Characterizing the Accuracy/Complexity Landscape of Explanations of Deep Networks through Knowledge Extraction
Knowledge extraction techniques are used to convert neural networks into symbolic descriptions with the objective of producing more comprehensible learning models. The central challenge is to find an explanation which is more comprehensible than the original model while still representing that model faithfully. The distributed nature of deep networks has led many to believe that the hidden features of a neural network cannot be explained by logical descriptions simple enough to be understood by humans, and that decompositional knowledge extraction should be abandoned in favour of other methods. In this paper we examine this question systematically by proposing a knowledge extraction method using 	extit{M-of-N} rules which allows us to map the complexity/accuracy landscape of rules describing hidden features in a Convolutional Neural Network (CNN). Experiments reported in this paper show that the shape of this landscape reveals an optimal trade off between comprehensibility and accuracy, showing that each latent variable has an optimal 	extit{M-of-N} rule to describe its behaviour. We find that the rules with optimal tradeoff in the first and final layer have a high degree of explainability whereas the rules with the optimal tradeoff in the second and third layer are less explainable. The results shed light on the feasibility of rule extraction from deep networks, and point to the value of decompositional knowledge extraction as a method of explainability.
 Overall, this is a interesting paper on an important topic: knowledge extraction from Neural Networks. Even though the authors seem propose a novel approach to knowledge extraction, the paper would dramatically benefit from two additions: - an empirical evaluation on at least two more datasets (as is, the paper uses a single dataset) - an illustrative-but-realistic example of how at least one rule is extracted from each layer of the neural network Other comments: - on page 4 (1st paragraph in 3.3), the authors talk about a "test set" that, it turns out, it is extracted from the actual training set (1st paragraph of 4.1); the authors should use a more careful terminology - from the paper, it seems that the authors tried a single randomly chosen set 1000 random inputs in 4.1; they should most definitely try several such sets - Figure 1 should have a legend in the image, rather than as a 2-line caption
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Characterizing the Accuracy/Complexity Landscape of Explanations of Deep Networks through Knowledge Extraction
Knowledge extraction techniques are used to convert neural networks into symbolic descriptions with the objective of producing more comprehensible learning models. The central challenge is to find an explanation which is more comprehensible than the original model while still representing that model faithfully. The distributed nature of deep networks has led many to believe that the hidden features of a neural network cannot be explained by logical descriptions simple enough to be understood by humans, and that decompositional knowledge extraction should be abandoned in favour of other methods. In this paper we examine this question systematically by proposing a knowledge extraction method using 	extit{M-of-N} rules which allows us to map the complexity/accuracy landscape of rules describing hidden features in a Convolutional Neural Network (CNN). Experiments reported in this paper show that the shape of this landscape reveals an optimal trade off between comprehensibility and accuracy, showing that each latent variable has an optimal 	extit{M-of-N} rule to describe its behaviour. We find that the rules with optimal tradeoff in the first and final layer have a high degree of explainability whereas the rules with the optimal tradeoff in the second and third layer are less explainable. The results shed light on the feasibility of rule extraction from deep networks, and point to the value of decompositional knowledge extraction as a method of explainability.
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>



 Overall, this is a interesting paper on an important topic: knowledge extraction from Neural Networks. Even though the authors seem propose a novel approach to knowledge extraction, the paper would dramatically benefit from two additions: - an empirical evaluation on at least two more datasets (as is, the paper uses a single dataset) - an illustrative-but-realistic example of how at least one rule is extracted from each layer of the neural network Other comments: - on page 4 (1st paragraph in 3.3), the authors talk about a "test set" that, it turns out, it is extracted from the actual training set (1st paragraph of 4.1); the authors should use a more careful terminology - from the paper, it seems that the authors tried a single randomly chosen set 1000 random inputs in 4.1; they should most definitely try several such sets - Figure 1 should have a legend in the image, rather than as a 2-line caption
assistant
 Overall, this is a interesting paper on an important topic: knowledge extraction from Neural Networks. Even though the authors seem propose a novel approach to knowledge extraction, the paper would dramatically benefit from two additions: - an empirical evaluation on at least two more datasets (as is, the paper uses a single dataset) - an illustrative-but-realistic example of how at least one rule is extracted from each layer of the neural network Other comments: - on page 4 (1st paragraph in 3.3), the authors talk about a "test set" that, it turns out, it is extracted from the actual training set (1st paragraph of 4.1); the authors should use a more careful terminology - from the paper, it seems that the authors tried a single randomly chosen set 1000 random inputs in 4.1; they should most definitely try several such sets - Figure 1 should have a legend in the image, rather than as a 2-line caption