In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. It...

subsequent classification tasks, and Variational autoencoders, with applications as generative models. Autoencoders are applied to many problems, including...

exponential family. Variational message passing: a modular algorithm for variational Bayesian inference. Variational autoencoder: an artificial neural...

In variational Bayesian methods, the evidence lower bound (often abbreviated ELBO, also sometimes called the variational lower bound or negative variational...

the ViT. The idea is essentially the same as vector quantized variational autoencoder (VQVAE) plus generative adversarial network (GAN). After such a...

layers. Variational autoencoder These are inspired by Helmholtz machines and combines probability network with neural networks. An Autoencoder is a 3-layer...

are universal approximators, GANs are asymptotically consistent. Variational autoencoders might be universal approximators, but it is not proven as of 2017...

generative model for existing information from text can be done by variational autoencoder and generative adversarial network (GAN). There are different models...

contrast, many alternative generative modeling methods such as variational autoencoder (VAE) and generative adversarial network do not explicitly represent...

machine (e.g. Restricted Boltzmann machine, Deep belief network) Variational autoencoder Generative adversarial network Flow-based generative model Energy...