“A fast learning algorithm for deep belief nets”

E11232

deep learning paper machine learning paper scientific paper

“A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.

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All labels observed (3)

Label	Occurrences
A fast learning algorithm for deep belief nets	2
"A Fast Learning Algorithm for Deep Belief Nets"	1
“A fast learning algorithm for deep belief nets” canonical	1

Statements (44)

Predicate	Object
instanceOf	deep learning paper ⓘ machine learning paper ⓘ scientific paper ⓘ
architectureProperty	lower layers form a directed belief network ⓘ multiple layers of latent variables ⓘ top two layers form an undirected graphical model ⓘ
author	Geoffrey Hinton ⓘ surface form: Geoffrey E. Hinton Simon Osindero ⓘ Yee-Whye Teh ⓘ
citationStatus	highly cited ⓘ
contribution	demonstrated effective layer-wise unsupervised pretraining ⓘ made deep neural networks easier to train ⓘ showed that greedy learning of one layer at a time works well ⓘ
evaluationDataset	MNIST ⓘ
evaluationDomain	handwritten digit recognition ⓘ
field	artificial intelligence ⓘ deep learning ⓘ machine learning ⓘ
fineTuningMethod	backpropagation ⓘ
hasPage	https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf ⓘ
impact	influenced development of modern deep learning methods ⓘ revived interest in deep neural networks ⓘ
introducesConcept	deep belief network ⓘ greedy layer-wise pretraining ⓘ unsupervised pretraining for deep networks ⓘ
language	English ⓘ
learningType	probabilistic generative learning ⓘ
networkType	deep belief network ⓘ deep generative model ⓘ
optimizationMethod	contrastive divergence ⓘ
pretrainingRole	initializes weights for subsequent supervised fine-tuning ⓘ
proposesMethod	stacking restricted Boltzmann machines ⓘ
publicationYear	2006 ⓘ
publishedIn	Neural Computation ⓘ
relatedTo	Boltzmann machines ⓘ deep neural network training ⓘ energy-based models ⓘ
shows	deep belief nets can achieve low error rates on MNIST ⓘ
title	A fast learning algorithm for deep belief nets ⓘ
topic	representation learning ⓘ unsupervised feature learning ⓘ
trainingParadigm	generative modeling ⓘ unsupervised learning ⓘ
usesModel	Boltzmann machines ⓘ surface form: restricted Boltzmann machine

Referenced by (4)

Full triples — surface form annotated when it differs from this entity's canonical label.

Geoffrey Hinton → notableWork → “A fast learning algorithm for deep belief nets” ⓘ

Deep belief networks → describedIn → “A fast learning algorithm for deep belief nets” ⓘ

this entity surface form: "A Fast Learning Algorithm for Deep Belief Nets"

Simon Osindero → hasPublication → “A fast learning algorithm for deep belief nets” ⓘ

this entity surface form: A fast learning algorithm for deep belief nets

Simon Osindero → coAuthorOf → “A fast learning algorithm for deep belief nets” ⓘ

this entity surface form: A fast learning algorithm for deep belief nets