Boltzmann machine

URI: https://gptkb.org/entity/Boltzmann_machine

GPTKB entity

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Statements (154)

Predicate	Object
gptkbp:instanceOf	gptkb:convolutional_neural_network energy-based model probabilistic graphical model
gptkbp:activatedBy	sigmoid sigmoid function
gptkbp:alsoKnownAs	gptkb:Bayes_network gptkb:Bayesian_belief_network gptkb:belief_network
gptkbp:alternativeName	Autoencoder Bayesian_network Restricted_Boltzmann_Machine
gptkbp:application	gptkb:gene_regulatory_networks computer vision information retrieval robotics speech recognition bioinformatics risk analysis diagnosis
gptkbp:canBe	optimization pattern recognition data representation probabilistic inference learned from data specified by experts
gptkbp:component	edges represent conditional dependencies nodes represent random variables
gptkbp:energyFunction	gptkb:Boltzmann_distribution E(v,h) = -a^Tv - b^Th - v^TWh
gptkbp:extendsTo	gptkb:Deep_Boltzmann_machine gptkb:Replicated_Softmax_model gptkb:Restricted_Boltzmann_machine
gptkbp:field	gptkb:artificial_intelligence gptkb:machine_learning computational neuroscience deep learning Machine learning Deep learning
gptkbp:generalizes	gptkb:Hopfield_network
gptkbp:hasApplication	dimensionality reduction feature extraction collaborative filtering image modeling speech modeling
gptkbp:hasComponent	gptkb:Decoder hidden units stochastic binary units visible units Encoder
gptkbp:hasGraphStructure	fully connected
gptkbp:hasLayerType	hidden layer visible layer
gptkbp:hasLearningRule	gptkb:Gibbs_sampling gptkb:Hebbian_learning
gptkbp:hasModel	joint probability distribution
gptkbp:hasNoDirectConnectionsBetween	hidden units visible units
gptkbp:hasNoIntraLayerConnections	true
gptkbp:hasVariant	gptkb:Conditional_RBM gptkb:Gaussian-Bernoulli_RBM gptkb:Multinomial_RBM gptkb:Replicated_Softmax_RBM
gptkbp:hiddenUnits	h
gptkbp:includesState	binary
gptkbp:input	gptkb:Vector binary
gptkbp:inspiredBy	gptkb:statistical_mechanics gptkb:Deep_Belief_Network
gptkbp:introduced	gptkb:Geoffrey_Hinton gptkb:Terry_Sejnowski
gptkbp:introducedIn	1980s 1985 1986
gptkbp:inventedBy	gptkb:Geoffrey_Hinton
gptkbp:isBipartite	true
gptkbp:isGenerative	true
gptkbp:isStochastic	true
gptkbp:isUndirected	true
gptkbp:limitation	computationally expensive difficult to scale slow training cannot represent cyclic dependencies complexity increases with number of variables structure learning is NP-hard parameter estimation can be difficult with sparse data cannot model sequential data directly difficult to train for large networks
gptkbp:namedAfter	gptkb:Ludwig_Boltzmann
gptkbp:optimizedFor	stochastic gradient descent
gptkbp:originatedIn	gptkb:Judea_Pearl
gptkbp:output	gptkb:Vector binary
gptkbp:parameter	weights biases
gptkbp:probabilisticModel	gptkb:Markov_Random_Field
gptkbp:property	encodes joint probability distribution supports causal discovery supports efficient inference algorithms supports learning from incomplete data supports parameter learning supports structure learning
gptkbp:reduces	Reconstruction error
gptkbp:relatedTo	gptkb:Principal_component_analysis gptkb:artificial_intelligence gptkb:probability_theory gptkb:Markov_network gptkb:Boltzmann_Machine gptkb:Deep_Belief_Network gptkb:Hopfield_network gptkb:Restricted_Boltzmann_machine Boltzmann machine statistics graph theory causal inference Neural network
gptkbp:represents	set of variables and their conditional dependencies
gptkbp:samplingMethod	gptkb:Gibbs_sampling
gptkbp:stackable	true
gptkbp:supportsAlgorithm	gptkb:Markov_Chain_Monte_Carlo gptkb:Gibbs_sampling belief propagation expectation-maximization variable elimination
gptkbp:trainer	Unsupervised learning
gptkbp:trainingAlgorithm	stochastic gradient descent contrastive divergence
gptkbp:usedFor	gptkb:dictionary gptkb:machine_learning decision making diagnosis dimensionality reduction unsupervised learning collaborative filtering feature learning generative modeling probabilistic inference prediction Data compression causal reasoning Dimensionality reduction Denoising Anomaly detection Feature learning
gptkbp:usedIn	gptkb:Netflix_Prize unsupervised learning pretraining deep networks
gptkbp:uses	gptkb:directed_acyclic_graph
gptkbp:variant	gptkb:Contractive_autoencoder gptkb:Denoising_autoencoder gptkb:Sparse_autoencoder gptkb:Variational_autoencoder
gptkbp:visibleUnits	v
gptkbp:bfsParent	gptkb:Geoffrey_Hinton
gptkbp:bfsLayer	4