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: Si bien es cierto el trabajo se encuentra bien estructurado y da muestras de una metodología adecuada, no queda clara la utilidad del estudio realizado. Los resultados obtenidos y presentados, se basan en un periodo de estudio muy reducido, lo cual quita confianza a las conclusiones. Se observan algunos pequeños errores de edición y redacción en el texto. Se recomienda ampliar el conjunto de datos de entrada del estudio.

SOLUTION: es

PROBLEM: 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?

SOLUTION: en

PROBLEM: Un buen trabajo que está en sintonía con el INFONOR. De hecho este trabajo da continuidad a uno presentado en el INFONOR 2012. Mi única ponderación es que la referencia [13] debería ser el artículo publicado y presentado en el INFONOR en vez de la página del INFONOR.

SOLUTION:
es