Record di evidenza del metodo

Restricted Boltzmann Machine

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.

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Restricted Boltzmann Machine (RBM) — Bipartite Generative Energy Model

Record tassonomico del metodo · latent-structure / deep-learning

Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507. · DOI 10.1126/science.1127647
Hinton, G. E. (2002). Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation, 14(8), 1771–1800. · DOI 10.1162/089976602760128018
Smolensky, P. (1986). Information Processing in Dynamical Systems: Foundations of Harmony Theory. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel Distributed Processing, Vol. 1 (pp. 194–281). MIT Press. · ISBN 978-0-262-68053-0
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 20). MIT Press. · ISBN 978-0-262-03561-3

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Used in the same domainAutoencodermachine-suggested · Relational suggestion, not evidence.Often confused withDeep Belief Networkmachine-suggested · Relational suggestion, not evidence.Used in the same domainVariational Autoencodermachine-suggested · Relational suggestion, not evidence.