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Compressed sensing is a signal processing method that acquires data directly in a compressed form.
Low-density parity check codes
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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
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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)
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Understanding Belief Propagation and Its Generalizations
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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
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Compressed sensing
Donoho D. L · 2006
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Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
Candès E., Romberg J. & Tao T · 2006
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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
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Bayesian Compressive Sensing
Ji S., Xue Y. & Carin L · 2008
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Compressed sensing and Bayesian experimental design
Seeger M. W. & Nickisch H · 2008
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On the iterative decoding of high rate LDPC codes with applications in compressed sensing
Zhang F. & Pfister H. D · 2008
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Modern Coding Theory (Cambridge University Press, 2008)
Richardson T. & Urbanke R · 2008
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Montanari A. & Bayati M · 2010
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Ré́nyi information dimension: fundamental limits of almost lossless analog compression
Wu Y. & Verdu S · 2010
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Bayesian Compressive Sensing Via Belief Propagation
Baron D., Sarvotham S. & Baraniuk R · 2010
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Statistical Mechanics of Compressed Sensing
Ganguli S. & Sompolinsky H · 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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Threshold saturation via spatial coupling: Why convolutional LDPC ensembles perform so well over the BEC
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Message-passing algorithms for compressed sensing
Donoho D. L., Maleki A. & Montanari A · 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.
A typical reconstruction limit of compressed sensing based on Lp-norm minimization
Kabashima Y., Wadayama T. & Tanaka T · 2009
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A single-letter characterization of optimal noisy compressed sensing
Guo D., Baron D. & Shamai S · 2009
Cited alongside, same era.
Information, Physics, and Computation (Oxford Press, Oxford, 2009)
Mézard M. & Montanari A · 2009
Cited alongside, same era.
Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing
Donoho D. & Tanner J · 2009
Cited alongside, same era.
Kudekar S., Richardson T. & Urbanke R · 2010
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The effect of spatial coupling on compressive sensing
Kudekar S. & Pfister H · 2010
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Coupled graphical models and their thresholds
Hassani S., Macris N. & Urbanke R · 2010
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Expectation-Maximization Bernoulli-Gaussian Approximate Message Passing
Vila J. P. & Schniter P · 2011
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A signal recovery algorithm for sparse matrix based compressed sensing (2011)
Kabashima Y. & Wadayama T · 2011
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On melting dynamics and the glass transition. II. Glassy dynamics as a melting process
Krzakala F. & Zdeborová L · 2011
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Statistical physics-based reconstruction in compressed sensing
Krzakala F., Mézard M., Sausset F., Sun Y. & Zdeborová L · 2012
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Universality in message passing algorithms (2012)
Bayati M., Lelarge M. & Montanari A · 2012
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Spatially Coupled Ensembles Universally Achieve Capacity under Belief Propagation (2012)
Kudekar S., Richardson T. & Urbanke R · 2012
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Subsampling at Information Theoretically Optimal Rates (2012)
Javanmard A. & Montanari A · 2012
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