Revisiting Reweighted Wake-Sleep
Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn. Sampling discrete latent variables can result in high-variance gradient estimators for two primary reasons: 1) branching on the samples within the model, and 2) the lack of a pathwise derivative for the samples. While current state-of-the-art methods employ control-variate schemes for the former and continuous-relaxation methods for the latter, their utility is limited by the complexities of implementing and training effective control-variate schemes and the necessity of evaluating (potentially exponentially) many branch paths in the model. Here, we revisit the Reweighted Wake Sleep (RWS; Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it circumvents both these issues, outperforming current state-of-the-art methods in learning discrete latent-variable models. Moreover, we observe that, unlike the Importance-weighted Autoencoder, RWS learns better models and inference networks with increasing numbers of particles, and that its benefits extend to continuous latent-variable models as well. Our results suggest that RWS is a competitive, often preferable, alternative for learning deep generative models.
 This paper conducts an extensive set of experiments on RWS and compares it against a set of benchmarks such as GMM and IWAE. The main contribution of the paper is the fact revealed by these experiments, that RWS learns better models and inference networks with increasing numbers of particles, and that its benefits extend to continuous latent variable models as well. The performance of RWS will increase significantly if we increase the number of particles. The experimental part is written in an inspiring way, and I enjoyed reading it. However, there should be stronger baselines incorporated. for example, https://arxiv.org/abs/1805.07445. Also, I think the authors could try to emphasize more on the shortcomings of RWS discovered by the GMM experiments, and how defensive importance sampling fixes it. There are several other parts in the paper that indicates interesting facts, diving deeper into it could possibly lead to more interesting findings. In all, I would consider these comparison results important to be somewhere in the literature, but because its lack of rigorous analysis and explanation for the observations, I personally think these observations alone are not novel enough to be an ICLR paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Revisiting Reweighted Wake-Sleep
Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn. Sampling discrete latent variables can result in high-variance gradient estimators for two primary reasons: 1) branching on the samples within the model, and 2) the lack of a pathwise derivative for the samples. While current state-of-the-art methods employ control-variate schemes for the former and continuous-relaxation methods for the latter, their utility is limited by the complexities of implementing and training effective control-variate schemes and the necessity of evaluating (potentially exponentially) many branch paths in the model. Here, we revisit the Reweighted Wake Sleep (RWS; Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it circumvents both these issues, outperforming current state-of-the-art methods in learning discrete latent-variable models. Moreover, we observe that, unlike the Importance-weighted Autoencoder, RWS learns better models and inference networks with increasing numbers of particles, and that its benefits extend to continuous latent-variable models as well. Our results suggest that RWS is a competitive, often preferable, alternative for learning deep generative models.
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 paper conducts an extensive set of experiments on RWS and compares it against a set of benchmarks such as GMM and IWAE. The main contribution of the paper is the fact revealed by these experiments, that RWS learns better models and inference networks with increasing numbers of particles, and that its benefits extend to continuous latent variable models as well. The performance of RWS will increase significantly if we increase the number of particles. The experimental part is written in an inspiring way, and I enjoyed reading it. However, there should be stronger baselines incorporated. for example, https://arxiv.org/abs/1805.07445. Also, I think the authors could try to emphasize more on the shortcomings of RWS discovered by the GMM experiments, and how defensive importance sampling fixes it. There are several other parts in the paper that indicates interesting facts, diving deeper into it could possibly lead to more interesting findings. In all, I would consider these comparison results important to be somewhere in the literature, but because its lack of rigorous analysis and explanation for the observations, I personally think these observations alone are not novel enough to be an ICLR paper.
assistant
 This paper conducts an extensive set of experiments on RWS and compares it against a set of benchmarks such as GMM and IWAE. The main contribution of the paper is the fact revealed by these experiments, that RWS learns better models and inference networks with increasing numbers of particles, and that its benefits extend to continuous latent variable models as well. The performance of RWS will increase significantly if we increase the number of particles. The experimental part is written in an inspiring way, and I enjoyed reading it. However, there should be stronger baselines incorporated. for example, https://arxiv.org/abs/1805.07445. Also, I think the authors could try to emphasize more on the shortcomings of RWS discovered by the GMM experiments, and how defensive importance sampling fixes it. There are several other parts in the paper that indicates interesting facts, diving deeper into it could possibly lead to more interesting findings. In all, I would consider these comparison results important to be somewhere in the literature, but because its lack of rigorous analysis and explanation for the observations, I personally think these observations alone are not novel enough to be an ICLR paper.