
Least Square Variational Bayesian Autoencoder with Regularization
In recent years Variation Autoencoders have become one of the most popul...
07/11/2017 ∙ by Gautam Ramachandra, et al. ∙ 0 ∙ shareread it

Importance Weighted Autoencoders
The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently ...
09/01/2015 ∙ by Yuri Burda, et al. ∙ 0 ∙ shareread it

AlphaDivergences in Variational Dropout
We investigate the use of alternative divergences to KullbackLeibler (K...
11/12/2017 ∙ by Bogdan Mazoure, et al. ∙ 0 ∙ shareread it

Approximate Variational Inference Based on a Finite Sample of Gaussian Latent Variables
Variational methods are employed in situations where exact Bayesian infe...
06/11/2019 ∙ by Nikolaos Gianniotis, et al. ∙ 0 ∙ shareread it

M^2VAE  Derivation of a MultiModal Variational Autoencoder Objective from the Marginal Joint LogLikelihood
This work gives an indepth derivation of the trainable evidence lower b...
03/18/2019 ∙ by Timo Korthals, et al. ∙ 0 ∙ shareread it

Biadversarial Variational Autoencoder
In the original version of the Variational Autoencoder, Kingma et al. as...
02/09/2019 ∙ by Arnaud Fickinger, et al. ∙ 0 ∙ shareread it

Variational Gaussian Approximation for Poisson Data
The Poisson model is frequently employed to describe count data, but in ...
09/18/2017 ∙ by Simon Arridge, et al. ∙ 0 ∙ shareread it
Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Function
In Bayesian machine learning, the posterior distribution is typically computationally intractable, hence variational inference is often required. In this approach, an evidence lower bound on the log likelihood of data is maximized during training. Variational Autoencoders (VAE) are one important example where variational inference is utilized. In this tutorial, we derive the variational lower bound loss function of the standard variational autoencoder. We do so in the instance of a gaussian latent prior and gaussian approximate posterior, under which assumptions the KullbackLeibler term in the variational lower bound has a closed form solution. We derive essentially everything we use along the way; everything from Bayes' theorem to the KullbackLeibler divergence.
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