Recurrent Normalization Propagation
We propose a LSTM parametrization that preserves the means and variances of the hidden states and memory cells across time. While having training benefits similar to Recurrent Batch Normalization and Layer Normalization, it does not need to estimate statistics at each time step, therefore, requiring fewer computations overall. We also investigate the parametrization impact on the gradient flows and present a way of initializing the weights accordingly. We evaluate our proposal on language modelling and image generative modelling tasks. We empirically show that it performs similarly or better than other recurrent normalization approaches, while being faster to execute.
The paper proposes an extension of weight normalization / normalization propagation to recurrent neural networks. Simple experiments suggest it works well. The contribution is potentially useful to a lot of people, as LSTMs are one of the basic building blocks in our field. The contribution is not extremely novel: the change with respect to weight normalization is minor. The experiments are also not very convincing: Layer normalization is reported to have higher test error as it overfits on their example, but in terms of optimization it seems to work better. Also the authors don*t seem to use the data dependent parameter init for weight normalization as proposed in that paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Recurrent Normalization Propagation
We propose a LSTM parametrization that preserves the means and variances of the hidden states and memory cells across time. While having training benefits similar to Recurrent Batch Normalization and Layer Normalization, it does not need to estimate statistics at each time step, therefore, requiring fewer computations overall. We also investigate the parametrization impact on the gradient flows and present a way of initializing the weights accordingly. We evaluate our proposal on language modelling and image generative modelling tasks. We empirically show that it performs similarly or better than other recurrent normalization approaches, while being faster to execute.
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 proposes an extension of weight normalization / normalization propagation to recurrent neural networks. Simple experiments suggest it works well. The contribution is potentially useful to a lot of people, as LSTMs are one of the basic building blocks in our field. The contribution is not extremely novel: the change with respect to weight normalization is minor. The experiments are also not very convincing: Layer normalization is reported to have higher test error as it overfits on their example, but in terms of optimization it seems to work better. Also the authors don*t seem to use the data dependent parameter init for weight normalization as proposed in that paper.
assistant
The paper proposes an extension of weight normalization / normalization propagation to recurrent neural networks. Simple experiments suggest it works well. The contribution is potentially useful to a lot of people, as LSTMs are one of the basic building blocks in our field. The contribution is not extremely novel: the change with respect to weight normalization is minor. The experiments are also not very convincing: Layer normalization is reported to have higher test error as it overfits on their example, but in terms of optimization it seems to work better. Also the authors don*t seem to use the data dependent parameter init for weight normalization as proposed in that paper.