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.	 In this paper, we derived an easy estimator for the ELBO, which does not rely on reparametrized sampling and therefore can be used to obtain differentiable estimates, even if reparametrization is not possible, e.g. if the latent variables z are Bernoulli or Categorically distributed. We have shown theoretically and in experiments, close to the optimal parameter configuration, the variance of the estimator approaches zero. This is a very desirable property for training.
We propose an easy method to train Variational Auto Encoders (VAE) with discrete latent representations, using importance sampling Introducting an importance sampling distribution and using samples from distribution to compute importance-weighted estimate of the gradient This paper proposes to use important sampling to optimize VAE with discrete latent variables.