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].
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Question: Summary: This paper on autoregressive generative models explores various extensions of PixelCNNs. The proposed changes are to replace the softmax function with a logistic mixture model, to use dropout for regularization, to use downsampling to increase receptive field size, and the introduction of particular skip connections. The authors find that this allows the PixelCNN to outperform a PixelRNN on CIFAR-10, the previous state-of-the-art model. The authors further explore the performance of PixelCNNs with smaller receptive field sizes.  Review: This is a useful contribution towards better tractable image models. In particular, autoregressive models can be quite slow at test time, and the more efficient architectures described here should help with that.  My main criticism regards the severe neglect of related work. Mixture models have been used a lot in autoregressive image modeling, including for multivariate conditional densities and including downsampling to increase receptive field size, albeit in a different manner: Domke (2008), Hosseini et al. (2010), Theis et al. (2012), Uria et al. (2013), Theis et al. (2015). Note that the logistic distribution is a special case of the Gaussian scale mixture (West, 1978).  The main difference seems to be the integration of the density to model integers. While this is clearly a good idea and the right way forward, the authors claim but do not support that not doing this has “proved to be a problem for earlier models based on continuous distributions”. Please elaborate, add a reference, or ideally report the performance achieved by PixelCNN++ without integration (and instead adding uniform noise to make the variables continuous).  60,000 images are not a lot in a high-dimensional space. While I can see the usefulness of regularization for specialized content – and this can serve as a good example to demonstrate the usefulness of dropout – why not use “80 million tiny images” (superset of CIFAR-10) for natural images? Semi-supervised learning should be fairly trivial here (because the model’s likelihood is tractable), so this data could even be used in the class-conditional case.  It would be interesting to know how fast the different models are at test time (i.e., when generating images).

Answer: en


Question: - Strengths: The paper addresses a relevant topic: learning the mapping between natural language and KB relations, in the context of QA (where we have only partial information for one of the arguments), and in the case of having a very large number of possible target relations.  The proposal consists in a new method to combine two different representations of the input text: a word level representation (i.e. with segmentation of the target relation names and also the input text), and relations as a single token (i.e without segmentation of relation names nor input text).   It seems, that the main contribution in QA is the ability to re-rank entities after the Entity Linking step.  Results show an improvement compared with the state of the art.   - Weaknesses: The approach has been evaluated in a limited dataset.   - General Discussion:  I think, section 3.1 fits better inside related work, so the 3.2 can become section 3 with the proposal. Thus, new section 3 can be splitted more properly.

Answer: en


Question: El trabajo presenta un estudio del estado del arte en lo que respecta a sistemas de robótica cooperativa de multi-robots. El foco de la revisión es la arquitectura de control presente en aquellos sistemas.  El abstract en inglés debe ser revisado en su redacción, en especial el uso de palabras, como "giving"->"providing", "panoramic"->"general", etc.  La introducción no entrega un mayor aporte a la estructura del documento. Se habla un poco de la investigación asociada al tema, pero con un uso muy bajo en referencias y la sección termina abruptamente sin explicar metodología o contribuciones del trabajo.  El trabajo entrega un "planteamiento del problema" muy poco claro y que no provee mayor relevancia. Más aun, además de identificar algunas aproximaciones al contexto, el trabajo no plantea ninguna solución, alternativa, categorización, marco de trabajo o aporte en general al (poco claro) problema presentado.  En resumen, el trabajo realiza una recopilación relativamente adecuada de información respecto a sistemas de robótica cooperativa de multi-robots, sin embargo, más allá de eso, no se revela un real aporte o relevancia desde el punto de vista científico al trabajo. Si estos puntos fueran más claros, si se presentara de forma directa la metodología, y si se presentara un producto concreto de investigación, este trabajo podría ser aceptado, pero en su estado actual no se recomienda.

Answer:
es