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The goal of compressed sensing is to estimate a vector from an underdetermined system of noisy linear measurements, by making use of prior knowledge on the structure of vectors in the relevant domain.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J Candes, Justin K Romberg, and Terence Tao · 2006
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Compressed sensing
David L Donoho · 2006
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Sparse mri: The application of compressed sensing for rapid mr imaging
Michael Lustig, David Donoho, and John M Pauly · 2007
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Prior image constrained compressed sensing (piccs): a method to accurately reconstruct dynamic ct images from highly undersampled projection data sets
Guang-Hong Chen, Jie Tang, and Shuai Leng · 2008
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Single-pixel imaging via compressive sampling
Marco F Duarte, Mark A Davenport, Dharmpal Takbar, Jason N Laska, Ting Sun, Kevin F Kelly, and Richard G Baraniuk · 2008
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The fast johnson–lindenstrauss transform and approximate nearest neighbors
Nir Ailon and Bernard Chazelle · 2009
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Random projections of smooth manifolds
Richard G Baraniuk and Michael B Wakin · 2009
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Simultaneous analysis of lasso and dantzig selector
Peter J Bickel, Ya’acov Ritov, and Alexandre B Tsybakov · 2009
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Compressed sensing and best k-term approximation
A. Cohen, W. Dahmen, and R. DeVore · 2009
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Compressive sensing recovery of spike trains using a structured sparsity model
Chinmay Hegde, Marco F Duarte, and Volkan Cevher · 2009
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A unified framework for high-dimensional analysis of m m -estimators with decomposable regularizers
Sahand Negahban, Bin Yu, Martin J Wainwright, and Pradeep K Ravikumar · 2009
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Fast global convergence rates of gradient methods for high-dimensional statistical recovery
Alekh Agarwal, Sahand Negahban, and Martin J Wainwright · 2010
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Model-based compressive sensing
Richard G Baraniuk, Volkan Cevher, Marco F Duarte, and Chinmay Hegde · 2010
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Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
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Image super-resolution via sparse representation
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Compressed sensing and dictionary learning
Guangliang Chen and Deanna Needell · 2016
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Inverting the generator of a generative adversarial network
Antonia Creswell and Anil Anthony Bharath · 2016
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Francis Bach, Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, et al · 2012
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Signal recovery on incoherent manifolds
Chinmay Hegde and Richard G Baraniuk · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
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Adam: A method for stochastic optimization
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Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2016
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Adversarially learned inference
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Accurate image super-resolution using very deep convolutional networks
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Semantic image inpainting with perceptual and contextual losses
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Towards understanding the invertibility of convolutional neural networks
Anna C. Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, and Honglak Lee · 2017
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