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].

Input: Consider Input: *** 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

Output: en


Input: Consider Input: El artículo es especialmente válido como una guía de implementación de un data center. Su nivel teórico no es relevante, sin embargo su aplicabilidad le hace de interés.  El artículo está bien escrito y documentado.  Se debe insistir que se trata de un caso y este caso está desarrollado completo siguiendo las normas TIA propuestas como referencia.

Output: es


Input: Consider Input: The paper presents a new environment, called Retro Learning Environment (RLE), for reinforcement learning. The authors focus on Super Nintendo but claim that the interface supports many others (including ALE). Benchmark results are given for standard algorithms in 5 new Super Nintendo games, and some results using a new "rivalry metric".  These environments (or, more generally, standardized evaluation methods like public data sets, competitions, etc.) have a long history of improving the quality of AI and machine learning research. One example in the past few years was the Atari Learning Environment (ALE) which has now turned into a standard benchmark for comparison of algorithms and results. In this sense, the RLE could be a worthy contribution to the field by encouraging new challenging domains for research.  That said, the main focus of this paper is presenting this new framework and showcasing the importance of new challenging domains. The results of experiments themselves are for existing algorithms. There are some new results that show reward shaping and policy shaping (having a bias toward going right in Super Mario) help during learning. And, yes, domain knowledge helps, but this is obvious. The rivalry training is an interesting idea, when training against a different opponent, the learner overfits to that opponent and forgets to play against the in-game AI; but then oddly, it gets evaluated on how well it does against the in-game AI!   Also the part of the paper that describes the scientific results (especially the rivalry training) is less polished, so this is disappointing. In the end, I'm not very excited about this paper.  I was hoping for a more significant scientific contribution to accompany in this new environment. It's not clear if this is necessary for publication, but also it's not clear that ICLR is the right venue for this work due to the contribution being mainly about the new code (for example, mloss.org could be a better 'venue', JMLR has an associated journal track for accompanying papers:


Output: en