“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

How this entity was disambiguated

This entity first appeared as the object of triple T93176 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: “A fast learning algorithm for deep belief nets”
Context triple: [Geoffrey Hinton, notableWork, “A fast learning algorithm for deep belief nets”]

A. Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
B. “Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
C. Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
D. Lifelong Learning Machines program
The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
E. Google Brain
Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: “A fast learning algorithm for deep belief nets”
Target entity description: “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.

A. Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
B. “Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
C. Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
D. Lifelong Learning Machines program
The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
E. Google Brain
Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
F. None of above. chosen

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

How these facts were elicited

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” ⓘ