Deep belief networks
E46988
artificial neural network architecture
deep generative model
probabilistic graphical model
representation learning model
unsupervised learning model
Deep belief networks are a class of deep generative neural network models composed of stacked layers of latent variables, typically built from restricted Boltzmann machines, used for unsupervised feature learning and representation.
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
artificial neural network architecture
ⓘ
deep generative model ⓘ probabilistic graphical model ⓘ representation learning model ⓘ unsupervised learning model ⓘ |
| assume |
directed connections in upper layers
ⓘ
undirected connections in lower layers ⓘ |
| basedOn | restricted Boltzmann machines ⓘ |
| canModel |
complex data distributions
ⓘ
high-dimensional data ⓘ |
| composedOf |
multiple layers of latent variables
ⓘ
stacked restricted Boltzmann machines ⓘ |
| describedIn |
“A fast learning algorithm for deep belief nets”
ⓘ
surface form:
"A Fast Learning Algorithm for Deep Belief Nets"
|
| developedBy |
Geoffrey Hinton
ⓘ
Simon Osindero ⓘ Yee-Whye Teh ⓘ |
| field |
artificial intelligence
ⓘ
machine learning ⓘ |
| hasProperty |
deep architecture
ⓘ
distributed representations ⓘ energy-based formulation ⓘ generative ⓘ hierarchical feature learning ⓘ layer-wise training ⓘ probabilistic ⓘ stochastic hidden units ⓘ unsupervised pretraining ⓘ |
| inspired | later deep learning pretraining methods ⓘ |
| introducedIn | 2006 ⓘ |
| publishedIn | Neural Computation ⓘ |
| relatedTo |
autoencoders
ⓘ
Boltzmann machines ⓘ
surface form:
deep Boltzmann machines
deep neural networks ⓘ energy-based models ⓘ restricted Boltzmann machines ⓘ variational autoencoders ⓘ |
| trainedBy |
backpropagation fine-tuning
ⓘ
contrastive divergence ⓘ greedy layer-wise pretraining ⓘ stochastic gradient descent ⓘ |
| usedFor |
classification
ⓘ
data generation ⓘ dimensionality reduction ⓘ dimensionality reduction for visualization ⓘ image recognition ⓘ pretraining deep neural networks ⓘ regression ⓘ representation learning ⓘ speech recognition ⓘ unsupervised feature learning ⓘ |
Referenced by (1)
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