An experimental study of layer-level training speed and its impact on generalization
How optimization influences the generalization ability of a DNN is still an active area of research. This work aims to unveil and study a factor of influence: the speed at which each layer trains. In our preliminary work, we develop a visualization technique and an optimization algorithm to monitor and control the layer rotation rate, a tentative measure of layer-level training speed, and show that it has a remarkably consistent and substantial impact on generalization. Our experiments further suggest that weight decay*s and adaptive gradients methods* impact on both generalization performance and speed of convergence are solely due to layer rotation rate changes compared to vanilla SGD, offering a novel interpretation of these widely used techniques, and providing supplementary evidence that layer-level training speed indeed impacts generalization. Besides these fundamental findings, we also expect that on a practical level, the tools we introduce will reduce the meta-parameter tuning required to get the best generalization out of a deep network.
 Pros: Overall, this is a nice empirical paper with a reasonably extensive set of experiments. It is interesting that, among networks that train to ~100% with Layca, the best generalizing ones tend to have balanced training between layers (Fig. 2), and that tuned SGD does not generalize as well as Layca (Fig. 4). I think this paper’s focus on discrepancies in training & generalization originating from layers of a deep network is an interesting and important topic of study that warrants further empirical and theoretical investigation from the community. I think the work already has some interesting results and will encourage further investigation. Cons: --Would appreciate greater discussion of the originality of the results; in particular, a more upfront discussion (which is currently concisely presented in the supplementary) regarding algorithms that are similar to Layca when less crucial steps are dropped, e.g. Yu et al 2017 and Ginsburg et al 2018. --After reading the paper, I don’t feel especially convinced that rotation (of the flattened weight matrix) is the best quantity to analyze training dynamics of a single layer. Could there be greater discussion & motivation for this, and in particular, relationship to work where weights are parameterized using orthogonal matrices, or even orthogonal initialization? Some minor comments: --Would have appreciated a discussion of the learning rate schedule (as well as other experimental details, e.g. loss function used and what role this plays) and whether networks with lower learning rates would need to be trained longer. --Greater discussion of why the first and last layer(s) do not experience the same rotation rate as other layers and if there would be better generalization if they did.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
An experimental study of layer-level training speed and its impact on generalization
How optimization influences the generalization ability of a DNN is still an active area of research. This work aims to unveil and study a factor of influence: the speed at which each layer trains. In our preliminary work, we develop a visualization technique and an optimization algorithm to monitor and control the layer rotation rate, a tentative measure of layer-level training speed, and show that it has a remarkably consistent and substantial impact on generalization. Our experiments further suggest that weight decay*s and adaptive gradients methods* impact on both generalization performance and speed of convergence are solely due to layer rotation rate changes compared to vanilla SGD, offering a novel interpretation of these widely used techniques, and providing supplementary evidence that layer-level training speed indeed impacts generalization. Besides these fundamental findings, we also expect that on a practical level, the tools we introduce will reduce the meta-parameter tuning required to get the best generalization out of a deep network.
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>



 Pros: Overall, this is a nice empirical paper with a reasonably extensive set of experiments. It is interesting that, among networks that train to ~100% with Layca, the best generalizing ones tend to have balanced training between layers (Fig. 2), and that tuned SGD does not generalize as well as Layca (Fig. 4). I think this paper’s focus on discrepancies in training & generalization originating from layers of a deep network is an interesting and important topic of study that warrants further empirical and theoretical investigation from the community. I think the work already has some interesting results and will encourage further investigation. Cons: --Would appreciate greater discussion of the originality of the results; in particular, a more upfront discussion (which is currently concisely presented in the supplementary) regarding algorithms that are similar to Layca when less crucial steps are dropped, e.g. Yu et al 2017 and Ginsburg et al 2018. --After reading the paper, I don’t feel especially convinced that rotation (of the flattened weight matrix) is the best quantity to analyze training dynamics of a single layer. Could there be greater discussion & motivation for this, and in particular, relationship to work where weights are parameterized using orthogonal matrices, or even orthogonal initialization? Some minor comments: --Would have appreciated a discussion of the learning rate schedule (as well as other experimental details, e.g. loss function used and what role this plays) and whether networks with lower learning rates would need to be trained longer. --Greater discussion of why the first and last layer(s) do not experience the same rotation rate as other layers and if there would be better generalization if they did.
assistant
 Pros: Overall, this is a nice empirical paper with a reasonably extensive set of experiments. It is interesting that, among networks that train to ~100% with Layca, the best generalizing ones tend to have balanced training between layers (Fig. 2), and that tuned SGD does not generalize as well as Layca (Fig. 4). I think this paper’s focus on discrepancies in training & generalization originating from layers of a deep network is an interesting and important topic of study that warrants further empirical and theoretical investigation from the community. I think the work already has some interesting results and will encourage further investigation. Cons: --Would appreciate greater discussion of the originality of the results; in particular, a more upfront discussion (which is currently concisely presented in the supplementary) regarding algorithms that are similar to Layca when less crucial steps are dropped, e.g. Yu et al 2017 and Ginsburg et al 2018. --After reading the paper, I don’t feel especially convinced that rotation (of the flattened weight matrix) is the best quantity to analyze training dynamics of a single layer. Could there be greater discussion & motivation for this, and in particular, relationship to work where weights are parameterized using orthogonal matrices, or even orthogonal initialization? Some minor comments: --Would have appreciated a discussion of the learning rate schedule (as well as other experimental details, e.g. loss function used and what role this plays) and whether networks with lower learning rates would need to be trained longer. --Greater discussion of why the first and last layer(s) do not experience the same rotation rate as other layers and if there would be better generalization if they did.