How deeply does Graph Neural Networks in Action cover variational autoencoder?

Explains encoders, decoders, latent distributions, and KL regularization, but does not derive the underlying mathematics.

Variational autoencoder

As explained in Graph Neural Networks in Action

A variational autoencoder is a probabilistic autoencoder that learns a parameterized latent-variable distribution and decodes samples from it to reconstruct or generate data.

A variational autoencoder (VAE) is a generative autoencoder that learns a structured, continuous latent distribution instead of a single fixed encoding. It can sample from that distribution to reconstruct training data or generate new examples. section 5.0 section 5.6

A VAE extends an autoencoder through a short pipeline:

Encodes data into a low-dimensional latent representation. section 5.6
Regularizes the learned latent distribution with a KL-divergence term against a prior distribution. section 5.6
Decodes samples from the latent distribution to reconstruct or generate data. section 5.0

Graph variational autoencoder

For graph data, a variational graph autoencoder (VGAE) uses an encoder and a decoder whose typical operation is a dot product over the edge list, supporting prediction of hidden or potential edges. section 5.6

How Graph Neural Networks in Action covers it

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Also explained in

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Generative AI in ActionA variational autoencoder is a generative neural model that encodes data into a probabilistic latent representation and decodes samples from that representation into reconstructed or newly generated data.
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Deep Learning with R, Third EditionA variational autoencoder is a probabilistic autoencoder whose encoder represents inputs as parameters of a latent probability distribution and whose decoder reconstructs or generates data from sampled latent points.
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Machine Learning Algorithms in DepthA variational autoencoder is a generative neural network that learns an approximate latent-variable posterior and reconstructs observations from sampled latent representations.
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Data Without LabelsA variational autoencoder is a neural-network generative model that learns a probabilistic latent representation and reconstructs data from sampled latent variables.

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Related concepts

encoderhas part · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.
autoencoderkind of · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
decodehas part · Decoding is the process of transforming an encoded or internal representation into an interpretable or target-form output.
generationkind of · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.
reconstruction lossuses · Reconstruction loss is a measure of the discrepancy between an input or observation and the output a model reconstructs from it or its representation.
image generationuses · Image generation is the computational creation of new images from learned models, input data, or specified conditions.

This explainer was generated by Manning's knowledge graph from the text of Graph Neural Networks in Action. The section links point to the parts of the book it draws on.