An Approximate Message Passing Framework for Side Information
暂无分享,去创建一个
You Zhou | Deanna Needell | Dror Baron | Anna Ma | Cynthia Rush | D. Needell | D. Baron | A. Ma | Cynthia Rush | You Zhou
[1] R. Gallager. Information Theory and Reliable Communication , 1968 .
[2] Michael Unser,et al. Sparse Image Deconvolution with Message Passing , 2013 .
[3] Yonina C. Eldar,et al. Compressed sensing for longitudinal MRI: An adaptive-weighted approach. , 2014, Medical physics.
[4] Henry Arguello,et al. Code aperture optimization for spectrally agile compressive imaging. , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.
[5] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[6] Miguel R. D. Rodrigues,et al. On the design of linear projections for compressive sensing with side information , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).
[7] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..
[8] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[9] Dror Baron,et al. An Analysis of State Evolution for Approximate Message Passing with Side Information , 2019, 2019 IEEE International Symposium on Information Theory (ISIT).
[10] Miguel R. D. Rodrigues,et al. Compressed Sensing With Prior Information: Strategies, Geometry, and Bounds , 2017, IEEE Transactions on Information Theory.
[11] Michael Unser,et al. Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning , 2012, IEEE Transactions on Information Theory.
[12] Nicolas Macris,et al. Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation , 2017, IEEE Transactions on Information Theory.
[13] Galen Reeves,et al. The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).
[14] Yanting Ma,et al. Compressive Imaging via Approximate Message Passing With Image Denoising , 2014, IEEE Transactions on Signal Processing.
[15] Ramji Venkataramanan,et al. Finite Sample Analysis of Approximate Message Passing Algorithms , 2016, IEEE Transactions on Information Theory.
[16] Richard G. Baraniuk,et al. A new compressive imaging camera architecture using optical-domain compression , 2006, Electronic Imaging.
[17] Rayan Saab,et al. Weighted ℓ1-Minimization for Sparse Recovery under Arbitrary Prior Information , 2016, ArXiv.
[18] Ramji Venkataramanan,et al. Finite-sample analysis of Approximate Message Passing , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).
[19] Hassan Mansour,et al. Recovery Analysis for Weighted ℓ1-Minimization Using a Null Space Property , 2014, ArXiv.
[20] Ahmad Beirami,et al. Optimal Trade-offs in Multi-Processor Approximate Message Passing , 2016, 1601.03790.
[21] Florent Krzakala,et al. Probabilistic reconstruction in compressed sensing: algorithms, phase diagrams, and threshold achieving matrices , 2012, ArXiv.
[22] Florent Krzakala,et al. Streaming Bayesian inference: Theoretical limits and mini-batch approximate message-passing , 2017, 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[23] Andrea Montanari,et al. The dynamics of message passing on dense graphs, with applications to compressed sensing , 2010, ISIT.
[24] Deanna Needell,et al. Conditional approximate message passing with side information , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.
[25] Sundeep Rangan,et al. Plug in estimation in high dimensional linear inverse problems a rigorous analysis , 2018, NeurIPS.
[26] André Kaup,et al. Sparse signal recovery with multiple prior information: Algorithm and measurement bounds , 2018, Signal Process..
[27] Volkan Cevher,et al. Adaptive-Rate Reconstruction of Time-Varying Signals With Application in Compressive Foreground Extraction , 2016, IEEE Transactions on Signal Processing.
[28] Ahmad Beirami,et al. Performance trade-offs in multi-processor approximate message passing , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).
[29] P. Mohana Shankar,et al. Fading and Shadowing in Wireless Systems , 2011 .
[30] Wei Lu,et al. Modified-CS: Modifying compressive sensing for problems with partially known support , 2009, 2009 IEEE International Symposium on Information Theory.
[31] A. Robert Calderbank,et al. Classification and Reconstruction of High-Dimensional Signals From Low-Dimensional Features in the Presence of Side Information , 2014, IEEE Transactions on Information Theory.
[32] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[33] Sundeep Rangan,et al. Generalized approximate message passing for estimation with random linear mixing , 2010, 2011 IEEE International Symposium on Information Theory Proceedings.
[34] Xing Wang,et al. Approximate message passing-based compressed sensing reconstruction with generalized elastic net prior , 2013, Signal Process. Image Commun..
[35] André Kaup,et al. Performance Bounds for Sparse Signal Reconstruction with Multiple Side Information , 2016, ArXiv.
[36] Sergio Verdú,et al. Randomly spread CDMA: asymptotics via statistical physics , 2005, IEEE Transactions on Information Theory.
[37] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[38] Sundeep Rangan,et al. Vector approximate message passing , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).
[39] Soren Forchhammer,et al. Measurement Bounds for Sparse Signal Recovery With Multiple Side Information , 2017 .
[40] Desmond P. Taylor,et al. A Statistical Model for Indoor Multipath Propagation , 2007 .
[41] Yanting Ma,et al. Approximate Message Passing Algorithm With Universal Denoising and Gaussian Mixture Learning , 2015, IEEE Transactions on Signal Processing.
[42] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[43] Andrea Montanari,et al. Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.
[44] Florent Krzakala,et al. Approximate message-passing with spatially coupled structured operators, with applications to compressed sensing and sparse superposition codes , 2013, 1312.1740.
[45] Ralf R. Müller,et al. Iterative multiuser joint decoding: optimal power allocation and low-complexity implementation , 2004, IEEE Transactions on Information Theory.
[46] Toshiyuki Tanaka,et al. A statistical-mechanics approach to large-system analysis of CDMA multiuser detectors , 2002, IEEE Trans. Inf. Theory.
[47] Sundeep Rangan,et al. On the convergence of approximate message passing with arbitrary matrices , 2014, 2014 IEEE International Symposium on Information Theory.
[48] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[49] Andrea Montanari,et al. The dynamics of message passing on dense graphs, with applications to compressed sensing , 2010, 2010 IEEE International Symposium on Information Theory.
[50] Sundeep Rangan,et al. Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization , 2015, IEEE Transactions on Information Theory.
[51] YuanXin,et al. Classification and Reconstruction of High-Dimensional Signals From Low-Dimensional Features in the Presence of Side Information , 2016 .
[52] Philip Schniter,et al. Dynamic Compressive Sensing of Time-Varying Signals Via Approximate Message Passing , 2012, IEEE Transactions on Signal Processing.
[53] Jie Tang,et al. Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. , 2008, Medical physics.
[54] Andrea Montanari,et al. State Evolution for Approximate Message Passing with Non-Separable Functions , 2017, Information and Inference: A Journal of the IMA.
[55] Theodore S. Rappaport,et al. Rapid Fading Due to Human Blockage in Pedestrian Crowds at 5G Millimeter-Wave Frequencies , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.
[56] R. Gribonval,et al. Exact Recovery Conditions for Sparse Representations With Partial Support Information , 2013, IEEE Transactions on Information Theory.