Revisiting Bayes by Backprop
In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by 80\%. Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs. We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics. We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks. We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them. We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared.
 The manuscript proposes a new framework for inference in RNN based upon the Bayes by Backprop (BBB) algorithm. In particular, the authors propose a new framework to "sharpen" the posterior. In particular, the hierarchical prior in (6) and (7) frame an interesting modification to directly learning a multivariate normal variational approximation. In the experimental results, it seems clear that this approach is beneficial, but it*s not clear as to why. In particular, how does the variational posterior change as a result of the hierarchical prior? It seems that (7) would push the center of the variational structure back towards the MAP point and reduces the variance of the output of the hierarchical prior; however, with the two layers in the prior it*s unclear what actually is happening. Carefully explaining *what* the authors believe is happening and exploring how it changes the variational approximation in a classic modeling framework would be beneficial to understanding the proposed change and evaluating it. As a final point, the authors state, "as long as the improvement along the gradient is great than the KL loss incurred...this method is guaranteed to make progress towards optimizing L." Do the authors mean that the negative log-likelihood will be improved in this case? Or the actual optimization? Improving the negative log-likelihood seems straightforward, but I am confused by what the authors mean by optimization. The new evaluation metric proposed in Section 6.1.1 is confusing, and I do not understand what the metric is trying to capture. This needs significantly more detail and explanation. Also, it is unclear to me what would happen when you input data examples that are opposite to the original input sequence; in particular, for many neural networks the predictions are unstable outside of the input domain and inputting infeasible data leads to unusable outputs. It*s completely feasible that these outputs would just be highly uncertain, and I*m not sure how you can ascribe meaning to them. The authors should not compare to the uniform prior as a baseline for entropy. It*s much more revealing to compare it to the empirical likelihoods of the words.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Revisiting Bayes by Backprop
In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by 80\%. Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs. We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics. We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks. We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them. We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared.
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 manuscript proposes a new framework for inference in RNN based upon the Bayes by Backprop (BBB) algorithm. In particular, the authors propose a new framework to "sharpen" the posterior. In particular, the hierarchical prior in (6) and (7) frame an interesting modification to directly learning a multivariate normal variational approximation. In the experimental results, it seems clear that this approach is beneficial, but it*s not clear as to why. In particular, how does the variational posterior change as a result of the hierarchical prior? It seems that (7) would push the center of the variational structure back towards the MAP point and reduces the variance of the output of the hierarchical prior; however, with the two layers in the prior it*s unclear what actually is happening. Carefully explaining *what* the authors believe is happening and exploring how it changes the variational approximation in a classic modeling framework would be beneficial to understanding the proposed change and evaluating it. As a final point, the authors state, "as long as the improvement along the gradient is great than the KL loss incurred...this method is guaranteed to make progress towards optimizing L." Do the authors mean that the negative log-likelihood will be improved in this case? Or the actual optimization? Improving the negative log-likelihood seems straightforward, but I am confused by what the authors mean by optimization. The new evaluation metric proposed in Section 6.1.1 is confusing, and I do not understand what the metric is trying to capture. This needs significantly more detail and explanation. Also, it is unclear to me what would happen when you input data examples that are opposite to the original input sequence; in particular, for many neural networks the predictions are unstable outside of the input domain and inputting infeasible data leads to unusable outputs. It*s completely feasible that these outputs would just be highly uncertain, and I*m not sure how you can ascribe meaning to them. The authors should not compare to the uniform prior as a baseline for entropy. It*s much more revealing to compare it to the empirical likelihoods of the words.
assistant
 The manuscript proposes a new framework for inference in RNN based upon the Bayes by Backprop (BBB) algorithm. In particular, the authors propose a new framework to "sharpen" the posterior. In particular, the hierarchical prior in (6) and (7) frame an interesting modification to directly learning a multivariate normal variational approximation. In the experimental results, it seems clear that this approach is beneficial, but it*s not clear as to why. In particular, how does the variational posterior change as a result of the hierarchical prior? It seems that (7) would push the center of the variational structure back towards the MAP point and reduces the variance of the output of the hierarchical prior; however, with the two layers in the prior it*s unclear what actually is happening. Carefully explaining *what* the authors believe is happening and exploring how it changes the variational approximation in a classic modeling framework would be beneficial to understanding the proposed change and evaluating it. As a final point, the authors state, "as long as the improvement along the gradient is great than the KL loss incurred...this method is guaranteed to make progress towards optimizing L." Do the authors mean that the negative log-likelihood will be improved in this case? Or the actual optimization? Improving the negative log-likelihood seems straightforward, but I am confused by what the authors mean by optimization. The new evaluation metric proposed in Section 6.1.1 is confusing, and I do not understand what the metric is trying to capture. This needs significantly more detail and explanation. Also, it is unclear to me what would happen when you input data examples that are opposite to the original input sequence; in particular, for many neural networks the predictions are unstable outside of the input domain and inputting infeasible data leads to unusable outputs. It*s completely feasible that these outputs would just be highly uncertain, and I*m not sure how you can ascribe meaning to them. The authors should not compare to the uniform prior as a baseline for entropy. It*s much more revealing to compare it to the empirical likelihoods of the words.