Introspection:Accelerating Neural Network Training By Learning Weight Evolution
Neural Networks are function approximators that have achieved state-of-the-art accuracy in numerous machine learning tasks. In spite of their great success in terms of accuracy, their large training time makes it difficult to use them for various tasks. In this paper, we explore the idea of learning weight evolution pattern from a simple network for accelerating training of novel neural networks. We use a neural network to learn the training pattern from MNIST classification and utilize it to accelerate training of neural networks used for CIFAR-10 and ImageNet classification. Our method has a low memory footprint and is computationally efficient. This method can also be used with other optimizers to give faster convergence. The results indicate a general trend in the weight evolution during training of neural networks.
The paper reads well and the idea is new. Sadly, many details needed for replicating the results (such as layer sizes of the CNNs, learning rates) are missing. The training of the introspection network could have been described in more detail. Also, I think that a model, which is closer to the current state-of-the-art should have been used in the ImageNet experiments. That would have made the results more convincing. Due to the novelty of the idea, I recommend the paper. I would increase the rating if an updated draft addresses the mentioned issues.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Introspection:Accelerating Neural Network Training By Learning Weight Evolution
Neural Networks are function approximators that have achieved state-of-the-art accuracy in numerous machine learning tasks. In spite of their great success in terms of accuracy, their large training time makes it difficult to use them for various tasks. In this paper, we explore the idea of learning weight evolution pattern from a simple network for accelerating training of novel neural networks. We use a neural network to learn the training pattern from MNIST classification and utilize it to accelerate training of neural networks used for CIFAR-10 and ImageNet classification. Our method has a low memory footprint and is computationally efficient. This method can also be used with other optimizers to give faster convergence. The results indicate a general trend in the weight evolution during training of 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 paper reads well and the idea is new. Sadly, many details needed for replicating the results (such as layer sizes of the CNNs, learning rates) are missing. The training of the introspection network could have been described in more detail. Also, I think that a model, which is closer to the current state-of-the-art should have been used in the ImageNet experiments. That would have made the results more convincing. Due to the novelty of the idea, I recommend the paper. I would increase the rating if an updated draft addresses the mentioned issues.
assistant
The paper reads well and the idea is new. Sadly, many details needed for replicating the results (such as layer sizes of the CNNs, learning rates) are missing. The training of the introspection network could have been described in more detail. Also, I think that a model, which is closer to the current state-of-the-art should have been used in the ImageNet experiments. That would have made the results more convincing. Due to the novelty of the idea, I recommend the paper. I would increase the rating if an updated draft addresses the mentioned issues.