Contextual Explanation Networks
We introduce contextual explanation networks (CENs)---a class of models that learn to predict by generating and leveraging intermediate explanations. CENs are deep networks that generate parameters for context-specific probabilistic graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularization and boosts performance in low-resource settings. We prove that local approximations to the decision boundary of our networks are consistent with the generated explanations. Our results on image and text classification and survival analysis tasks demonstrate that CENs are competitive with the state-of-the-art while offering additional insights behind each prediction, valuable for decision support.
 The article "Contextual Explanation Networks" introduces the class of models which learn the intermediate explanations in order to make final predictions. The contexts can be learned by, in principle, any model including neural networks, while the final predictions are supposed to be made by some simple models like linear ones. The probabilistic model allows for the simultaneous training of explanation and prediction parts as opposed to some recent post-hoc methods. The experimental part of the paper considers variety of experiments, including classification on MNIST, CIFAR-10, IMDB and also some experiments on survival analysis. I should note, that the quality of the algorithm is in general similar to other methods considered (as expected). However, while in some cases the CEN algorithm is slightly better, in other cases it appears to sufficiently loose, see for example left part of Figure 3(b) for MNIST data set. It would be interesting to know the explanation. Also, it would be interesting to have more examples of qualitative analysis to see, that the learned explanations are really useful. I am a bit worried, that while we have interpretability with respect to intermediate features, these features theirselves might be very hard to interpret. To sum up, I think that the general idea looks very natural and the results are quite supportive. However, I don*t feel myself confident enough in this area of research to make strong conclusion on the quality of the paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Contextual Explanation Networks
We introduce contextual explanation networks (CENs)---a class of models that learn to predict by generating and leveraging intermediate explanations. CENs are deep networks that generate parameters for context-specific probabilistic graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularization and boosts performance in low-resource settings. We prove that local approximations to the decision boundary of our networks are consistent with the generated explanations. Our results on image and text classification and survival analysis tasks demonstrate that CENs are competitive with the state-of-the-art while offering additional insights behind each prediction, valuable for decision support.
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 article "Contextual Explanation Networks" introduces the class of models which learn the intermediate explanations in order to make final predictions. The contexts can be learned by, in principle, any model including neural networks, while the final predictions are supposed to be made by some simple models like linear ones. The probabilistic model allows for the simultaneous training of explanation and prediction parts as opposed to some recent post-hoc methods. The experimental part of the paper considers variety of experiments, including classification on MNIST, CIFAR-10, IMDB and also some experiments on survival analysis. I should note, that the quality of the algorithm is in general similar to other methods considered (as expected). However, while in some cases the CEN algorithm is slightly better, in other cases it appears to sufficiently loose, see for example left part of Figure 3(b) for MNIST data set. It would be interesting to know the explanation. Also, it would be interesting to have more examples of qualitative analysis to see, that the learned explanations are really useful. I am a bit worried, that while we have interpretability with respect to intermediate features, these features theirselves might be very hard to interpret. To sum up, I think that the general idea looks very natural and the results are quite supportive. However, I don*t feel myself confident enough in this area of research to make strong conclusion on the quality of the paper.
assistant
 The article "Contextual Explanation Networks" introduces the class of models which learn the intermediate explanations in order to make final predictions. The contexts can be learned by, in principle, any model including neural networks, while the final predictions are supposed to be made by some simple models like linear ones. The probabilistic model allows for the simultaneous training of explanation and prediction parts as opposed to some recent post-hoc methods. The experimental part of the paper considers variety of experiments, including classification on MNIST, CIFAR-10, IMDB and also some experiments on survival analysis. I should note, that the quality of the algorithm is in general similar to other methods considered (as expected). However, while in some cases the CEN algorithm is slightly better, in other cases it appears to sufficiently loose, see for example left part of Figure 3(b) for MNIST data set. It would be interesting to know the explanation. Also, it would be interesting to have more examples of qualitative analysis to see, that the learned explanations are really useful. I am a bit worried, that while we have interpretability with respect to intermediate features, these features theirselves might be very hard to interpret. To sum up, I think that the general idea looks very natural and the results are quite supportive. However, I don*t feel myself confident enough in this area of research to make strong conclusion on the quality of the paper.