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Compressed sensing is triggering a major evolution in signal acquisition.
Solution of ‘Solvable Model of a Spin-Glass’
Thouless, D. J., Anderson, P. W. & Palmer, R. G · 1977
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Maximum Likelihood from Incomplete Data via the EM Algorithm
Dempster, A., Laird, N. & Rubin, D · 1977
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Reverend Bayes on inference engines: A distributed hierarchical approach
Pearl, J · 1982
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Spin-Glass Theory and Beyond , vol. 9 of Lecture Notes in Physics (World Scientific, Singapore, 1987)
Mézard, M., Parisi, G. & Virasoro, M. A · 1987
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Sparse Approximate Solutions to Linear Systems
Natarajan, B. K · 1995
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Time-varying periodic convolutional codes with low-density parity-check matrix
Jimenez Felstrom, A. & Zigangirov, K · 1999
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The Nishimori line and Bayesian statistics
Iba, Y · 1999
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Factor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. & Loeliger, H.-A · 2001
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Statistical Physics of Spin Glasses and Information Processing (Oxford University Press, Oxford, 2001)
Nishimori, H · 2001
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A statistical-mechanics approach to large-system analysis of CDMA multiuser detectors
Tanaka, T · 2002
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Understanding Belief Propagation and Its Generalizations
Yedidia, J., Freeman, W. & Weiss, Y · 2003
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Decoding by linear programming
Candès, E. J. & Tao, T · 2005
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Neighborliness of randomly projected simplices in high dimensions
Donoho, D. L. & Tanner, J · 2005
Cited alongside, same era.
Randomly spread CDMA: Asymptotics via statistical physics
Guo, D. & Verdú, S · 2005
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Compressed sensing
Donoho, D. L · 2006
Cited alongside, same era.
Asymptotic Mean-Square Optimality of Belief Propagation for Sparse Linear Systems
Guo, D. & Wang, C.-C · 2006
Cited alongside, same era.
An Introduction To Compressive Sampling
Candès, E. J. & Wakin, M. B · 2008
Cited alongside, same era.
Single-pixel imaging via compressive sampling
Duarte, M. F., Davenport, M.A., Takhar, D., Laska, J.N., Ting Sun, Kelly, K.F. & Baraniuk, R.G · 2008
Cited alongside, same era.
Statistical Mechanics of Compressed Sensing
Ganguli, S. & Sompolinsky, H · 2010
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Threshold saturation via spatial coupling: Why convolutional LDPC ensembles perform so well over the BEC
Kudekar, S., Richardson, T. & Urbanke, R · 2010
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On the thresholds of generalized LDPC convolutional codes based on protographs
Lentmaier, M. & Fettweis, G · 2010
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Coupled graphical models and their thresholds
Hassani, S., Macris, N. & Urbanke, R · 2010
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Message passing algorithms for compressed sensing: I. motivation and construction
Donoho, D., Maleki, A. & Montanari, A · 2010
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Estimation with random linear mixing, belief propagation and compressed sensing
Rangan, S · 2010
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Modern Coding Theory (Cambridge University Press, 2008)
Richardson, T. & Urbanke, R · 2008
Cited alongside, same era.
A single-letter characterization of optimal noisy compressed sensing
Guo, D., Baron, D. & Shamai, S · 2009
Cited alongside, same era.
Message-passing algorithms for compressed sensing
Donoho, D. L., Maleki, A. & Montanari, A · 2009
Cited alongside, same era.
A typical reconstruction limit of compressed sensing based on Lp-norm minimization
Kabashima, Y., Wadayama, T. & Tanaka, T · 2009
Cited alongside, same era.
Asymptotic Analysis of MAP Estimation via the Replica Method and Applications to Compressed Sensing
Rangan, S., Fletcher, A. & Goyal, V · 2009
Cited alongside, same era.
Information, Physics, and Computation (Oxford Press, Oxford, 2009)
Mézard, M. & Montanari, A · 2009
Cited alongside, same era.
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The effect of spatial coupling on compressive sensing
Kudekar, S. & Pfister H · 2010
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Generalized Approximate Message Passing for Estimation with Random Linear Mixing
Rangan, S · 2011
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The dynamics of message passing on dense graphs, with applications to compressed sensing
Montanari, A. & Bayati, M · 2011
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Inference and Phase Transitions in the Detection of Modules in Sparse Networks
Decelle, A., Krzakala, F., Moore, C. & Zdeborová, L · 2011
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Donoho, D. L., Javanmard, A. & Montanari, A · 2011
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