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gptkbp:instanceOf
|
gptkb:model
generative model
|
|
gptkbp:abbreviation
|
gptkb:VAE
|
|
gptkbp:application
|
drug discovery
image generation
speech synthesis
semi-supervised learning
representation learning
molecular design
|
|
gptkbp:basedOn
|
autoencoders
variational inference
|
|
gptkbp:category
|
gptkb:artificial_intelligence
gptkb:machine_learning
probabilistic models
|
|
gptkbp:citation
|
https://arxiv.org/abs/1312.6114
|
|
gptkbp:decoderNetwork
|
gptkb:convolutional_neural_network
|
|
gptkbp:encoderNetwork
|
gptkb:convolutional_neural_network
|
|
gptkbp:feature
|
reparameterization trick
probabilistic encoding
continuous latent space
probabilistic decoding
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Variational Autoencoders
|
|
gptkbp:introduced
|
gptkb:Diederik_P._Kingma
gptkb:Max_Welling
|
|
gptkbp:introducedIn
|
2013
|
|
gptkbp:latentSpace
|
probabilistic
|
|
gptkbp:latentVariable
|
gptkb:Gaussian_distribution
|
|
gptkbp:lossFunction
|
gptkb:KL_divergence
reconstruction loss
|
|
gptkbp:notableContributor
|
gptkb:Diederik_P._Kingma
gptkb:Max_Welling
|
|
gptkbp:notablePaperYear
|
2013
|
|
gptkbp:notablePublication
|
gptkb:Auto-Encoding_Variational_Bayes
|
|
gptkbp:objective
|
ELBO
evidence lower bound
|
|
gptkbp:openSource
|
gptkb:TensorFlow
gptkb:Keras
gptkb:PyTorch
|
|
gptkbp:relatedTo
|
gptkb:Generative_Adversarial_Networks
Bayesian inference
Autoencoders
|
|
gptkbp:trainer
|
backpropagation
stochastic gradient descent
|
|
gptkbp:usedFor
|
dimensionality reduction
unsupervised learning
generative modeling
anomaly detection
data generation
|
|
gptkbp:bfsParent
|
gptkb:Max_Welling
|
|
gptkbp:bfsLayer
|
6
|