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We study the compressed sensing reconstruction problem for a broad class of random, band-diagonal sensing matrices.
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M. Bayati and A. Montanari, The dynamics of message passing on dense graphs, with applications to compressed sensing , IEEE Trans. on Inform. Theory 57
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E. Candés and M. Davenport, How well can we estimate a sparse vector? , arXiv:1104.5246v3
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D.L. Donoho, A. Maleki, and A. Montanari, The Noise Sensitivity Phase Transition in Compressed Sensing , IEEE Trans. on Inform. Theory 57
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P. Indyk, E. Price, and D.P. Woodruff, On the Power of Adaptivity in Sparse Recovery , IEEE Symposium on the Foundations of Computer Science, FOCS, October 2011
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Cited alongside, same era.
K. Do Ba, P. Indyk, E. Price, and D. P. Woodruff, Lower bounds for sparse recovery , Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’10, 2010, pp. 1190–1197
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D. Baron, S. Sarvotham, and R. Baraniuk, Bayesian Compressive Sensing Via Belief Propagation , IEEE Trans. on Signal Proc. 58
2010
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D. L. Donoho and J. Tanner, Counting the faces of randomly-projected hypercubes and orthants, with applications , Discrete & Computational Geometry 43
2010
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S.H. Hassani, N. Macris, and R. Urbanke, Coupled graphical models and their thresholds , Proceedings of IEEE Inform. Theory Workshop (Dublin), 2010
2010
Cited alongside, same era.
S. Kudekar, C. Measson, T. Richardson, and R. Urbanke, Threshold Saturation on BMS Channels via Spatial Coupling , Proceedings of the International Symposium on Turbo Codes and Iterative Information Processing (Brest), 2010
2010
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S. Kudekar and H.D. Pfister, The effect of spatial coupling on compressive sensing , 48th Annual Allerton Conference, 2010, pp. 347 –353
2010
Cited alongside, same era.
M. Lentmaier and G. P. Fettweis, On the thresholds of generalized LDPC convolutional codes based on protographs , IEEE Intl. Symp. on Inform. Theory (Austin, Texas), August 2010
2010
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2011
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2011
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S. Kudekar, T. Richardson, and R. Urbanke, Threshold Saturation via Spatial Coupling: Why Convolutional LDPC Ensembles Perform So Well over the BEC , IEEE Trans. on Inform. Theory 57
2011
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S. Rangan, Generalized Approximate Message Passing for Estimation with Random Linear Mixing , IEEE Intl. Symp. on Inform. Theory (St. Perersbourg), August 2011, pp. 2168 – 2172
2011
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J. Vila and P. Schniter, Expectation-maximization bernoulli-gaussian approximate message passing , Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), 2011
2011
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2012
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2012
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S. Kudekar, T. Richardson, and R. Urbanke, Spatially coupled ensembles universally achieve capacity under belief propagation , Information Theory Proceedings (ISIT), 2012 IEEE International Symposium on, IEEE, 2012, pp. 453–457
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A. Montanari, Graphical models concepts in compressed sensing , Compressed Sensing (Y.C. Eldar and G. Kutyniok, eds.), Cambridge University Press, 2012
2012
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