Title: Least Square Variational Bayesian Autoencoder with Regularization,
Abstract: In recent years Variation Autoencoders have become one of the most popular
unsupervised learning of complicated distributions.Variational Autoencoder
(VAE) provides more efficient reconstructive performance over a traditional
autoencoder. Variational auto enocders make better approximaiton than MCMC. The
VAE defines a generative process in terms of ancestral sampling through a
cascade of hidden stochastic layers. They are a directed graphic models.
Variational autoencoder is trained to maximise the variational lower bound.
Here we are trying maximise the likelihood and also at the same time we are
trying to make a good approximation of the data. Its basically trading of the
data log-likelihood and the KL divergence from the true posterior. This paper
describes the scenario in which we wish to find a point-estimate to the
parameters $\theta$ of some parametric model in which we generate each
observations by first sampling a local latent variable and then sampling the
associated observation. Here we use least square loss function with
regularization in the the reconstruction of the image, the least square loss
function was found to give better reconstructed images and had a faster
training time.