FastNorm: Improving Numerical Stability of Deep Network Training with Efficient Normalization
We propose a modification to weight normalization techniques that provides the same convergence benefits but requires fewer computational operations. The proposed method, FastNorm, exploits the low-rank properties of weight updates and infers the norms without explicitly calculating them, replacing an computation with an one for a fully-connected layer. It improves numerical stability and reduces accuracy variance enabling higher learning rate and offering better convergence. We report experimental results that illustrate the advantage of the proposed method.
 This authors proposed to use an implicit weight normalization approach to replace the explicit weight normalization used in training of neural networks. The authors claimed to obtain efficiency improvement and better numerical stability. This is a short paper that contains five pages. The idea of the proposed implicit weight normalization is to apply the normalization to scaling the input rather than the rows of the matrices. In terms of the overall time complexity, the improvement seems quite limited considering that the normalization is not the bottleneck operations in the training. In addition, it is not very clear how the proposed approach benefits the mini-batch training of the network. In terms of numerical stability, though experimental results were reported, there is no theoretical analysis. The experiments are quite limited.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
FastNorm: Improving Numerical Stability of Deep Network Training with Efficient Normalization
We propose a modification to weight normalization techniques that provides the same convergence benefits but requires fewer computational operations. The proposed method, FastNorm, exploits the low-rank properties of weight updates and infers the norms without explicitly calculating them, replacing an computation with an one for a fully-connected layer. It improves numerical stability and reduces accuracy variance enabling higher learning rate and offering better convergence. We report experimental results that illustrate the advantage of the proposed method.
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 authors proposed to use an implicit weight normalization approach to replace the explicit weight normalization used in training of neural networks. The authors claimed to obtain efficiency improvement and better numerical stability. This is a short paper that contains five pages. The idea of the proposed implicit weight normalization is to apply the normalization to scaling the input rather than the rows of the matrices. In terms of the overall time complexity, the improvement seems quite limited considering that the normalization is not the bottleneck operations in the training. In addition, it is not very clear how the proposed approach benefits the mini-batch training of the network. In terms of numerical stability, though experimental results were reported, there is no theoretical analysis. The experiments are quite limited.
assistant
 This authors proposed to use an implicit weight normalization approach to replace the explicit weight normalization used in training of neural networks. The authors claimed to obtain efficiency improvement and better numerical stability. This is a short paper that contains five pages. The idea of the proposed implicit weight normalization is to apply the normalization to scaling the input rather than the rows of the matrices. In terms of the overall time complexity, the improvement seems quite limited considering that the normalization is not the bottleneck operations in the training. In addition, it is not very clear how the proposed approach benefits the mini-batch training of the network. In terms of numerical stability, though experimental results were reported, there is no theoretical analysis. The experiments are quite limited.