… variational lower bound yields a simple differentiable unbiased estimator of the lower bound;
this SGVB (Stochastic Gradient Variational Bayes… , we propose the AutoEncoding VB (AEVB…

… We propose a framework for learning representations that relies on auto-encoding
variational Bayes, in which the search space is constrained via kernel-based measures of …

… fit with auto-encoding variational Bayes (AEVB), practitioners often let the variational
distribution … The auto-encoding variational Bayes (AEVB) algorithm performs model selection by …

… We present, to the best of our knowledge, the first fast Auto-Encoding Variational Bayes
based inference method for jointly inferring topics and visualization. Since our method is black …

We would like to learn latent representations that are low-dimensional and highly interpretable.
A model that has these characteristics is the Gaussian Process Latent Variable Model. …

… We believe it is correct to use Auto-Encoding Variational Bayesian here to encode x, infer
the probability distribution z, and decode the hidden variable to the observed x’. …

Auto-encoding Variational Bayes (AEVB) is a powerful and … is well-known for training the
Variational Auto-Encoder (VAE). In … be interpreted as performing variational inference. Important …

… Finally, we aim at investigating the effectiveness of our auto-encoding variational NMT (AEVNMT).Appendix
A contains a detailed description of the architectures that parameterise our …

… -free setting, we develop variational inference with synthetic likelihoods that allows us to
learn such models. • We develop a principled objective function for auto-encoding GANs (α-GAN…

… the variational auto-encoding framework where the noise is added to the latent variables…
variational inference based on recognition networks, which we call the variational autoencoding …