Deep Learning Methods for Solving Linear Inverse Problems: Research
Directions and Paradigms

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms

27 July 2020

Jie Chen

Papers citing "Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms"

9 / 109 papers shown

Title
Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals Jun Fang Yanning Shen Hongbin Li Pu Wang CML 100 213 0 09 Nov 2013
Conditioning of Random Block Subdictionaries with Applications to Block-Sparse Recovery and Regression W. Bajwa Marco F. Duarte A. Calderbank 54 21 0 20 Sep 2013
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization Po-Yu Chen I. Selesnick 50 165 0 23 Aug 2013
Early stopping and non-parametric regression: An optimal data-dependent stopping rule Garvesh Raskutti Martin J. Wainwright Bin Yu 50 299 0 15 Jun 2013
ADADELTA: An Adaptive Learning Rate Method Matthew D. Zeiler ODL 93 6,619 0 22 Dec 2012
Learning efficient sparse and low rank models Pablo Sprechmann A. Bronstein Guillermo Sapiro 140 192 0 14 Dec 2012
Forest Sparsity for Multi-channel Compressive Sensing Cheng Chen Yeqing Li Junzhou Huang 34 25 0 20 Nov 2012
A Dirty Model for Multiple Sparse Regression A. Jalali Pradeep Ravikumar Sujay Sanghavi 64 47 0 29 Jun 2011
Message Passing Algorithms for Compressed Sensing D. Donoho A. Maleki Andrea Montanari 83 2,352 0 21 Jul 2009