Padam: Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the generalization gap of adaptive gradient methods an open problem. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". We design a new algorithm, called Partially adaptive momentum estimation method (Padam), which unifies the Adam/Amsgrad with SGD by introducing a partial adaptive parameter p, to achieve the best from both worlds. Experiments on standard benchmarks show that Padam can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks.
 The authors propose a modification of existing adaptive variants of SGD to avoid problems with generalization. It is known that adaptive gradient algorithms such as Adam tend to find good parameter values more quickly initially, but in the later phases of training they stop making good progress due to necessarily low learning rates so SGD often outperforms them past a certain point. The suggested algorithm Padam achieves the best of both worlds, quick initial improvements and good performance in the later stages. This is potentially a very significant contribution which could become the next state-of-the-art optimization method for deep learning. The paper is very clear and well-written, providing a good overview of existing approaches and explaining the specific issue it addresses. The authors have included the right amount of equations so that they provide the required details but do not obfuscate the explanations. The experiments consist of a comprehensive evaluation of Padam against the popular alternatives and show clear improvements over them. I have not evaluated the convergence theorem or its proof since this is not my area of expertise. One thing that stood out to me is that I don*t see why theta* should be unique. Some minor suggestions for improving the paper: Towards the end of section 2 you mention a non-convergence issue of Adam. It would be useful to add a few sentences to explain exactly what the issue is. I would suggest moving the details of the grid search for p to the main text since many readers would be interested to know what*s typically a good value for this parameter. Would it make sense to try to adapt the value of p, increasing it as the training progresses? Since that*s an obvious extension some comment about it would be useful. On the bottom of page 6: "Figures 1 plots" -> "Figure 1 plots". Make sure to protect the proper names in the bibliography so that they are typeset starting with uppercase letters.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Padam: Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the generalization gap of adaptive gradient methods an open problem. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". We design a new algorithm, called Partially adaptive momentum estimation method (Padam), which unifies the Adam/Amsgrad with SGD by introducing a partial adaptive parameter p, to achieve the best from both worlds. Experiments on standard benchmarks show that Padam can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks.
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 authors propose a modification of existing adaptive variants of SGD to avoid problems with generalization. It is known that adaptive gradient algorithms such as Adam tend to find good parameter values more quickly initially, but in the later phases of training they stop making good progress due to necessarily low learning rates so SGD often outperforms them past a certain point. The suggested algorithm Padam achieves the best of both worlds, quick initial improvements and good performance in the later stages. This is potentially a very significant contribution which could become the next state-of-the-art optimization method for deep learning. The paper is very clear and well-written, providing a good overview of existing approaches and explaining the specific issue it addresses. The authors have included the right amount of equations so that they provide the required details but do not obfuscate the explanations. The experiments consist of a comprehensive evaluation of Padam against the popular alternatives and show clear improvements over them. I have not evaluated the convergence theorem or its proof since this is not my area of expertise. One thing that stood out to me is that I don*t see why theta* should be unique. Some minor suggestions for improving the paper: Towards the end of section 2 you mention a non-convergence issue of Adam. It would be useful to add a few sentences to explain exactly what the issue is. I would suggest moving the details of the grid search for p to the main text since many readers would be interested to know what*s typically a good value for this parameter. Would it make sense to try to adapt the value of p, increasing it as the training progresses? Since that*s an obvious extension some comment about it would be useful. On the bottom of page 6: "Figures 1 plots" -> "Figure 1 plots". Make sure to protect the proper names in the bibliography so that they are typeset starting with uppercase letters.
assistant
 The authors propose a modification of existing adaptive variants of SGD to avoid problems with generalization. It is known that adaptive gradient algorithms such as Adam tend to find good parameter values more quickly initially, but in the later phases of training they stop making good progress due to necessarily low learning rates so SGD often outperforms them past a certain point. The suggested algorithm Padam achieves the best of both worlds, quick initial improvements and good performance in the later stages. This is potentially a very significant contribution which could become the next state-of-the-art optimization method for deep learning. The paper is very clear and well-written, providing a good overview of existing approaches and explaining the specific issue it addresses. The authors have included the right amount of equations so that they provide the required details but do not obfuscate the explanations. The experiments consist of a comprehensive evaluation of Padam against the popular alternatives and show clear improvements over them. I have not evaluated the convergence theorem or its proof since this is not my area of expertise. One thing that stood out to me is that I don*t see why theta* should be unique. Some minor suggestions for improving the paper: Towards the end of section 2 you mention a non-convergence issue of Adam. It would be useful to add a few sentences to explain exactly what the issue is. I would suggest moving the details of the grid search for p to the main text since many readers would be interested to know what*s typically a good value for this parameter. Would it make sense to try to adapt the value of p, increasing it as the training progresses? Since that*s an obvious extension some comment about it would be useful. On the bottom of page 6: "Figures 1 plots" -> "Figure 1 plots". Make sure to protect the proper names in the bibliography so that they are typeset starting with uppercase letters.