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: This paper essentially presents a new inductive bias in the architecture of (convolutional) neural networks (CNN). The mathematical motivations/derivations of the proposed architecture are detailed and rigorous. The proposed architecture promises to produce equivariant representations with steerable features using fewer parameters than traditional CNNs, which is particularly useful in small data regimes. Interesting and novel connections are presented between steerable filters and so called “steerable fibers”. The architecture is strongly inspired by the author’s previous work, as well as that of “capsules” (Hinton, 2011). The proposed architecture is compared on CIFAR10 against state-of-the-art inspired architectures (ResNets), and is shown to be superior particularly in the small data regime. The lack of empirical comparison on large scale dataset, such as ImageNet or COCO makes this largely a theoretical contribution. I would have also liked to see more empirical evaluation of the equivariance properties. It is not intuitively clear exactly why this architecture performs better on CIFAR10 as it is not clear that capturing equivariances helps to classify different instances of object categories. Wouldn’t action-recognition in videos, for example, not be a better illustrative dataset?

Answer: en


Question: The paper proposes a model that aims at learning to label nodes of graph in a semi-supervised setting. The idea of the model is based on the use of the graph structure to regularize the representations learned at the node levels. Experimental results are provided on different tasks  The underlying idea of this paper (graph regularization) has been already explored in different papers – e.g 'Learning latent representations of nodes for classifying in heterogeneous social networks' [Jacob et al. 2014],   [Weston et al 2012] where a real graph structure is used instead of a built one. The experiments lack of strong comparisons with other graph models (e.g Iterative Classification, 'Learning from labeled and unlabeled data on a directed graph', ...). So the novelty of the paper and the experimental protocol are not strong enough to accpet the paper.  Pros: * Learning over graph is an important topic  Cons: * Many existing approaches have already exploited the same types of ideas, resulting in very close models * Lack of comparison w.r.t existing models

Answer: en


Question: El artículo cumple con el formato establecido y posee referencias bibliográficas actualizadas.  No se explica claramente qué es JPI, no queda claro el objetivo del artículo. El artículo es una descripción de la aplicación del concepto de JPI, el cual no está bien desarrollado, no queda claro si los autores son quienes proponen esta nueva estrategia o están aplicando una propuesta de otros investigadores. La redacción puede ser mejorada. Se sugiere extender el artículo para que expliquen claramente todos los conceptos, alcances y objetivos de la investigación.

Answer:
es