Salīdzināt metodes

	Ierobežota Boltzmanna mašīna (RBM)	Autoencoder
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Latent structure	Machine learning
Izcelsmes gads	1986	2006
Autors	Smolensky, P. (1986); popularised by Hinton, G. E. & Salakhutdinov, R. R. (2006)	Hinton, G.E. & Salakhutdinov, R.R.
Tips	Generative energy-based probabilistic model	Neural network (encoder-decoder)
Pirmavots	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.
Citi nosaukumi	RBM, Harmonium, restricted Boltzmann machine, RBM generative model	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network
Saistītās	3	4
Kopsavilkums	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.