In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

*** Paper Summary ***  This paper simplify matching network by considering only a single prototype per class which is obtained as the average of the embedding of the training class samples. Empirical comparisons with matching networks are reported.  *** Review ***  The paper reads well and clearly motivate the work. This work of learning metric learning propose to simplify an earlier work (matching network) which is a great objective. However, I am not sure it achieve better results than matching networks. The space of learning embeddings to optimize nearest neighbor classification has been explored before, but the idea of averaging the propotypes is interesting (as a non-linear extension of Mensink et al 2013). I would suggest to improve the discussion of related work and to consolidate the results section to help distinguish between the methods you outperform and the one you do not.   The related work section can be extended to include work on learning distance metric to optimize a nearest neighbor classification, see Weinberger et al, 2005 and subsequent work. Extensions to perform the same task with neural networks can be found in Min et al, 09 that purse a goal very close to yours. Regarding approaches pursuing similar goals with a different learning objective, you cite siamese network with pairwise supervision. The learning to rank (for websearch) litterature with triplet supervision or global ranking losses is also highly relevant, ie. one example "the query" defines the class and the embedding space need to be such that positive/relevant document are closer to the query than the others. I would suggest to start with Chris Burges 2010 tutorial. One learning class   I am not sure the reported results correctly reflect the state of the art for all tasks. The results are positive on Omniglot but I feel that you should also report the better results of matching networks on miniImageNet with fine tuning and full contextual embeddings. It can be considered misleading not to report it. On Cub 200, I thought that the state-of-the-art was 50.1%, when using features from GoogLeNet (Akata et al 2015), could you comment on this?  Overall, paper could greatly be improved, both in the discussion of related work and with a less partial reporting of prior empirical results.  *** References ***  Large Margin Nearest Neighbors. Weinberger et al, 2005 From RankNet to LambdaRank to LambdaMART: An Overview, Chris J.C. Burges, June 23, 2010 A Deep Non-linear Feature Mapping for Large-Margin kNN Classification, Min et al, 09
en

The present submission discusses a "causal regularizer", which promotes the use of causal dependencies (X -> Y, where X is a feature of the learning problem, and Y is the target variable) in predictive models. Similarly, such causal regularizer penalizes the use of non-causal dependencies, which can arise due to reverse causation (Y -> X) or confounding (X <- Z -> Y, where Z is a hidden confounder).  + Overall, this submission tackles one of the most important problems in machine learning, which is to build causal models. The paper discusses and addresses this issue effectively when applied to a dataset in heart disease. In their experiments, the authors correctly identify some of the common causes of heart disease by virtue of their causal regularizer.  - The authors do not discuss the robustness of their approach with respect to choice of hyper-parameters (both describing the neural network architecture and the generative model that synthesizes artificial causal data). This seems like a crucial issue, in particular when dealing with medical data.  - The conclusions of the experimental evaluation should be discussed in greater length. On the one hand, Figure 4.a shows that there are no differences between L1 and causal regularization in terms of predictive performance, but it is difficult to conclude if this result is statistically significant without access to error-bars. On the other hand, Table 3 describes the qualitative differences between L1 and causal regularization. However, this table is hard to read: How were the 30 rows selected? What does the red highlighting mean? Are these red rows some true causal features that were missed? If so, this is related to precision. What about recall? Did the causal regularization pick up many non-causal features as causal?  - Regarding causal classifiers, this paper should do a much better job at reviewing previous work. For instance, the paper "Towards a Learning Theory of Cause-Effect Inference" from Lopez-Paz et al. is missing from the references. However, this prior work studies many of the aspects that are hinted as novel in this submission. In particular, the prior work of Lopez-Paz 1) introduces the concept of Mother distribution (referred as Nature hyper-prior in this submission) which explicitly factorizes the distribution over causes and mechanisms, 2) circumvented intractable likelihoods by synthesizing and training on causal data, 3) tackled the confounding case (compare Figure 1 of this submission and Appendix C of Lopez-Paz), and 4) dealt with discrete data seamlessly (such as the ChaLearn data from Section 5.3 in Lopez-Paz).  On a positive note, this is a well-written paper that addresses the important, under-appreciated problem of incorporating causal reasoning into machine learning. On a negative note, the novelty of the technical contributions is modest and the qualitative evaluation of the results could be greatly extended. In short, I am leaning slightly towards acceptance.
en

- Strengths:  - Weaknesses: Many grammar errors, such as the abstract  - General Discussion:

en