|
gptkbp:instanceOf
|
gptkb:convolutional_neural_network
|
|
gptkbp:activatedBy
|
sigmoid
|
|
gptkbp:canBe
|
pattern recognition
data representation
data generation
|
|
gptkbp:component
|
hidden units
visible units
connections (edges)
|
|
gptkbp:energyFunction
|
energy-based model
|
|
gptkbp:field
|
gptkb:artificial_intelligence
gptkb:machine_learning
computational neuroscience
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Boltzmann Machine
|
|
gptkbp:inspiredBy
|
gptkb:statistical_mechanics
|
|
gptkbp:introduced
|
gptkb:Geoffrey_Hinton
gptkb:Terry_Sejnowski
|
|
gptkbp:introducedIn
|
1985
|
|
gptkbp:limitation
|
scalability issues
slow training
difficult convergence
|
|
gptkbp:namedAfter
|
gptkb:Ludwig_Boltzmann
|
|
gptkbp:probabilisticModel
|
yes
|
|
gptkbp:relatedTo
|
gptkb:Deep_Belief_Network
Boltzmann machine
|
|
gptkbp:roadType
|
recurrent neural network
|
|
gptkbp:trainingAlgorithm
|
stochastic gradient descent
contrastive divergence
|
|
gptkbp:type
|
unsupervised learning
binary stochastic unit
|
|
gptkbp:usedFor
|
dimensionality reduction
unsupervised learning
feature learning
generative modeling
|
|
gptkbp:bfsParent
|
gptkb:convolutional_neural_network
gptkb:Boltzmann_machine
|
|
gptkbp:bfsLayer
|
5
|