TASK DEFINITION: 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].
PROBLEM: 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).

SOLUTION: en

PROBLEM: El artículo describe el armado de un robot para docencia de pregrado, con la información de ecuaciones y control para su utilización. No presenta un trabajo de investigación específico, pero es una buena alternativa para ser mostrado en el congreso. Algunos comentarios de carácter general, que deben ser modificado para su aceptación final: 1.- Por las características de la comunicación, se entiende las pocas referencias que presenta. 2.- Las ecuaciones fueron copiadas y pegadas, en la mayoría de las veces, no se ven muy legibles, por lo que se aconseja reescribirlas con un editor de ecuaciones todas. 3.- Las figuras no presentan fuentes o referencias , de donde fueron extraídas. 4.- Existen párrafos largos que pueden acomodarse mejor con una puntuación.  En general es un trabajo bien escrito, se entiende fácilmente su contenido, su presentación con los "copy paste" atentan.

SOLUTION: es

PROBLEM: Review: Multimodal Word Distributions  - Strengths:  Overall a very strong paper.  - Weaknesses: The comparison against similar approaches could be extended.  - General Discussion:  The main focus of this paper is the introduction of a new model for learning multimodal word distributions formed from Gaussian mixtures for multiple word meanings. i. e. representing a word by a set of many Gaussian distributions.  The approach, extend the model introduced by Vilnis and McCallum (2014) which represented word as unimodal Gaussian distribution. By using a multimodal, the current approach attain the problem of polysemy.  Overall, a very strong paper, well structured and clear. The experimentation is correct and the qualitative analysis made in table 1 shows results as expected from the approach.  There’s not much that can be faulted and all my comments below are meant to help the paper gain additional clarity.   Some comments:   _ It may be interesting to include a brief explanation of the differences between the approach from Tian et al. 2014 and the current one. Both split single word representation into multiple prototypes by using a mixture model.   _ There are some missing citations that could me mentioned in related work as :  Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space Neelakantan, A., Shankar. J. Passos, A., McCallum. EMNLP 2014 Do Multi-Sense Embeddings Improve Natural Language Understanding? Li and Jurafsky, EMNLP 2015 Topical Word Embeddings. Liu Y., Liu Z., Chua T.,Sun M. AAAI 2015  _ Also, the inclusion of the result from those approaches in tables 3 and 4 could be interesting.   _ A question to the authors: What do you attribute the loss of performance of w2gm against w2g in the analysis of SWCS?  I have read the response.

SOLUTION:
en