In Parallel Distributed Processing: Volume 1 by D. Rumelhart and J. McLelland , chapter 6: Information Processing in Dynamical Systems: Foundations of Harmony Theory, pp.194-281, 1986. ,
Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.313504-507, 2006. ,
DOI : 10.1126/science.1127647
Deep Boltzmann machines, Artificial Intelligence and Statistics, pp.448-455, 2009. ,
Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, vol.22, issue.8, pp.1771-1800, 2002. ,
DOI : 10.1162/089976600300015385
Training restricted Boltzmann machines using approximations to the likelihood gradient, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.1064-1071, 2008. ,
DOI : 10.1145/1390156.1390290
A Practical Guide to Training Restricted Boltzmann Machines, pp.599-619, 2012. ,
DOI : 10.1145/1390156.1390290
URL : http://learning.cs.toronto.edu/%7Ehinton/absps/guideTR.pdf
Nonequilibrium thermodynamics of restricted Boltzmann machines, Physical Review E, vol.96, issue.2, p.22131, 2017. ,
DOI : 10.1023/A:1008923215028
Neural networks and physical systems with emergent collective computational abilities., Proceedings of the National Academy of Sciences, vol.79, issue.8, pp.2554-2558, 1982. ,
DOI : 10.1073/pnas.79.8.2554
Statistical mechanics of neural networks near saturation, Annals of Physics, vol.173, issue.1, pp.30-67, 1987. ,
DOI : 10.1016/0003-4916(87)90092-3
Maximum Storage Capacity in Neural Networks, Europhysics Letters (EPL), vol.4, issue.4, p.481, 1987. ,
DOI : 10.1209/0295-5075/4/4/016
Optimal storage properties of neural network models, Journal of Physics A: Mathematical and General, vol.21, issue.1, p.271, 1988. ,
DOI : 10.1088/0305-4470/21/1/031
On the equivalence of Hopfield networks and Boltzmann Machines, Neural Networks, vol.34, pp.1-9, 2012. ,
DOI : 10.1016/j.neunet.2012.06.003
Training restricted Boltzmann machines via the Thouless-Anderson-Palmer free energy, Proceedings of the 28th International Conference on Neural Information Processing Systems, NIPS'15, pp.640-648, 2015. ,
Advanced mean-field theory of the restricted Boltzmann machine, Physical Review E, vol.91, issue.5, p.50101, 2015. ,
DOI : 10.1088/1742-5468/2014/05/P05020
Mean-Field Inference in Gaussian Restricted Boltzmann Machine, Journal of the Physical Society of Japan, vol.85, issue.3, p.34001, 2016. ,
DOI : 10.7566/JPSJ.85.034001
URL : http://arxiv.org/pdf/1512.00927
Learning multiple belief propagation fixed points for real time inference. Physica A: Statistical Mechanics and its Applications, pp.149-163, 2010. ,
DOI : 10.1016/j.physa.2009.08.030
URL : https://hal.archives-ouvertes.fr/inria-00371372
Phase diagram of restricted Boltzmann machines and generalized Hopfield networks with arbitrary priors, 2017. ,
Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses, Journal of Statistical Mechanics: Theory and Experiment, vol.2017, issue.5, p.2017053302, 2017. ,
DOI : 10.1088/1742-5468/aa6ddc
Multitasking Associative Networks, Multitasking associative networks, p.268101, 2012. ,
DOI : 10.1088/0305-4470/36/37/302
URL : http://arxiv.org/pdf/1111.5191
Emergence of compositional representations in restricted Boltzmann machines, Phys. Rev. Let, vol.118, p.138301, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01555107
Statistical physics of inference: thresholds and algorithms, Advances in Physics, vol.19, issue.5, pp.453-552, 2016. ,
DOI : 10.1214/009117905000000233
Mixtures of Probabilistic Principal Component Analyzers, Neural Computation, vol.2, issue.1, pp.443-482, 1999. ,
DOI : 10.1007/BF00162527
Auto-association by multilayer perceptrons and singular value decomposition, Biological Cybernetics, vol.13, issue.4-5, pp.291-294, 1988. ,
DOI : 10.1109/MASSP.1987.1165576
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, 2014. ,
Spectral dynamics of learning in restricted Boltzmann machines, EPL (Europhysics Letters), vol.119, issue.6, p.60001, 2017. ,
DOI : 10.1209/0295-5075/119/60001
A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines, 2017. ,
DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES, Mathematics of the USSR-Sbornik, vol.1, issue.4, p.457, 1967. ,
DOI : 10.1070/SM1967v001n04ABEH001994
Mean-field message-passing equations in the Hopfield model and its generalizations, Physical Review E, vol.6, issue.2, p.22117, 2017. ,
DOI : 10.1103/PhysRevLett.102.238701
Mean-field equations for spin models with orthogonal interaction matrices, Journal of Physics A: Mathematical and General, vol.28, issue.18, p.5267, 1995. ,
DOI : 10.1088/0305-4470/28/18/016
URL : http://arxiv.org/pdf/cond-mat/9503009
Adaptive and self-averaging Thouless-Anderson-Palmer mean-field theory for probabilistic modeling, Physical Review E, vol.77, issue.5, p.56131, 2001. ,
DOI : 10.1103/PhysRevLett.77.4671
Spin-glass models of neural networks, Physical Review A, vol.5, issue.2, pp.1007-1018, 1985. ,
DOI : 10.1088/0305-4608/5/5/017
Spin Glass Theory and Beyond, World Scientific, 1987. ,
Stability of the Sherrington-Kirkpatrick solution of a spin glass model, Journal of Physics A: Mathematical and General, vol.11, issue.5, pp.983-990, 1978. ,
DOI : 10.1088/0305-4470/11/5/028
An introduction to pattern formation in nonequilibrium systems, pp.55-92, 1987. ,
DOI : 10.1007/3-540-17206-8_3
On the criticality of inferred models, Journal of Statistical Mechanics: Theory and Experiment, vol.2011, issue.10, pp.2011-10012, 2011. ,
DOI : 10.1088/1742-5468/2011/10/P10012