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Florent Krzakala | Lenka Zdeborová | Thibault Lesieur | F. Krzakala | L. Zdeborová | T. Lesieur | Florent Krzakala
[1] C. Eckart,et al. The approximation of one matrix by another of lower rank , 1936 .
[2] S. Kirkpatrick,et al. Solvable Model of a Spin-Glass , 1975 .
[3] D. Thouless,et al. Spherical Model of a Spin-Glass , 1976 .
[4] R. Palmer,et al. Solution of 'Solvable model of a spin glass' , 1977 .
[5] G. Toulouse,et al. Coexistence of Spin-Glass and Ferromagnetic Orderings , 1981 .
[6] H. Sommers. Theory of a Heisenberg spin glass , 1981 .
[7] T. Plefka. Convergence condition of the TAP equation for the infinite-ranged Ising spin glass model , 1982 .
[8] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[9] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[10] Kanter,et al. Mean-field theory of the Potts glass. , 1985, Physical review letters.
[11] Giorgio Parisi,et al. SK Model: The Replica Solution without Replicas , 1986 .
[12] J. Yedidia,et al. How to expand around mean-field theory using high-temperature expansions , 1991 .
[13] J. Nadal,et al. Optimal unsupervised learning , 1994 .
[14] Michael Biehl,et al. Statistical mechanics of unsupervised structure recognition , 1994 .
[15] Sompolinsky,et al. Statistical mechanics of the maximum-likelihood density estimation. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[16] Hilbert J. Kappen,et al. Boltzmann Machine Learning Using Mean Field Theory and Linear Response Correction , 1997, NIPS.
[17] George M. Church,et al. Biclustering of Expression Data , 2000, ISMB.
[18] H. Nishimori. Statistical Physics of Spin Glasses and Information Processing , 2001 .
[19] D. Sherrington,et al. Absence of replica symmetry breaking in a region of the phase diagram of the Ising spin glass , 2000, cond-mat/0008139.
[20] 西森 秀稔. Statistical physics of spin glasses and information processing : an introduction , 2001 .
[21] William T. Freeman,et al. Understanding belief propagation and its generalizations , 2003 .
[22] S. Péché,et al. Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices , 2004, math/0403022.
[23] Larry Wasserman,et al. All of Statistics: A Concise Course in Statistical Inference , 2004 .
[24] Arlindo L. Oliveira,et al. Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[25] Huan Liu,et al. Subspace clustering for high dimensional data: a review , 2004, SKDD.
[26] M. Rattray,et al. Principal-component-analysis eigenvalue spectra from data with symmetry-breaking structure. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.
[27] J. Franklin,et al. The elements of statistical learning: data mining, inference and prediction , 2005 .
[28] R. Tibshirani,et al. Sparse Principal Component Analysis , 2006 .
[29] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[30] A. Montanari,et al. Rigorous Inequalities Between Length and Time Scales in Glassy Systems , 2006, cond-mat/0603018.
[31] M. Wainwright,et al. High-dimensional analysis of semidefinite relaxations for sparse principal components , 2008, 2008 IEEE International Symposium on Information Theory.
[32] I. Johnstone,et al. Sparse Principal Components Analysis , 2009, 0901.4392.
[33] Andrea Montanari,et al. Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.
[34] Santo Fortunato,et al. Community detection in graphs , 2009, ArXiv.
[35] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[36] Sundeep Rangan,et al. Estimation with random linear mixing, belief propagation and compressed sensing , 2010, 2010 44th Annual Conference on Information Sciences and Systems (CISS).
[37] Andrea Montanari,et al. The dynamics of message passing on dense graphs, with applications to compressed sensing , 2010, ISIT.
[38] Cristopher Moore,et al. Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[39] Adel Javanmard,et al. State Evolution for General Approximate Message Passing Algorithms, with Applications to Spatial Coupling , 2012, ArXiv.
[40] Sundeep Rangan,et al. Iterative estimation of constrained rank-one matrices in noise , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.
[41] Geoffrey E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.
[42] Elchanan Mossel,et al. Spectral redemption in clustering sparse networks , 2013, Proceedings of the National Academy of Sciences.
[43] Andrea Montanari,et al. Finding Hidden Cliques of Size \sqrt{N/e} in Nearly Linear Time , 2013, ArXiv.
[44] Philippe Rigollet,et al. Complexity Theoretic Lower Bounds for Sparse Principal Component Detection , 2013, COLT.
[45] Philippe Rigollet,et al. Computational Lower Bounds for Sparse PCA , 2013, ArXiv.
