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].

[EX Q]: In this paper, the authors explicitly design geometrical structure into a CNN by combining it with a Scattering network. This aids stability and limited-data performance. The paper is well written, the contribution of combining Scattering and CNNs is novel and the results seem promising. I feel that such work was a missing piece in the Scattering literature to make it useful for practical applications.  I wish the authors would have investigated the effect of the stable bottom layers with respect to adversarial examples. This can be done in a relatively straightforward way with software like cleverhans [1] or deep fool [2]. It would be very interesting if the first layer's stability in the hybrid architectures increases robustness significantly, as this would tell us that these fooling images are related to low-level geometry. Finding that this is not the case, would be very interesting as well.  Further, the proposed architecture is not evaluated on real limited data problems. This would further strengthen the improved generalization claim. However, I admit that the Cifar-100 / Cifar-10 difference already seems like a promising indicator in this regard.  If one of the two points above will be addressed in an additional experiment, I would be happy to raise my score from 6 to 7.  Summary:   + An interesting approach is presented that might be useful for real-world limited data scenarios. + Limited data results look promising. - Adversarial examples are not investigated in the experimental section. - No realistic small-data problem is addressed.  Minor: - The authors should add a SOTA ResNet to Table 3, as NiN is indeed out of fashion these days. - Some typos: tacke, developping, learni.  [1]
[EX A]: en

[EX Q]: Un Objeto de Aprendizaje sirve como apoyo al proceso educativo, para desarrollar competencias en los estudiantes en alguno de los tres tipos de saberes (domino conceptual, dominio de procedimientos y desarrollo de actitudes y valores). En ninguno de estos ámbitos se dan evidencias que lo demuestren. Además de que las experiencias son muy básicas, el abstract, lo cito textual: "El Artículo presenta, las experiencias recabadas de la aplicación de Objetos de Aprendizaje en el desarrollo de la asignatura de Introducción a la Teoría de Autómatas, debido a que en el desarrollo del curso se han desplegado cambios significativos, planteando un seguimiento académico detallado y exhaustivo mediante un esquema de “Retroalimentación Rápida” y “Preguntas Cortas”.", no se muestran las experiencias recabadas ni tampoco se menciona como las obtendrían.
[EX A]: es

[EX Q]: The authors propose an approach to learning optimization algorithms by framing the problem as a policy search task, then using the guided policy search algorithm. The method is a nice contribution to the "learning to learn" framework, and actually was developed simultaneously to a few papers that have since already been published. It's definitely a useful addition to this space.    The biggest issue with this paper is that the results simply aren't that compelling. The methodology and proposed approach is nice, and the text is improved upon a previous version posted to Arxiv, but it seems that for most problems the results aren't that much better than some of the more common off-the-shelf optimization approaches that _don't_ require learning anything. Furthermore, in the one domain where the method does seem to (marginally) outperform the other methods, the neural net domain, it's unclear why we'd want to use the proposed approaches instead of SGD-based methods and their like (which of course everyone actually does for optimization).    Pros:  + Nice contribution to the learning to learn framework  + Takes a different approach from past (concurrent) work, namely one based upon policy search    Cons:  - Experiments are not particularly compelling    Overall, this work is a little borderline. Still, the PCs have determined that it was deserving of appearing at the conference. We hope the authors can strengthen the empirical validation for the camera ready version.
[EX A]:
en

