Discrete Variational Autoencoders
Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We present a novel method to train a class of probabilistic models with discrete latent variables using the variational autoencoder framework, including backpropagation through the discrete latent variables. The associated class of probabilistic models comprises an undirected discrete component and a directed hierarchical continuous component. The discrete component captures the distribution over the disconnected smooth manifolds induced by the continuous component. As a result, this class of models efficiently learns both the class of objects in an image, and their specific realization in pixels, from unsupervised data; and outperforms state-of-the-art methods on the permutation-invariant MNIST, Omniglot, and Caltech-101 Silhouettes datasets.
This is an interesting paper on how to handle reparameterization in VAEs when you have discrete variables. The idea is to introduce a smoothing transformation that is shared between the generative model and the recognition model (leading to cancellations). A second contribution is to introduce an RBM as the prior model P(z) and to use autoregressive connections in generative and recognition models. The whole package becomes a bit entangled and complex and it is hard to figure out what causes the claimed good performance. Experiments that study these contributions separately would have been nice. The framework does become a little complex but this should not be a problem if nice software is delivered that can be used in a plug and play mode. Overall, the paper is very rich with ideas so I think it would be a great contribution to the conference.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Discrete Variational Autoencoders
Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We present a novel method to train a class of probabilistic models with discrete latent variables using the variational autoencoder framework, including backpropagation through the discrete latent variables. The associated class of probabilistic models comprises an undirected discrete component and a directed hierarchical continuous component. The discrete component captures the distribution over the disconnected smooth manifolds induced by the continuous component. As a result, this class of models efficiently learns both the class of objects in an image, and their specific realization in pixels, from unsupervised data; and outperforms state-of-the-art methods on the permutation-invariant MNIST, Omniglot, and Caltech-101 Silhouettes datasets.
user
<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 is an interesting paper on how to handle reparameterization in VAEs when you have discrete variables. The idea is to introduce a smoothing transformation that is shared between the generative model and the recognition model (leading to cancellations). A second contribution is to introduce an RBM as the prior model P(z) and to use autoregressive connections in generative and recognition models. The whole package becomes a bit entangled and complex and it is hard to figure out what causes the claimed good performance. Experiments that study these contributions separately would have been nice. The framework does become a little complex but this should not be a problem if nice software is delivered that can be used in a plug and play mode. Overall, the paper is very rich with ideas so I think it would be a great contribution to the conference.
assistant
This is an interesting paper on how to handle reparameterization in VAEs when you have discrete variables. The idea is to introduce a smoothing transformation that is shared between the generative model and the recognition model (leading to cancellations). A second contribution is to introduce an RBM as the prior model P(z) and to use autoregressive connections in generative and recognition models. The whole package becomes a bit entangled and complex and it is hard to figure out what causes the claimed good performance. Experiments that study these contributions separately would have been nice. The framework does become a little complex but this should not be a problem if nice software is delivered that can be used in a plug and play mode. Overall, the paper is very rich with ideas so I think it would be a great contribution to the conference.