Hierarchical interpretations for neural network predictions
Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their applications. To ameliorate this problem, we introduce the use of hierarchical interpretations to explain DNN predictions through our proposed method: agglomerative contextual decomposition (ACD). Given a prediction from a trained DNN, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive. We introduce ACD using examples from Stanford Sentiment Treebank and ImageNet, in order to diagnose incorrect predictions, identify dataset bias, and extract polarizing phrases of varying lengths. Through human experiments, we demonstrate that ACD enables users both to identify the more accurate of two DNNs and to better trust a DNN*s outputs. We also find that ACD*s hierarchy is largely robust to adversarial perturbations, implying that it captures fundamental aspects of the input and ignores spurious noise.
 This paper proposes a hierarchical extension of contextual decomposition. The approach is validated in qualitative examples and a small scale usability study Quality, The paper is well motivated. Contextual decomposition is briefly described but detailed enough to self-contained. The experimental evaluation produces usability evidence. Uncertainty could have been better explained, Clarity, The main methodological contribution (hierarchical CD) is well motivated but only provided in the form of an algorithm. Could have been more precisely described and optimality discussed. Originality & significance The work builds heavily on CD but has the hierarchical extension is original and significant. Uncertainty estimates could have improved the significance of the usability study pros and cons + interesting problem + well-motivated algorithmic extension of CD - uncertainty of usability experiment?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Hierarchical interpretations for neural network predictions
Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their applications. To ameliorate this problem, we introduce the use of hierarchical interpretations to explain DNN predictions through our proposed method: agglomerative contextual decomposition (ACD). Given a prediction from a trained DNN, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive. We introduce ACD using examples from Stanford Sentiment Treebank and ImageNet, in order to diagnose incorrect predictions, identify dataset bias, and extract polarizing phrases of varying lengths. Through human experiments, we demonstrate that ACD enables users both to identify the more accurate of two DNNs and to better trust a DNN*s outputs. We also find that ACD*s hierarchy is largely robust to adversarial perturbations, implying that it captures fundamental aspects of the input and ignores spurious noise.
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 proposes a hierarchical extension of contextual decomposition. The approach is validated in qualitative examples and a small scale usability study Quality, The paper is well motivated. Contextual decomposition is briefly described but detailed enough to self-contained. The experimental evaluation produces usability evidence. Uncertainty could have been better explained, Clarity, The main methodological contribution (hierarchical CD) is well motivated but only provided in the form of an algorithm. Could have been more precisely described and optimality discussed. Originality & significance The work builds heavily on CD but has the hierarchical extension is original and significant. Uncertainty estimates could have improved the significance of the usability study pros and cons + interesting problem + well-motivated algorithmic extension of CD - uncertainty of usability experiment?
assistant
 This paper proposes a hierarchical extension of contextual decomposition. The approach is validated in qualitative examples and a small scale usability study Quality, The paper is well motivated. Contextual decomposition is briefly described but detailed enough to self-contained. The experimental evaluation produces usability evidence. Uncertainty could have been better explained, Clarity, The main methodological contribution (hierarchical CD) is well motivated but only provided in the form of an algorithm. Could have been more precisely described and optimality discussed. Originality & significance The work builds heavily on CD but has the hierarchical extension is original and significant. Uncertainty estimates could have improved the significance of the usability study pros and cons + interesting problem + well-motivated algorithmic extension of CD - uncertainty of usability experiment?