Training Variational Auto Encoders with Discrete Latent Representations using Importance Sampling
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.
 This paper propose to use important sampling to optimize VAE with discrete latent variables. Basically, the methods proposed by this paper is rather simple and trivial. There are some discussions on why important sampling is not a good choice for VAE. Please refer: https://stats.stackexchange.com/q/255756 Moreover, if you focus on VAE with discrete latent variable, you should compare at least with Gumbel-Softmax: https://arxiv.org/abs/1611.01144
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Training Variational Auto Encoders with Discrete Latent Representations using Importance Sampling
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.
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<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper propose to use important sampling to optimize VAE with discrete latent variables. Basically, the methods proposed by this paper is rather simple and trivial. There are some discussions on why important sampling is not a good choice for VAE. Please refer: https://stats.stackexchange.com/q/255756 Moreover, if you focus on VAE with discrete latent variable, you should compare at least with Gumbel-Softmax: https://arxiv.org/abs/1611.01144
assistant
 This paper propose to use important sampling to optimize VAE with discrete latent variables. Basically, the methods proposed by this paper is rather simple and trivial. There are some discussions on why important sampling is not a good choice for VAE. Please refer: https://stats.stackexchange.com/q/255756 Moreover, if you focus on VAE with discrete latent variable, you should compare at least with Gumbel-Softmax: https://arxiv.org/abs/1611.01144