|
gptkbp:instance_of
|
gptkb:machine_learning
|
|
gptkbp:analyzes
|
Graph Structure
Energy Landscape
|
|
gptkbp:can_be_extended_by
|
gptkb:Deep_Boltzmann_Machines
gptkb:Variational_Autoencoders
|
|
gptkbp:can_be_used_for
|
Dimensionality Reduction
Feature Learning
Generative Modeling
|
|
gptkbp:consists_of
|
Hidden Units
Visible Units
|
|
gptkbp:developed_by
|
gptkb:Geoffrey_R._Hinton
|
|
gptkbp:has_applications_in
|
gptkb:Computer_Vision
gptkb:Natural_Language_Processing
Recommender Systems
|
|
gptkbp:has_limitations
|
Training Complexity
Sampling Difficulty
|
|
gptkbp:has_variants
|
gptkb:Deep_Boltzmann_Machines
Restricted Boltzmann Machines
Conditional Boltzmann Machines
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Boltzmann Machines
|
|
gptkbp:is_characterized_by
|
Energy Function
Stochastic Behavior
Binary States
|
|
gptkbp:is_evaluated_by
|
gptkb:BIC
Cross-Validation
Log-Likelihood
AIC
Free Energy
|
|
gptkbp:is_implemented_in
|
gptkb:Tensor_Flow
gptkb:Python
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
Bayesian Inference
Information Theory
Statistical Physics
|
|
gptkbp:is_part_of
|
gptkb:Artificial_Intelligence
gptkb:neural_networks
gptkb:statistical_mechanics
|
|
gptkbp:is_related_to
|
gptkb:Model
Markov Chains
|
|
gptkbp:is_used_in
|
gptkb:speeches
Anomaly Detection
Image Reconstruction
|
|
gptkbp:learns_by
|
Contrastive Divergence
|
|
gptkbp:model
|
Probability Distributions
Energy-Based Models
|
|
gptkbp:related_to
|
gptkb:Deep_Belief_Networks
Restricted Boltzmann Machines
|
|
gptkbp:requires
|
Training Data
|
|
gptkbp:training
|
Gibbs Sampling
Contrastive Divergence Algorithm
Mean Field Approximation
|
|
gptkbp:type_of
|
gptkb:Stochastic_Neural_Network
|
|
gptkbp:used_in
|
gptkb:Deep_Learning
|
|
gptkbp:bfsParent
|
gptkb:Geoffrey_R._Hinton
|
|
gptkbp:bfsLayer
|
3
|