527 citations · 3.5k across the 52 of their papers we have counts for
7 papers · 2 filters
Vector Approximate Message Passing for the Generalized Linear Model
Philip Schniter, Sundeep Rangan, Alyson K. Fletcher
The generalized linear model (GLM), where a random vector is observed through a noisy, possibly nonlinear, function of a linear transform output $\boldsymbol{z}=\b…
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
Mark Borgerding, Philip Schniter, Sundeep Rangan
Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse proble…
Denoising based Vector Approximate Message Passing
Philip Schniter, Sundeep Rangan, Alyson Fletcher
The denoising-based approximate message passing (D-AMP) methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D int…
Channel Estimation in Broadband Millimeter Wave MIMO Systems with Few-Bit ADCs
Jianhua Mo, Philip Schniter, Robert W. Heath
We develop a broadband channel estimation algorithm for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters (ADCs). Our…
Onsager-corrected deep learning for sparse linear inverse problems
Mark Borgerding, Philip Schniter
Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse proble…
Phase diagram of matrix compressed sensing
Christophe Schülke, Philip Schniter, Lenka Zdeborová
In the problem of matrix compressed sensing we aim to recover a low-rank matrix from few of its element-wise linear projections. In this contribution we analyze the asymptotic perf…