Compressive imaging via approximate message passing with image denoising
We consider compressive imaging problems, where images are reconstructed from a
reduced number of linear measurements. Our objective is to improve over existing …
reduced number of linear measurements. Our objective is to improve over existing …
Contact tracing enhances the efficiency of COVID-19 group testing
Group testing can save testing resources in the context of the ongoing COVID-19 pandemic.
In group testing, we are given n samples, one per individual, and arrange them into m< n …
In group testing, we are given n samples, one per individual, and arrange them into m< n …
Group testing with side information via generalized approximate message passing
Group testing can help maintain a widespread testing program using fewer resources amid
a pandemic. In a group testing setup, we are given samples, one per individual. Each …
a pandemic. In a group testing setup, we are given samples, one per individual. Each …
A CVAE-within-Gibbs sampler for Bayesian linear inverse problems with hyperparameters
J Yang, Y Niu, Q Zhou - Computational and Applied Mathematics, 2023 - Springer
We propose a conditional variational auto-encoder within Gibbs sampling (CVAE-within-
Gibbs) for Bayesian linear inverse problems where the prior or the likelihood function …
Gibbs) for Bayesian linear inverse problems where the prior or the likelihood function …
Recovery from linear measurements with complexity-matching universal signal estimation
We study the compressed sensing (CS) signal estimation problem where an input signal is
measured via a linear matrix multiplication under additive noise. While this setup usually …
measured via a linear matrix multiplication under additive noise. While this setup usually …
Compressed sensing via universal denoising and approximate message passing
We study compressed sensing (CS) signal reconstruction problems where an input signal is
measured via matrix multiplication under additive white Gaussian noise. Our signals are …
measured via matrix multiplication under additive white Gaussian noise. Our signals are …
Statistical physics and information theory perspectives on linear inverse problems
J Zhu - arXiv preprint arXiv:1705.05070, 2017 - arxiv.org
Many real-world problems in machine learning, signal processing, and communications
assume that an unknown vector $ x $ is measured by a matrix A, resulting in a vector $ y …
assume that an unknown vector $ x $ is measured by a matrix A, resulting in a vector $ y …
Nonlinear Function Estimation with Empirical Bayes and Approximate Message Passing
Nonlinear function estimation is core to modern machine learning applications. In this paper,
to perform nonlinear function estimation, we reduce a nonlinear inverse problem to a linear …
to perform nonlinear function estimation, we reduce a nonlinear inverse problem to a linear …
Minimax Compressed Sensing Reconstruction
In compressive sensing, one basic issue is the robustness of signal recovery solutions in the
presence of uncertainties. The main objective of this project is to analysis the robustness of …
presence of uncertainties. The main objective of this project is to analysis the robustness of …