227 citations · 786 across the 29 of their papers we have counts for
21 papers · 1 filter
Asymptotics of MAP Inference in Deep Networks
Parthe Pandit, Mojtaba Sahraee, Sundeep Rangan +1
Deep generative priors are a powerful tool for reconstruction problems with complex data such as images and text. Inverse problems using such models require solving an inference pr…
Bilinear Recovery using Adaptive Vector-AMP
Subrata Sarkar, Alyson K. Fletcher, Sundeep Rangan +1
We consider the problem of jointly recovering the vector and the matrix from noisy measurements $\boldsymbol{Y} = \boldsymbol{A}(\boldsymbol{b})\b…
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…
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…
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…
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…