[46] Florent Krzakala,et al. Phase diagram and approximate message passing for blind calibration and dictionary learning , 2013, 2013 IEEE International Symposium on Information Theory.
[47] Toshiyuki Tanaka,et al. Low-rank matrix reconstruction and clustering via approximate message passing , 2013, NIPS.
[48] Volkan Cevher,et al. Fixed Points of Generalized Approximate Message Passing With Arbitrary Matrices , 2016, IEEE Transactions on Information Theory.
[49] Andrea Montanari,et al. Information-theoretically optimal sparse PCA , 2014, 2014 IEEE International Symposium on Information Theory.
[50] Andrea Montanari,et al. A statistical model for tensor PCA , 2014, NIPS.
[51] Florent Krzakala,et al. Variational free energies for compressed sensing , 2014, 2014 IEEE International Symposium on Information Theory.
[52] Volkan Cevher,et al. Bilinear Generalized Approximate Message Passing—Part I: Derivation , 2013, IEEE Transactions on Signal Processing.
[53] Florent Krzakala,et al. On convergence of approximate message passing , 2014, 2014 IEEE International Symposium on Information Theory.
[54] Florent Krzakala,et al. Phase transitions in sparse PCA , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).
[55] Andrea Montanari,et al. Finding Hidden Cliques of Size $$\sqrt{N/e}$$N/e in Nearly Linear Time , 2013, Found. Comput. Math..
[56] Sundeep Rangan,et al. Adaptive damping and mean removal for the generalized approximate message passing algorithm , 2014, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[57] Florent Krzakala,et al. Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer Free Energy , 2015, NIPS 2015.
[58] Florent Krzakala,et al. Statistical physics of inference: thresholds and algorithms , 2015, ArXiv.
[59] Florent Krzakala,et al. MMSE of probabilistic low-rank matrix estimation: Universality with respect to the output channel , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[60] R. Monasson,et al. Estimating the principal components of correlation matrices from all their empirical eigenvectors , 2015, 1503.00287.
[61] Andrea Montanari,et al. Finding One Community in a Sparse Graph , 2015, Journal of Statistical Physics.
[62] B. Nadler,et al. DO SEMIDEFINITE RELAXATIONS SOLVE SPARSE PCA UP TO THE INFORMATION LIMIT , 2013, 1306.3690.
[63] Florent Krzakala,et al. Inferring sparsity: Compressed sensing using generalized restricted Boltzmann machines , 2016, 2016 IEEE Information Theory Workshop (ITW).
[64] Marc Lelarge,et al. Recovering Asymmetric Communities in the Stochastic Block Model , 2018, IEEE Transactions on Network Science and Engineering.
[65] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[66] Florent Krzakala,et al. Mutual information in rank-one matrix estimation , 2016, 2016 IEEE Information Theory Workshop (ITW).
[67] Andrea Montanari,et al. Asymptotic mutual information for the binary stochastic block model , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).
[68] Jess Banks,et al. Phase transitions and optimal algorithms in high-dimensional Gaussian mixture clustering , 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[69] Ankur Moitra,et al. Message‐Passing Algorithms for Synchronization Problems over Compact Groups , 2016, ArXiv.
[70] Sebastian Fischer,et al. Exploring Artificial Intelligence In The New Millennium , 2016 .
[71] Andrea Montanari,et al. Sparse PCA via Covariance Thresholding , 2013, J. Mach. Learn. Res..
[72] Florent Krzakala,et al. Phase Transitions and Sample Complexity in Bayes-Optimal Matrix Factorization , 2014, IEEE Transactions on Information Theory.
[73] Ankur Moitra,et al. Optimality and Sub-optimality of PCA for Spiked Random Matrices and Synchronization , 2016, ArXiv.
[74] Nicolas Macris,et al. Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula , 2016, NIPS.
[75] Léo Miolane. Fundamental limits of low-rank matrix estimation , 2017 .
[76] Rémi Monasson,et al. Emergence of Compositional Representations in Restricted Boltzmann Machines , 2016, Physical review letters.
[77] M. Mézard. Mean-field message-passing equations in the Hopfield model and its generalizations. , 2016, Physical review. E.
[78] Marc Lelarge,et al. Fundamental limits of symmetric low-rank matrix estimation , 2016, Probability Theory and Related Fields.
[79] Nicolas Macris,et al. Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method , 2018, ArXiv.
[80] Jess Banks,et al. Information-theoretic bounds and phase transitions in clustering, sparse PCA, and submatrix localization , 2016, 2017 IEEE International Symposium on Information Theory (ISIT).