A deep learning approach to structured signal recovery
In this paper, we develop a new framework for sensing and recovering structured signals. In
contrast to compressive sensing (CS) systems that employ linear measurements, sparse …
contrast to compressive sensing (CS) systems that employ linear measurements, sparse …
From denoising to compressed sensing
CA Metzler, A Maleki… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
A denoising algorithm seeks to remove noise, errors, or perturbations from a signal.
Extensive research has been devoted to this arena over the last several decades, and as a …
Extensive research has been devoted to this arena over the last several decades, and as a …
Unsupervised learning with Stein's unbiased risk estimator
Learning from unlabeled and noisy data is one of the grand challenges of machine learning.
As such, it has seen a flurry of research with new ideas proposed continuously. In this work …
As such, it has seen a flurry of research with new ideas proposed continuously. In this work …
Tfpnp: Tuning-free plug-and-play proximal algorithms with applications to inverse imaging problems
Plug-and-Play (PnP) is a non-convex optimization framework that combines proximal
algorithms, for example, the alternating direction method of multipliers (ADMM), with …
algorithms, for example, the alternating direction method of multipliers (ADMM), with …
Compressive imaging via approximate message passing with image denoising
We consider compressive imaging problems, where images are reconstructed from a
reduced number of linear measurements. Our objective is to improve over existing …
reduced number of linear measurements. Our objective is to improve over existing …
Beamspace channel estimation for massive MIMO mmWave systems: Algorithm and VLSI design
SH Mirfarshbafan, A Gallyas-Sanhueza… - … on Circuits and …, 2020 - ieeexplore.ieee.org
Millimeter-wave (mmWave) communication in combination with massive multiuser multiple-
input multiple-output (MU-MIMO) enables high-bandwidth data transmission to multiple …
input multiple-output (MU-MIMO) enables high-bandwidth data transmission to multiple …
Near optimal compressed sensing without priors: Parametric SURE approximate message passing
Both theoretical analysis and empirical evidence confirm that the approximate message
passing (AMP) algorithm can be interpreted as recursively solving a signal denoising …
passing (AMP) algorithm can be interpreted as recursively solving a signal denoising …
Unsourced random access over fading channels via data repetition, permutation, and scrambling
We focus on an unsourced random access (URA) system for communication over fading
channels where the payload of each packet is encoded for error-correction, repeated …
channels where the payload of each packet is encoded for error-correction, repeated …
Consistent parameter estimation for lasso and approximate message passing
Consistent parameter estimation for LASSO and approximate message passing Page 1 The
Annals of Statistics 2018, Vol. 46, No. 1, 119–148 https://doi.org/10.1214/17-AOS1544 © Institute …
Annals of Statistics 2018, Vol. 46, No. 1, 119–148 https://doi.org/10.1214/17-AOS1544 © Institute …
Hybrid approximate message passing
Gaussian and quadratic approximations of message passing algorithms on graphs have
attracted considerable recent attention due to their computational simplicity, analytic …
attracted considerable recent attention due to their computational simplicity, analytic …