180 citations · 435 across the 12 of their papers we have counts for
17 papers
Autotuning Plug-and-Play Algorithms for MRI
Saurav K. Shastri, Rizwan Ahmad, Philip Schniter
For magnetic resonance imaging (MRI), recently proposed "plug-and-play" (PnP) image recovery algorithms have shown remarkable performance. These PnP algorithms are similar to tradi…
MRI Image Recovery using Damped Denoising Vector AMP
Subrata Sarkar, Rizwan Ahmad, Philip Schniter
Motivated by image recovery in magnetic resonance imaging (MRI), we propose a new approach to solving linear inverse problems based on iteratively calling a deep neural-network, so…
OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging
Chong Chen, Yingmin Liu, Philip Schniter +5
Cardiovascular MRI (CMR) is a non-invasive imaging modality that provides excellent soft-tissue contrast without the use of ionizing radiation. Physiological motions and limited sp…
Sketching Datasets for Large-Scale Learning (long version)
Rémi Gribonval, Antoine Chatalic, Nicolas Keriven +3
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e.g., clustering, classification…
Free-breathing Cardiovascular MRI Using a Plug-and-Play Method with Learned Denoiser
Sizhuo Liu, Edward Reehorst, Philip Schniter +1
Cardiac magnetic resonance imaging (CMR) is a noninvasive imaging modality that provides a comprehensive evaluation of the cardiovascular system. The clinical utility of CMR is ham…
Inference in Multi-Layer Networks with Matrix-Valued Unknowns
Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan +2
We consider the problem of inferring the input and hidden variables of a stochastic multi-layer neural network from an observation of the output. The hidden variables in each layer…
Inference with Deep Generative Priors in High Dimensions
Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan +2
Deep generative priors offer powerful models for complex-structured data, such as images, audio, and text. Using these priors in inverse problems typically requires estimating the…
Asteroid mining with small spacecraft and its economic feasibility
Pablo Calla, Dan Fries, Chris Welch
Asteroid mining offers the possibility to revolutionize supply of resources vital for human civilization. Preliminary analysis suggests that Near-Earth Asteroids (NEA) contain enou…
Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis
Alyson K. Fletcher, Sundeep Rangan, Subrata Sarkar +1
Estimating a vector from noisy linear measurements often requires use of prior knowledge or structural constraints on for accurat…
Sketched Clustering via Hybrid Approximate Message Passing
Evan Byrne, Antoine Chatalic, Remi Gribonval +1
In sketched clustering, a dataset of samples is first sketched down to a vector of modest size, from which the centroids are subsequently extracted. Advantages include i) reduc…
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems
Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Philip Schniter +1
The problem of estimating a random vector x from noisy linear measurements y = A x + w with unknown parameters on the distributions of x and w, which must also be learned, arises i…
A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm
Maher Al-Shoukairi, Philip Schniter, Bhaskar D. Rao
In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Max…
Vector Approximate Message Passing
Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
The standard linear regression (SLR) problem is to recover a vector from noisy linear observations . The approximate message pas…
Expectation Consistent Approximate Inference: Generalizations and Convergence
Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Sundeep Rangan +1
Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference pro…
Parametric Bilinear Generalized Approximate Message Passing
Jason T. Parker, Philip Schniter
We propose a scheme to estimate the parameters and of the bilinear form from noisy measurements , where and…
Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing
Justin Ziniel, Philip Schniter, Per Sederberg
For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (…
Hybrid Approximate Message Passing
Sundeep Rangan, Alyson K. Fletcher, Vivek K. Goyal +2
Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable recent attention due to their computational simplicity, analytic tractabil…