|
gptkbp:instanceOf
|
recurrent neural network
|
|
gptkbp:activatedBy
|
sigmoid
|
|
gptkbp:canBe
|
data compression
pattern recognition
dimensionality reduction
feature extraction
collaborative filtering
data reconstruction
generative tasks
|
|
gptkbp:canLearn
|
internal representations
|
|
gptkbp:component
|
weights
hidden layer
visible layer
biases
|
|
gptkbp:difficulty
|
computationally expensive
slow training
|
|
gptkbp:energyFunction
|
gptkb:Boltzmann_distribution
|
|
gptkbp:field
|
gptkb:statistical_mechanics
deep learning
neural networks
|
|
gptkbp:hasHiddenUnits
|
yes
|
|
gptkbp:hasModel
|
probability distributions
|
|
gptkbp:hasVisibleUnits
|
yes
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Boltzmann machines
|
|
gptkbp:inspiredBy
|
thermodynamics
statistical physics
|
|
gptkbp:introduced
|
gptkb:Geoffrey_Hinton
|
|
gptkbp:introducedIn
|
1985
|
|
gptkbp:learningRule
|
gptkb:Hebbian_learning
|
|
gptkbp:namedAfter
|
gptkb:Ludwig_Boltzmann
|
|
gptkbp:optimizationProblem
|
maximum likelihood estimation
|
|
gptkbp:relatedTo
|
gptkb:Hopfield_network
gptkb:Restricted_Boltzmann_machine
|
|
gptkbp:roadType
|
graph
|
|
gptkbp:samplingMethod
|
gptkb:Markov_Chain_Monte_Carlo
gptkb:Gibbs_sampling
|
|
gptkbp:state
|
binary
|
|
gptkbp:trainingAlgorithm
|
stochastic gradient descent
contrastive divergence
|
|
gptkbp:usedFor
|
dimensionality reduction
unsupervised learning
feature learning
generative modeling
|
|
gptkbp:usedIn
|
gptkb:artificial_intelligence
gptkb:machine_learning
|
|
gptkbp:variant
|
gptkb:Gaussian-Bernoulli_Boltzmann_machine
gptkb:Replicated_Softmax_Boltzmann_machine
gptkb:Deep_Boltzmann_machine
gptkb:Restricted_Boltzmann_machine
|
|
gptkbp:bfsParent
|
gptkb:Deep_energy-based_models
|
|
gptkbp:bfsLayer
|
6
|