Gradients of Counterfactuals
Gradients have been used to quantify feature importance in machine learning models. Unfortunately, in nonlinear deep networks, not only individual neurons but also the whole network can saturate, and as a result an important input feature can have a tiny gradient. We study various networks, and observe that this phenomena is indeed widespread, across many inputs. We propose to examine interior gradients, which are gradients of counterfactual inputs constructed by scaling down the original input. We apply our method to the GoogleNet architecture for object recognition in images, as well as a ligand-based virtual screening network with categorical features and an LSTM based language model for the Penn Treebank dataset. We visualize how interior gradients better capture feature importance. Furthermore, interior gradients are applicable to a wide variety of deep networks, and have the attribution property that the feature importance scores sum to the the prediction score. Best of all, interior gradients can be computed just as easily as gradients. In contrast, previous methods are complex to implement, which hinders practical adoption.
This work proposes to use visualization of gradients to further understand the importance of features (i.e. pixels) for visual classification. Overall, this presented visualizations are interesting, however, the approach is very ad hoc. The authors do not explain why visualizing regular gradients isn*t correlated with the importance of features relevant to the given visual category and proceed to the interior gradient approach. One particular question with regular gradients at features that form the spatial support of the visual class. Is it the case that the gradients of the features that are confident of the prediction remain low, while those with high uncertainty will have strong gradients? With regards to the interior gradients, it is unclear how the scaling parameter alpha affects the feature importance and how it is related to attention. Finally, does this model use batch normalization?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Gradients of Counterfactuals
Gradients have been used to quantify feature importance in machine learning models. Unfortunately, in nonlinear deep networks, not only individual neurons but also the whole network can saturate, and as a result an important input feature can have a tiny gradient. We study various networks, and observe that this phenomena is indeed widespread, across many inputs. We propose to examine interior gradients, which are gradients of counterfactual inputs constructed by scaling down the original input. We apply our method to the GoogleNet architecture for object recognition in images, as well as a ligand-based virtual screening network with categorical features and an LSTM based language model for the Penn Treebank dataset. We visualize how interior gradients better capture feature importance. Furthermore, interior gradients are applicable to a wide variety of deep networks, and have the attribution property that the feature importance scores sum to the the prediction score. Best of all, interior gradients can be computed just as easily as gradients. In contrast, previous methods are complex to implement, which hinders practical adoption.
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 work proposes to use visualization of gradients to further understand the importance of features (i.e. pixels) for visual classification. Overall, this presented visualizations are interesting, however, the approach is very ad hoc. The authors do not explain why visualizing regular gradients isn*t correlated with the importance of features relevant to the given visual category and proceed to the interior gradient approach. One particular question with regular gradients at features that form the spatial support of the visual class. Is it the case that the gradients of the features that are confident of the prediction remain low, while those with high uncertainty will have strong gradients? With regards to the interior gradients, it is unclear how the scaling parameter alpha affects the feature importance and how it is related to attention. Finally, does this model use batch normalization?
assistant
This work proposes to use visualization of gradients to further understand the importance of features (i.e. pixels) for visual classification. Overall, this presented visualizations are interesting, however, the approach is very ad hoc. The authors do not explain why visualizing regular gradients isn*t correlated with the importance of features relevant to the given visual category and proceed to the interior gradient approach. One particular question with regular gradients at features that form the spatial support of the visual class. Is it the case that the gradients of the features that are confident of the prediction remain low, while those with high uncertainty will have strong gradients? With regards to the interior gradients, it is unclear how the scaling parameter alpha affects the feature importance and how it is related to attention. Finally, does this model use batch normalization?