|
gptkbp:instanceOf
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gptkb:model
gptkb:generative_model
|
|
gptkbp:application
|
data compression
image generation
representation learning
anomaly detection
|
|
gptkbp:canBe
|
dimensionality reduction
feature extraction
semi-supervised learning
data synthesis
data denoising
generating new samples
missing data imputation
|
|
gptkbp:category
|
gptkb:deep_generative_model
gptkb:model
gptkb:unsupervised_learning_algorithm
|
|
gptkbp:citation
|
gptkb:Kingma,_D.P._&_Welling,_M._(2013)._Auto-Encoding_Variational_Bayes.
arXiv:1312.6114
|
|
gptkbp:field
|
gptkb:artificial_intelligence
deep learning
unsupervised learning
|
|
gptkbp:hasComponent
|
latent space
decoder
encoder
|
|
gptkbp:hasConcept
|
variational inference
probabilistic modeling
reparameterization trick
encoder-decoder architecture
latent variable model
|
|
gptkbp:hasVariant
|
gptkb:beta-VAE
gptkb:disentangled_VAE
conditional variational autoencoder
|
|
gptkbp:input
|
data sample
|
|
gptkbp:introduced
|
gptkb:Diederik_P._Kingma
gptkb:Max_Welling
|
|
gptkbp:introducedIn
|
2013
|
|
gptkbp:lossFunction
|
reconstruction loss
KL divergence loss
|
|
gptkbp:objective
|
gptkb:Kullback-Leibler_divergence
evidence lower bound
|
|
gptkbp:openSource
|
gptkb:TensorFlow
gptkb:Keras
gptkb:PyTorch
|
|
gptkbp:output
|
gptkb:organization
latent representation
|
|
gptkbp:relatedTo
|
gptkb:generative_adversarial_networks
Bayesian inference
autoencoders
|
|
gptkbp:uses
|
neural networks
stochastic gradient descent
|
|
gptkbp:bfsParent
|
gptkb:GANs
|
|
gptkbp:bfsLayer
|
6
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
variational autoencoders
|