Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models
The contrastive divergence algorithm is a popular approach to training energy-based latent variable models, which has been widely used in many machine learning models such as the restricted Boltzmann machines and deep belief nets. Despite its empirical success, the contrastive divergence algorithm is also known to have biases that severely affect its convergence. In this article we propose an unbiased version of the contrastive divergence algorithm that completely removes its bias in stochastic gradient methods, based on recent advances on unbiased Markov chain Monte Carlo methods. Rigorous theoretical analysis is developed to justify the proposed algorithm, and numerical experiments show that it significantly improves the existing method. Our findings suggest that the unbiased contrastive divergence algorithm is a promising approach to training general energy-based latent variable models.
The paper introduces an efficient, unbiased contrastive divergence-like algorithm for training energy-based generative models on the example of Restricted Boltzmann Machine. The proposed algorithm is built upon a very interesting work on unbiased finite-step MCMC approximations by Jacob et al, 2017. Despite the actual theory being published some time ago, the submitted paper popularises these ideas in the machine learning community and contains optimised variants of the existing algorithms for training of RBMs. The paper is mostly written well and does a good job of introducing unbiased MCMC estimators. Authors evaluate their method on rather toyish datasets (by modern standards), however, their empirical analysis is thorough. The improvement upon the standard CD and persistent CD is clear. It also appears that the algorithm actually does not require too many steps and generally does not introduce a lot of computational overhead. The only question I have is why CD has only been tried with k=1 steps? I would be interested in its performance for different number of steps including the dynamically chosen number provided by the empirical 	au in UCD for a given iteration. Even though I do not expect a significant improvement to be obtained, this would separate the effect of the number of steps chosen “right” from unbiasedness of the gradient estimator. Other baselines, including those mentioned in the related work, could also make the comparison more complete. I would also suggest including https://arxiv.org/abs/1905.04062, as it seems to be relevant in the spirit.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models
The contrastive divergence algorithm is a popular approach to training energy-based latent variable models, which has been widely used in many machine learning models such as the restricted Boltzmann machines and deep belief nets. Despite its empirical success, the contrastive divergence algorithm is also known to have biases that severely affect its convergence. In this article we propose an unbiased version of the contrastive divergence algorithm that completely removes its bias in stochastic gradient methods, based on recent advances on unbiased Markov chain Monte Carlo methods. Rigorous theoretical analysis is developed to justify the proposed algorithm, and numerical experiments show that it significantly improves the existing method. Our findings suggest that the unbiased contrastive divergence algorithm is a promising approach to training general energy-based latent variable 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>



The paper introduces an efficient, unbiased contrastive divergence-like algorithm for training energy-based generative models on the example of Restricted Boltzmann Machine. The proposed algorithm is built upon a very interesting work on unbiased finite-step MCMC approximations by Jacob et al, 2017. Despite the actual theory being published some time ago, the submitted paper popularises these ideas in the machine learning community and contains optimised variants of the existing algorithms for training of RBMs. The paper is mostly written well and does a good job of introducing unbiased MCMC estimators. Authors evaluate their method on rather toyish datasets (by modern standards), however, their empirical analysis is thorough. The improvement upon the standard CD and persistent CD is clear. It also appears that the algorithm actually does not require too many steps and generally does not introduce a lot of computational overhead. The only question I have is why CD has only been tried with k=1 steps? I would be interested in its performance for different number of steps including the dynamically chosen number provided by the empirical 	au in UCD for a given iteration. Even though I do not expect a significant improvement to be obtained, this would separate the effect of the number of steps chosen “right” from unbiasedness of the gradient estimator. Other baselines, including those mentioned in the related work, could also make the comparison more complete. I would also suggest including https://arxiv.org/abs/1905.04062, as it seems to be relevant in the spirit.
assistant
The paper introduces an efficient, unbiased contrastive divergence-like algorithm for training energy-based generative models on the example of Restricted Boltzmann Machine. The proposed algorithm is built upon a very interesting work on unbiased finite-step MCMC approximations by Jacob et al, 2017. Despite the actual theory being published some time ago, the submitted paper popularises these ideas in the machine learning community and contains optimised variants of the existing algorithms for training of RBMs. The paper is mostly written well and does a good job of introducing unbiased MCMC estimators. Authors evaluate their method on rather toyish datasets (by modern standards), however, their empirical analysis is thorough. The improvement upon the standard CD and persistent CD is clear. It also appears that the algorithm actually does not require too many steps and generally does not introduce a lot of computational overhead. The only question I have is why CD has only been tried with k=1 steps? I would be interested in its performance for different number of steps including the dynamically chosen number provided by the empirical 	au in UCD for a given iteration. Even though I do not expect a significant improvement to be obtained, this would separate the effect of the number of steps chosen “right” from unbiasedness of the gradient estimator. Other baselines, including those mentioned in the related work, could also make the comparison more complete. I would also suggest including https://arxiv.org/abs/1905.04062, as it seems to be relevant in the spirit.