Comparer des méthodes

	Réseau de croyance profond (DBN)	Machine de Boltzmann restreinte (RBM)
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Latent structure
Année d'origine	2006	1986
Auteur d'origine	Geoffrey Hinton, Simon Osindero & Yee-Whye Teh	Smolensky, P. (1986); popularised by Hinton, G. E. & Salakhutdinov, R. R. (2006)
Type	Generative probabilistic model	Generative energy-based probabilistic model
Source fondatrice	Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.	Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.
Alias	DBN, Deep Generative Network, Stacked RBM Network, Derin İnanç Ağı	RBM, Harmonium, restricted Boltzmann machine, RBM generative model
Apparentées	3	3
Résumé	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.	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.