|
gptkbp:instance_of
|
gptkb:machine_learning
|
|
gptkbp:applies_to
|
gptkb:Deep_Learning
|
|
gptkbp:can
|
Latent Variables
Complex Distributions
Hierarchical Representations
|
|
gptkbp:can_be_combined_with
|
gptkb:neural_networks
gptkb:Recurrent_Neural_Networks
|
|
gptkbp:can_be_used_for
|
Dimensionality Reduction
Feature Learning
|
|
gptkbp:can_create
|
New Data Samples
|
|
gptkbp:composed_of
|
Hidden Layers
Visible Layer
|
|
gptkbp:developed_by
|
gptkb:Geoffrey_R._Hinton
|
|
gptkbp:has_applications_in
|
gptkb:Natural_Language_Processing
Image Recognition
Recommendation Systems
|
|
gptkbp:has_limitations
|
Scalability Issues
Training Complexity
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Deep Boltzmann Machines
|
|
gptkbp:is_compared_to
|
gptkb:Support_Vector_Machines
gptkb:Traditional_Neural_Networks
Deep Neural Networks
|
|
gptkbp:is_evaluated_by
|
Log-Likelihood
Reconstruction Error
Benchmark Datasets
Synthetic Datasets
|
|
gptkbp:is_explored_in
|
Conferences
Research Papers
Thesis Works
|
|
gptkbp:is_implemented_in
|
gptkb:Tensor_Flow
gptkb:Python
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
gptkb:statistical_mechanics
Information Theory
|
|
gptkbp:is_part_of
|
gptkb:Artificial_Intelligence
gptkb:neural_networks
Generative Models
|
|
gptkbp:is_related_to
|
gptkb:Deep_Belief_Networks
gptkb:Variational_Autoencoders
|
|
gptkbp:is_trained_in
|
Contrastive Divergence
|
|
gptkbp:is_used_in
|
gptkb:speeches
Anomaly Detection
Game AI
|
|
gptkbp:related_to
|
Restricted Boltzmann Machines
|
|
gptkbp:requires
|
Large Datasets
|
|
gptkbp:training
|
Stochastic Gradient Descent
Batch Training
Mini-Batch Training
|
|
gptkbp:used_for
|
Unsupervised Learning
|
|
gptkbp:bfsParent
|
gptkb:Boltzmann_Machines
|
|
gptkbp:bfsLayer
|
4
|