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In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling in 2013. It is part of the families of probabilistic graphical models and variational Bayesian methods.
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displaystyle distribution phi theta variational data latent network mathbb encoder space autoencoder vae sim function model neural noise mu real
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Variational autoencoder | is a | generative model with a prior and noise distribution respectively | 0.90 | text |
| IMAGENET is typically assumed to have a gaussianly distributed noise | instance of | a standard VAE task | 0.80 | text |
| Variational autoencoder | related to Evidence lower bound (ELBO) | Like | 0.60 | section |
| Variational autoencoder | related to Evidence lower bound (ELBO) | VAEs | 0.60 | section |
| Variational autoencoder | related to Evidence lower bound (ELBO) | For | 0.60 | section |
| Variational autoencoder | related to Evidence lower bound (ELBO) | As | 0.60 | section |
| Variational autoencoder | related to Further reading | Kingma | 0.60 | section |
| Variational autoencoder | related to Further reading | Diederik | 0.60 | section |
| Variational autoencoder | related to Further reading | Welling | 0.60 | section |
| Variational autoencoder | related to Further reading | Max | 0.60 | section |
| Variational autoencoder | related to Further reading | An Introduction | 0.60 | section |
| Variational autoencoder | related to Further reading | Variational Autoencoders | 0.60 | section |
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