Comparer des méthodes

	Machine de Boltzmann restreinte (RBM)	Réseau de croyance profond (DBN)
Domaine	Apprentissage profond	Apprentissage profond
Famille	Latent structure	Machine learning
Année d'origine	1986	2006
Auteur d'origine	Smolensky, P. (1986); popularised by Hinton, G. E. & Salakhutdinov, R. R. (2006)	Geoffrey Hinton, Simon Osindero & Yee-Whye Teh
Type	Generative energy-based probabilistic model	Generative probabilistic model
Source fondatrice	Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.
Alias	RBM, Harmonium, restricted Boltzmann machine, RBM generative model	DBN, Deep Generative Network, Stacked RBM Network, Derin İnanç Ağı
Apparentées	3	3
Résumé	A Restricted Boltzmann Machine is a two-layer generative probabilistic model consisting of visible (observed) and hidden (latent) binary units connected by an undirected bipartite graph with no within-layer connections. Originally introduced as the 'Harmonium' by Paul Smolensky in 1986 and powerfully revived by Geoffrey Hinton and Ruslan Salakhutdinov in their landmark 2006 Science paper, RBMs became historically pivotal as the building block for greedy layer-wise pre-training of Deep Belief Networks, restarting interest in deep neural networks after years of stagnation.	A Deep Belief Network is a generative probabilistic model composed of multiple layers of stochastic, latent variables. Introduced by Hinton, Osindero, and Teh in 2006, DBNs were among the first deep architectures to be trained efficiently. Each pair of adjacent layers forms a Restricted Boltzmann Machine, and the network is trained greedily, one layer at a time, before optional supervised fine-tuning. DBNs revived interest in deep learning and demonstrated that hierarchical feature learning from raw data is tractable.