Improving Stochastic Gradient Descent with Feedback
In this paper we propose a simple and efficient method for improving stochastic gradient descent methods by using feedback from the objective function. The method tracks the relative changes in the objective function with a running average, and uses it to adaptively tune the learning rate in stochastic gradient descent. We specifically apply this idea to modify Adam, a popular algorithm for training deep neural networks. We conduct experiments to compare the resulting algorithm, which we call Eve, with state of the art methods used for training deep learning models. We train CNNs for image classification, and RNNs for language modeling and question answering. Our experiments show that Eve outperforms all other algorithms on these benchmark tasks. We also analyze the behavior of the feedback mechanism during the training process.
The paper introduced an extension of Adam optimizer that automatically adjust learning rate by comparing the subsequent values of the cost function during training. The authors empirically demonstrated the benefit of the Eve optimizer on CIFAR convnets, logistic regression and RNN problems. I have the following concerns about the paper - The proposed method is VARIANT to arbitrary shifts and scaling to the cost function. - A more fair comparison with other baseline methods would be using additional exponential decay learning scheduling between the lower and upper threshold of d_t. I suspect 1/d_t just shrinks as an exponential decay from Figure 2. - Three additional hyper-parameters: k, K, _x0008_eta_3. Overall, I think the method has its fundamental flew and the paper offers very limited novelty. There is no theoretical justification on the modification, and it would be good for the authors to discuss the potential failure mode of the proposed method. Furthermore, it is hard for me to follow Section 3.2. The writing quality and clarity of the method section can be further improved.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Improving Stochastic Gradient Descent with Feedback
In this paper we propose a simple and efficient method for improving stochastic gradient descent methods by using feedback from the objective function. The method tracks the relative changes in the objective function with a running average, and uses it to adaptively tune the learning rate in stochastic gradient descent. We specifically apply this idea to modify Adam, a popular algorithm for training deep neural networks. We conduct experiments to compare the resulting algorithm, which we call Eve, with state of the art methods used for training deep learning models. We train CNNs for image classification, and RNNs for language modeling and question answering. Our experiments show that Eve outperforms all other algorithms on these benchmark tasks. We also analyze the behavior of the feedback mechanism during the training process.
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 introduced an extension of Adam optimizer that automatically adjust learning rate by comparing the subsequent values of the cost function during training. The authors empirically demonstrated the benefit of the Eve optimizer on CIFAR convnets, logistic regression and RNN problems. I have the following concerns about the paper - The proposed method is VARIANT to arbitrary shifts and scaling to the cost function. - A more fair comparison with other baseline methods would be using additional exponential decay learning scheduling between the lower and upper threshold of d_t. I suspect 1/d_t just shrinks as an exponential decay from Figure 2. - Three additional hyper-parameters: k, K, _x0008_eta_3. Overall, I think the method has its fundamental flew and the paper offers very limited novelty. There is no theoretical justification on the modification, and it would be good for the authors to discuss the potential failure mode of the proposed method. Furthermore, it is hard for me to follow Section 3.2. The writing quality and clarity of the method section can be further improved.
assistant
The paper introduced an extension of Adam optimizer that automatically adjust learning rate by comparing the subsequent values of the cost function during training. The authors empirically demonstrated the benefit of the Eve optimizer on CIFAR convnets, logistic regression and RNN problems. I have the following concerns about the paper - The proposed method is VARIANT to arbitrary shifts and scaling to the cost function. - A more fair comparison with other baseline methods would be using additional exponential decay learning scheduling between the lower and upper threshold of d_t. I suspect 1/d_t just shrinks as an exponential decay from Figure 2. - Three additional hyper-parameters: k, K, _x0008_eta_3. Overall, I think the method has its fundamental flew and the paper offers very limited novelty. There is no theoretical justification on the modification, and it would be good for the authors to discuss the potential failure mode of the proposed method. Furthermore, it is hard for me to follow Section 3.2. The writing quality and clarity of the method section can be further improved.