Disambiguation evidence for “A fast learning algorithm for deep belief nets” via surface form
"A fast learning algorithm for deep belief nets"
As subject (44)
Triples where this entity appears as subject under the
label "A fast learning algorithm for deep belief nets".
| Predicate | Object |
|---|---|
| architectureProperty | lower layers form a directed belief network ⓘ |
| architectureProperty | multiple layers of latent variables ⓘ |
| architectureProperty | top two layers form an undirected graphical model ⓘ |
| author |
Geoffrey Hinton
ⓘ
surface form:
Geoffrey E. Hinton
|
| author | Simon Osindero ⓘ |
| author | Yee-Whye Teh ⓘ |
| citationStatus | highly cited ⓘ |
| contribution | demonstrated effective layer-wise unsupervised pretraining ⓘ |
| contribution | made deep neural networks easier to train ⓘ |
| contribution | showed that greedy learning of one layer at a time works well ⓘ |
| evaluationDataset | MNIST ⓘ |
| evaluationDomain | handwritten digit recognition ⓘ |
| field | artificial intelligence ⓘ |
| field | deep learning ⓘ |
| field | machine learning ⓘ |
| fineTuningMethod | backpropagation ⓘ |
| hasPage | https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf ⓘ |
| impact | influenced development of modern deep learning methods ⓘ |
| impact | revived interest in deep neural networks ⓘ |
| instanceOf | deep learning paper ⓘ |
| instanceOf | machine learning paper ⓘ |
| instanceOf | scientific paper ⓘ |
| introducesConcept | deep belief network ⓘ |
| introducesConcept | greedy layer-wise pretraining ⓘ |
| introducesConcept | unsupervised pretraining for deep networks ⓘ |
| language | English ⓘ |
| learningType | probabilistic generative learning ⓘ |
| networkType | deep belief network ⓘ |
| networkType | 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 ⓘ |
| relatedTo | deep neural network training ⓘ |
| relatedTo | 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 ⓘ |
| topic | unsupervised feature learning ⓘ |
| trainingParadigm | generative modeling ⓘ |
| trainingParadigm | unsupervised learning ⓘ |
| usesModel |
Boltzmann machines
ⓘ
surface form:
restricted Boltzmann machine
|
As object (2)
Triples where some other subject referred to this entity
as "A fast learning algorithm for deep belief nets".
Simon Osindero
→
coAuthorOf
→
"A fast learning algorithm for deep belief nets"
ⓘ
↳ resolves to “A fast learning algorithm for deep belief nets”
Simon Osindero
→
hasPublication
→
"A fast learning algorithm for deep belief nets"
ⓘ
↳ resolves to “A fast learning algorithm for deep belief nets”