Diagonal EP under variance-profile Gaussian matrices produces Gaussian-process dynamics with profile-dependent memory instead of conventional scalar state evolution.
hub
Message-passing algorithms for compressed sensing
4 Pith papers cite this work, alongside 2,477 external citations. Polarity classification is still indexing.
4
Pith papers citing it
2,477
external citations · Crossref
hub tools
representative citing papers
Diffusion-OAMP combines a pre-trained diffusion model with the OAMP algorithm under an SNR-matching rule to enable training-free reconstruction of compressed images transmitted over noisy wireless channels.
An empirical study proposing DeMUN, a memory-based unrolled network, and finding that intermediate loss and residual connections improve reconstruction while projector depth beyond five layers matters little.
A convex relaxation algorithm and two Bayesian inference algorithms recover sparse binary defect maps from underdetermined linear measurements, demonstrated with eddy-current imaging simulations.
citing papers explorer
-
Gaussian-Process Dynamics of Diagonal Expectation Propagation under Variance-Profile Gaussian Measurements
Diagonal EP under variance-profile Gaussian matrices produces Gaussian-process dynamics with profile-dependent memory instead of conventional scalar state evolution.
-
Diffusion-OAMP for Joint Image Compression and Wireless Transmission
Diffusion-OAMP combines a pre-trained diffusion model with the OAMP algorithm under an SNR-matching rule to enable training-free reconstruction of compressed images transmitted over noisy wireless channels.
-
Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems
An empirical study proposing DeMUN, a memory-based unrolled network, and finding that intermediate loss and residual connections improve reconstruction while projector depth beyond five layers matters little.
-
Extension of compressive sampling to binary vector recovery for model-based defect imaging
A convex relaxation algorithm and two Bayesian inference algorithms recover sparse binary defect maps from underdetermined linear measurements, demonstrated with eddy-current imaging simulations.