Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015. ,
DOI : 10.1007/s10994-013-5335-x
Hierarchical Variational Models, International Conference on Machine Learning (ICML), 2016. ,
Community Structure in Time-Dependent, Multiscale, and Multiplex Networks, Science, vol.328, issue.5980, pp.876-878, 2010. ,
DOI : 10.1126/science.1184819
Information, physics, and computation, 2009. ,
DOI : 10.1093/acprof:oso/9780198570837.001.0001
A statistical-mechanics approach to large-system analysis of CDMA multiuser detectors, IEEE Transactions on Information Theory, vol.48, issue.11, pp.2888-2910, 2002. ,
DOI : 10.1109/TIT.2002.804053
Optimal Phase Transitions in Compressed Sensing, IEEE Transactions on Information Theory, vol.58, issue.10, pp.6241-6263, 2012. ,
DOI : 10.1109/TIT.2012.2205894
URL : http://arxiv.org/abs/1111.6822
The mutual information in random linear estimation, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016. ,
DOI : 10.1109/ALLERTON.2016.7852290
The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact, 2016 IEEE International Symposium on Information Theory (ISIT), pp.2016-665 ,
DOI : 10.1109/ISIT.2016.7541382
Solution of 'Solvable model of a spin glass', Philosophical Magazine, vol.35, issue.3, p.593, 1977. ,
DOI : 10.1103/PhysRevLett.35.1792
The space of interactions in neural networks: Gardner's computation with the cavity method, Phys.: Conference Series, pp.2181-012001, 1989. ,
DOI : 10.1088/0305-4470/22/12/018
Message-passing algorithms for compressed sensing, Proc. Natl. Acad. Sci, p.18914, 2009. ,
DOI : 10.1073/pnas.0909892106
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2767368
Generalized approximate message passing for estimation with random linear mixing, 2011 IEEE International Symposium on Information Theory Proceedings, pp.2168-2172, 2011. ,
DOI : 10.1109/ISIT.2011.6033942
Probabilistic reconstruction in compressed sensing: algorithms, phase diagrams, and threshold achieving matrices, Journal of Statistical Mechanics: Theory and Experiment, vol.2012, issue.08, p.8009, 2012. ,
DOI : 10.1088/1742-5468/2012/08/P08009
URL : https://hal.archives-ouvertes.fr/hal-00764645
Deep Exponential Families, AISTATS, pp.762-771, 2015. ,
The Dynamics of Message Passing on Dense Graphs, with Applications to Compressed Sensing, IEEE Transactions on Information Theory, vol.57, issue.2, pp.764-785, 2011. ,
DOI : 10.1109/TIT.2010.2094817
Understanding belief propagation and its generalizations Exploring artificial intelligence in the new millennium, pp.236-239, 2003. ,
Analysis of CDMA systems that are characterized by eigenvalue spectrum, Europhysics Letters (EPL), vol.76, issue.6, p.1193, 2006. ,
DOI : 10.1209/epl/i2006-10380-5
Support Recovery With Sparsely Sampled Free Random Matrices, IEEE Transactions on Information Theory, vol.59, issue.7, pp.4243-4271, 2013. ,
DOI : 10.1109/TIT.2013.2250578
URL : http://arxiv.org/abs/1208.5269
Signal recovery using expectation consistent approximation for linear observations, 2014 IEEE International Symposium on Information Theory, p.226, 2014. ,
DOI : 10.1109/ISIT.2014.6874828
URL : http://arxiv.org/abs/1401.5151
S-AMP: Approximate message passing for general matrix ensembles, 2014 IEEE Information Theory Workshop (ITW), pp.192-196, 2014. ,
Vector Approximate Message Passing, 2016. ,
Mean-field message-passing equations in the Hopfield model and its generalizations, Physical Review E, vol.95, issue.2, 2016. ,
DOI : 10.1103/PhysRevE.95.022117
Three unfinished works on the optimal storage capacity of networks, Journal of Physics A: Mathematical and General, vol.22, issue.12, p.1983, 1989. ,
DOI : 10.1088/0305-4470/22/12/004
Statistical mechanics of learning, 2001. ,
DOI : 10.1017/CBO9781139164542