Methoden vergelijken

	Restricted Boltzmann Machine (RBM)	Auto-encoder	Diep Geloofsnetwerk (DBN)
Vakgebied	Deep learning	Deep learning	Deep learning
Familie	Latent structure	Machine learning	Machine learning
Jaar van ontstaan	1986	2006	2006
Grondlegger	Smolensky, P. (1986); popularised by Hinton, G. E. & Salakhutdinov, R. R. (2006)	Hinton, G.E. & Salakhutdinov, R.R.	Geoffrey Hinton, Simon Osindero & Yee-Whye Teh
Type	Generative energy-based probabilistic model	Neural network (encoder-decoder)	Generative probabilistic model
Oorspronkelijke bron	Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	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.
Aliassen	RBM, Harmonium, restricted Boltzmann machine, RBM generative model	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	DBN, Deep Generative Network, Stacked RBM Network, Derin İnanç Ağı
Verwant	3	4	3
Samenvatting	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.	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	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.