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.
The paper proposes a new criterion (sample importance) to study the impact of samples during the training of deep neural networks. This criterion is not clearly defined (the term phi^t_{i,j} is never defined, only phi^t_i is defined; Despite the unclear definition, it is understood that sample importance is the squared l2 norm of the gradient for a sample i and at time t strangely scaled by the squared learning rate (the learning rate should have nothing to do with the importance of a sample in this context). The paper presents experiments on the well known MNIST and CIFAR datasets with correspondingly appropriate network architectures and choice of hyper-parameters and initialisations. The size of the hidden layers is a bit small for Mnist and very small for CIFAR (this could explain the very poor performance in figure 6: 50% error on CIFAR) The study of the evolution of sample importance during training depending on layers seems to lead to trivial conclusions - “the overall sample importance is different under different epochs” => yes the norm of the gradient is expected to vary - “Output layer always has the largest average sample importance per parameter, and its contribution reaches the maximum in the early training stage and then drops” => 1. yes since the gradient flows backwards, the gradient is expected to be stronger for the output layer and it is expected to become more diffuse as it propagates to lower layers which are not stable. As learning progresses one would expect the output layer to have progressively smaller gradients. 2. the norm of the gradient depends on the scaling of the variables The question of Figure 4 is absurd “Is Sample Importance the same as Negative log-likelihood of a sample?”. Of course not. The results are very bad on CIFAR which discredits the applicability of those results. On Mnist performance is not readable (figure 7): Error rate should only be presented between 0 and 10 or 20% Despite these important issues (there are others), the paper manages to raises some interesting things: the so-called easy samples and hard samples do seem to correspond (although the study is very preliminary in this regard) to what would intuitively be considered easy (the most representative/canonical samples) and hard (edge cases) samples. Also the experiments are very well presented.
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>



The paper proposes a new criterion (sample importance) to study the impact of samples during the training of deep neural networks. This criterion is not clearly defined (the term phi^t_{i,j} is never defined, only phi^t_i is defined; Despite the unclear definition, it is understood that sample importance is the squared l2 norm of the gradient for a sample i and at time t strangely scaled by the squared learning rate (the learning rate should have nothing to do with the importance of a sample in this context). The paper presents experiments on the well known MNIST and CIFAR datasets with correspondingly appropriate network architectures and choice of hyper-parameters and initialisations. The size of the hidden layers is a bit small for Mnist and very small for CIFAR (this could explain the very poor performance in figure 6: 50% error on CIFAR) The study of the evolution of sample importance during training depending on layers seems to lead to trivial conclusions - “the overall sample importance is different under different epochs” => yes the norm of the gradient is expected to vary - “Output layer always has the largest average sample importance per parameter, and its contribution reaches the maximum in the early training stage and then drops” => 1. yes since the gradient flows backwards, the gradient is expected to be stronger for the output layer and it is expected to become more diffuse as it propagates to lower layers which are not stable. As learning progresses one would expect the output layer to have progressively smaller gradients. 2. the norm of the gradient depends on the scaling of the variables The question of Figure 4 is absurd “Is Sample Importance the same as Negative log-likelihood of a sample?”. Of course not. The results are very bad on CIFAR which discredits the applicability of those results. On Mnist performance is not readable (figure 7): Error rate should only be presented between 0 and 10 or 20% Despite these important issues (there are others), the paper manages to raises some interesting things: the so-called easy samples and hard samples do seem to correspond (although the study is very preliminary in this regard) to what would intuitively be considered easy (the most representative/canonical samples) and hard (edge cases) samples. Also the experiments are very well presented.
assistant
The paper proposes a new criterion (sample importance) to study the impact of samples during the training of deep neural networks. This criterion is not clearly defined (the term phi^t_{i,j} is never defined, only phi^t_i is defined; Despite the unclear definition, it is understood that sample importance is the squared l2 norm of the gradient for a sample i and at time t strangely scaled by the squared learning rate (the learning rate should have nothing to do with the importance of a sample in this context). The paper presents experiments on the well known MNIST and CIFAR datasets with correspondingly appropriate network architectures and choice of hyper-parameters and initialisations. The size of the hidden layers is a bit small for Mnist and very small for CIFAR (this could explain the very poor performance in figure 6: 50% error on CIFAR) The study of the evolution of sample importance during training depending on layers seems to lead to trivial conclusions - “the overall sample importance is different under different epochs” => yes the norm of the gradient is expected to vary - “Output layer always has the largest average sample importance per parameter, and its contribution reaches the maximum in the early training stage and then drops” => 1. yes since the gradient flows backwards, the gradient is expected to be stronger for the output layer and it is expected to become more diffuse as it propagates to lower layers which are not stable. As learning progresses one would expect the output layer to have progressively smaller gradients. 2. the norm of the gradient depends on the scaling of the variables The question of Figure 4 is absurd “Is Sample Importance the same as Negative log-likelihood of a sample?”. Of course not. The results are very bad on CIFAR which discredits the applicability of those results. On Mnist performance is not readable (figure 7): Error rate should only be presented between 0 and 10 or 20% Despite these important issues (there are others), the paper manages to raises some interesting things: the so-called easy samples and hard samples do seem to correspond (although the study is very preliminary in this regard) to what would intuitively be considered easy (the most representative/canonical samples) and hard (edge cases) samples. Also the experiments are very well presented.