Recurrent Batch Normalization
We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is both possible and beneficial to batch-normalize the hidden-to-hidden transition, thereby reducing internal covariate shift between time steps. We evaluate our proposal on various sequential problems such as sequence classification, language modeling and question answering. Our empirical results show that our batch-normalized LSTM consistently leads to faster convergence and improved generalization.
This paper extends batch normalization successfully to RNNs where batch normalization has previously failed or done poorly. The experiments and datasets tackled show definitively the improvement that batch norm LSTMs provide over standard LSTMs. They also cover a variety of examples, including character level (PTB and Text8), word level (CNN question-answering task), and pixel level (MNIST and pMNIST). The supplied training curves also quite clearly show the potential improvements in training time which is an important metric for consideration. The experiment on pMNIST also solidly shows the advantage of batch norm in the recurrent setting for establishing long term dependencies. I additionally also appreciated the gradient flow insight, specifically the impact of unit variance on tanh derivatives. Showing it not just for batch normalization but additionally the *toy task* (Figure 1b) was hugely useful. Overall I find this paper a useful additional contribution to the usage of batch normalization and would be necessary information for successfully employing it in a recurrent setting.
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### Title + Abstract
Recurrent Batch Normalization
We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is both possible and beneficial to batch-normalize the hidden-to-hidden transition, thereby reducing internal covariate shift between time steps. We evaluate our proposal on various sequential problems such as sequence classification, language modeling and question answering. Our empirical results show that our batch-normalized LSTM consistently leads to faster convergence and improved generalization.
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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 extends batch normalization successfully to RNNs where batch normalization has previously failed or done poorly. The experiments and datasets tackled show definitively the improvement that batch norm LSTMs provide over standard LSTMs. They also cover a variety of examples, including character level (PTB and Text8), word level (CNN question-answering task), and pixel level (MNIST and pMNIST). The supplied training curves also quite clearly show the potential improvements in training time which is an important metric for consideration. The experiment on pMNIST also solidly shows the advantage of batch norm in the recurrent setting for establishing long term dependencies. I additionally also appreciated the gradient flow insight, specifically the impact of unit variance on tanh derivatives. Showing it not just for batch normalization but additionally the *toy task* (Figure 1b) was hugely useful. Overall I find this paper a useful additional contribution to the usage of batch normalization and would be necessary information for successfully employing it in a recurrent setting.
assistant
This paper extends batch normalization successfully to RNNs where batch normalization has previously failed or done poorly. The experiments and datasets tackled show definitively the improvement that batch norm LSTMs provide over standard LSTMs. They also cover a variety of examples, including character level (PTB and Text8), word level (CNN question-answering task), and pixel level (MNIST and pMNIST). The supplied training curves also quite clearly show the potential improvements in training time which is an important metric for consideration. The experiment on pMNIST also solidly shows the advantage of batch norm in the recurrent setting for establishing long term dependencies. I additionally also appreciated the gradient flow insight, specifically the impact of unit variance on tanh derivatives. Showing it not just for batch normalization but additionally the *toy task* (Figure 1b) was hugely useful. Overall I find this paper a useful additional contribution to the usage of batch normalization and would be necessary information for successfully employing it in a recurrent setting.