Large Batch Training of Convolutional Networks with Layer-wise Adaptive Rate Scaling
A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with a mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. However, training with a large batch often results in lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome these optimization difficulties, we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled AlexNet and ResNet-50 to a batch size of 16K.
 The paper proposes a new approach to determine learning late for convolutional neural networks. It starts from observation that for batch learning with a fixed number of epochs, the accuracy drops when the batch size is too large. Assuming that the number or epochs and batch size are fixed, the contribution of the paper is a heuristic that assigns different learning late to each layer of a network depending on a ratio of the norms of weights and gradients in a layer. The experimental results show that the proposed heuristic helps AlexNet and ResNet end up in a larger accuracy on ImageNet data. Positives: - the proposed approach is intuitively justified - the experimental results are encouraging Negatives: - the methodological contribution is minor - no attempt is made to theoretically justify the proposed heuristic - the method introduces one or two new hyperparameters and it is not clear from the experimental results what overhead is this adding to network training - the experiments are done only on a single data set, which is not sufficient to establish superiority of an approach Suggestions: - consider using different abbreviation (LARS is used for least-angle regression)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Large Batch Training of Convolutional Networks with Layer-wise Adaptive Rate Scaling
A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with a mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. However, training with a large batch often results in lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome these optimization difficulties, we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled AlexNet and ResNet-50 to a batch size of 16K.
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 a new approach to determine learning late for convolutional neural networks. It starts from observation that for batch learning with a fixed number of epochs, the accuracy drops when the batch size is too large. Assuming that the number or epochs and batch size are fixed, the contribution of the paper is a heuristic that assigns different learning late to each layer of a network depending on a ratio of the norms of weights and gradients in a layer. The experimental results show that the proposed heuristic helps AlexNet and ResNet end up in a larger accuracy on ImageNet data. Positives: - the proposed approach is intuitively justified - the experimental results are encouraging Negatives: - the methodological contribution is minor - no attempt is made to theoretically justify the proposed heuristic - the method introduces one or two new hyperparameters and it is not clear from the experimental results what overhead is this adding to network training - the experiments are done only on a single data set, which is not sufficient to establish superiority of an approach Suggestions: - consider using different abbreviation (LARS is used for least-angle regression)
assistant
 The paper proposes a new approach to determine learning late for convolutional neural networks. It starts from observation that for batch learning with a fixed number of epochs, the accuracy drops when the batch size is too large. Assuming that the number or epochs and batch size are fixed, the contribution of the paper is a heuristic that assigns different learning late to each layer of a network depending on a ratio of the norms of weights and gradients in a layer. The experimental results show that the proposed heuristic helps AlexNet and ResNet end up in a larger accuracy on ImageNet data. Positives: - the proposed approach is intuitively justified - the experimental results are encouraging Negatives: - the methodological contribution is minor - no attempt is made to theoretically justify the proposed heuristic - the method introduces one or two new hyperparameters and it is not clear from the experimental results what overhead is this adding to network training - the experiments are done only on a single data set, which is not sufficient to establish superiority of an approach Suggestions: - consider using different abbreviation (LARS is used for least-angle regression)