227 citations · 786 across the 29 of their papers we have counts for
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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…
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…
Learning and Free Energies for Vector Approximate Message Passing
Alyson K. Fletcher, Philip Schniter
Vector approximate message passing (VAMP) is a computationally simple approach to the recovery of a signal from noisy linear measurements $\mathbf{y}=\mathbf{Ax}+\math…
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…