instruction:
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].
question:
I reviewed the manuscript as of December 6th. The authors perform a systematic investigation of various retraining methods for making a classification network robust to adversarial examples. The authors achieve lower error rates using their RAD and IAEC methods perform better then previously introduced distillation methods for retraining networks to be robust to adversarial examples. This method suggests a promising direction for building a defense for adversarial examples. Major Comments: I find the paper to not be lacking in exposition and clarity. The paper has a laundry list of related results (page 2) but no clear message. I *think* one larger point is the superior performance of their retraining techniques but it is not clear how well these techniques perform compared to other retraining techniques, nor are the details of the retraining techniques clear. The paper requires more discussion and a clear exposition about the methods the authors introduced (i.e. RAD, IAEC). What follow are some more detailed comments along this theme of improving the exposition and clarity: - The authors should provide more details about how they constructed the auto-encoder in the IAEC method (diagram?). The same needs to be said for the RAD method. The authors point to a previous workshop submission (
answer:
en

question:
El trabajo describe la navegacion de un robot movil utilizando ROS y el hardware Raspberry PI. La descripcion cae en focalizarse en detalles de configuracion, al punto de parecer un manual de instalacion o puesta en marcha. No hay reporte de resultados, menos aun analisis de la solucion propuesta. Dado lo anterior, y tal como se presenta no es un articulo para conferencia. Solo un reporte de como utilizar y configurar la Raspberry PI.
answer:
es

question:
Combining storage and processing capabilities is an interesting research topic because data transfer is a major issue for many machine learning tasks. The paper itself is well-written, but unfortunately addresses a lot of things only to medium depth (probably due length constraints). My opinion is that a journal with an in-depth discussion of the technical details would be a better target for this paper. Even though the researchers took an interesting approach to evaluate the performance of the system, it's difficult for me to grasp the expected practical improvements of this approach. With such a big focus on GPU (and more specialized hardware such as TPUs), the one question that comes to mind: By how much does this - or do you expect it to - beat the latest and greatest GPU on a real task? I don't consider myself an expert on this topic even though I have some experience with SystemC.
answer:
en