Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation
Jean Barbier, Nicolas Macris, Mohamad Dia +1
We consider the estimation of a signal from the knowledge of its noisy linear random Gaussian projections. A few examples where this problem is relevant are compressed sensing, spa…
The Mutual Information in Random Linear Estimation
Jean Barbier, Mohamad Dia, Nicolas Macris +1
We consider the estimation of a signal from the knowledge of its noisy linear random Gaussian projections, a problem relevant in compressed sensing, sparse superposition codes or c…
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
Jean Barbier, Mohamad Dia, Nicolas Macris +3
Factorizing low-rank matrices has many applications in machine learning and statistics. For probabilistic models in the Bayes optimal setting, a general expression for the mutual i…
Threshold Saturation of Spatially Coupled Sparse Superposition Codes for All Memoryless Channels
Jean Barbier, Mohamad Dia, Nicolas Macris
We recently proved threshold saturation for spatially coupled sparse superposition codes on the additive white Gaussian noise channel. Here we generalize our analysis to a much bro…
Proof of Threshold Saturation for Spatially Coupled Sparse Superposition Codes
Jean Barbier, Mohamad Dia, Nicolas Macris
Recently, a new class of codes, called sparse superposition or sparse regression codes, has been proposed for communication over the AWGN channel. It has been proven that they achi…