title: Training Variational Auto Encoders with Discrete Latent Representations using Importance Sampling
abstract: The Variational Auto Encoder (VAE) is a popular generative 
  latent variable model that is often 
  applied for representation learning.
  Standard VAEs assume continuous valued 
  latent variables and are trained by maximization
  of the evidence lower bound (ELBO). Conventional methods obtain a 
  differentiable estimate of the ELBO with reparametrized sampling and
  optimize it with Stochastic Gradient Descend (SGD). However, this is not possible if 
  we want to train VAEs with discrete valued latent variables, 
  since reparametrized sampling is not possible. Till now, there
  exist no simple solutions to circumvent this problem.
  In this paper, we propose an easy method to train VAEs 
  with binary or categorically valued latent representations. Therefore, we use a differentiable
  estimator for the ELBO which is based on importance sampling. In experiments, we verify the approach and
  train two different VAEs architectures with Bernoulli and 
  Categorically distributed latent representations on two different benchmark
  datasets.	
authors: Alexander Bartler Felix Wiewel Bin Yang Lukas Mauch
hIndex: Lukas Mauch 11