Sample Importance in Training Deep Neural Networks
The contribution of each sample during model training varies across training iterations and the model*s parameters. We define the concept of sample importance as the change in parameters induced by a sample. In this paper, we explored the sample importance in training deep neural networks using stochastic gradient descent. We found that *easy* samples -- samples that are correctly and confidently classified at the end of the training -- shape parameters closer to the output, while the *hard* samples impact parameters closer to the input to the network. Further, *easy* samples are relevant in the early training stages, and *hard* in the late training stage. Further, we show that constructing batches which contain samples of comparable difficulties tends to be a poor strategy compared to maintaining a mix of both hard and easy samples in all of the batches. Interestingly, this contradicts some of the results on curriculum learning which suggest that ordering training examples in terms of difficulty can lead to better performance.
This paper examines the so called *Sample Importance* of each sample of a training data set, and its effect to the overall learning process. The paper shows empirical results that shows different training cases induces bigger gradients at different stages of learning and different layers. The paper shows some interesting results contrary to the common curriculum learning ideas of using easy training samples first. However, it is unclear how one should define *Easy* training cases. In addition, the experiments demonstrating ordering either NLL or SI is worse than mixed or random batch construction to be insightful. Possible Improvements: It would be nice to factor out the magnitudes of the gradients to the contribution of *sample importance*. Higher gradient (as a function of a particular weight vector) can be affected weight/initialization, thereby introducing noise to the model. It would also be interesting if improvements based on *Sample importance* could be made to the batch selection algorithm to beat the baseline of random batch selection. Overall this paper is a good paper with various experiments examining how various samples in SGD influences the various aspect of training.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Sample Importance in Training Deep Neural Networks
The contribution of each sample during model training varies across training iterations and the model*s parameters. We define the concept of sample importance as the change in parameters induced by a sample. In this paper, we explored the sample importance in training deep neural networks using stochastic gradient descent. We found that *easy* samples -- samples that are correctly and confidently classified at the end of the training -- shape parameters closer to the output, while the *hard* samples impact parameters closer to the input to the network. Further, *easy* samples are relevant in the early training stages, and *hard* in the late training stage. Further, we show that constructing batches which contain samples of comparable difficulties tends to be a poor strategy compared to maintaining a mix of both hard and easy samples in all of the batches. Interestingly, this contradicts some of the results on curriculum learning which suggest that ordering training examples in terms of difficulty can lead to better performance.
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>



This paper examines the so called *Sample Importance* of each sample of a training data set, and its effect to the overall learning process. The paper shows empirical results that shows different training cases induces bigger gradients at different stages of learning and different layers. The paper shows some interesting results contrary to the common curriculum learning ideas of using easy training samples first. However, it is unclear how one should define *Easy* training cases. In addition, the experiments demonstrating ordering either NLL or SI is worse than mixed or random batch construction to be insightful. Possible Improvements: It would be nice to factor out the magnitudes of the gradients to the contribution of *sample importance*. Higher gradient (as a function of a particular weight vector) can be affected weight/initialization, thereby introducing noise to the model. It would also be interesting if improvements based on *Sample importance* could be made to the batch selection algorithm to beat the baseline of random batch selection. Overall this paper is a good paper with various experiments examining how various samples in SGD influences the various aspect of training.
assistant
This paper examines the so called *Sample Importance* of each sample of a training data set, and its effect to the overall learning process. The paper shows empirical results that shows different training cases induces bigger gradients at different stages of learning and different layers. The paper shows some interesting results contrary to the common curriculum learning ideas of using easy training samples first. However, it is unclear how one should define *Easy* training cases. In addition, the experiments demonstrating ordering either NLL or SI is worse than mixed or random batch construction to be insightful. Possible Improvements: It would be nice to factor out the magnitudes of the gradients to the contribution of *sample importance*. Higher gradient (as a function of a particular weight vector) can be affected weight/initialization, thereby introducing noise to the model. It would also be interesting if improvements based on *Sample importance* could be made to the batch selection algorithm to beat the baseline of random batch selection. Overall this paper is a good paper with various experiments examining how various samples in SGD influences the various aspect of training.