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: Fondo: - Se menciona de manera muy superficial el lenguaje a utilizar en forma posterior a Scratch, quizás no es necesario o se debe ahondar más en el tema. - El número de alumnos sobre el cual se aplicó el estudio es muy pequeño para obtener resultados concluyentes. - Sería deseable una comparación o estudio entre la utilización de Scratch y Alice (mencionado en el trabajo). - Concluir a partir del experimento que Scratch es una herramienta adecuada para la educación en Chile, sin incluir resultados con otros lenguajes es un poco apresurado. - Sería deseable mencionar que instituciones o trabajos relacionados con lenguajes para fomentar el pensamiento lógico se han realizado en Chile. No se menciona ninguno como referencia o en la bibliografía. - El ejercicio utilizado en el experimento es uno sólo, se deberían utilizar más si se desea objetivamente evaluar su utilización como lenguaje para mejorar el razonamiento lógico. - La primera cita proviene de un portal de noticias Colombiano y del 2006. Sería deseable una referencia más sólida y local asociada quizás a los lineamientos y planes de estudio del ministerio de educación.   Forma: - Se debe re-escribir el abstract: posee muchas palabras separadas por comas y la redacción incluye ideas desconectadas entre sí. - Se cita 8 veces la cita 7/8. Con la mitad de ellas queda clara la referencia. - ".. pero pocos aprender a programar .." debería ser: ".. pero pocos aprendeN a programar ..". - La cita 2 dice .. "Fundamentos de POgramacion".. debería ser: ".. Fundamentos de Programación".

SOLUTION: es

PROBLEM: Authors propose a competitive learning architecture that learn different RNN predictors independently, akin to a committee of experts which are chosen with a hard switch at run-time. This work is applied to the task of predictive different driving behaviors from human drivers, and combines behaviors at test time, often switching behaviors within seconds. Prediction loss is lower than the similar but non-competitive architecture used as a baseline. It is not very clear how to interpret the results, what is the real impact of the model. If behaviors switch very often, can this really be seen as choosing the best driving mode for a given situation? Maybe the motivation needs to be rephrased a little to be more convincing? The competitive approach presented is interesting but not really novel, thus the impact of this paper for a conference such as ICLR may be limited.

SOLUTION: en

PROBLEM: A few issues with this paper: 1- I find finding #2 trivial and unworthy of mention, but the author don't seem to agree with me that it is. See discussions. 2- Finding #1 relies on Fig #4, which appears very noisy and doesn't provide any error analysis. It makes me question how robust this finding is. One would have naively expected the power usage trend to mirror Fig #3, but given the level of noise, I can't convince myself whether the null hypothesis of there being no dependency between batch size and power consumption is more likely than the alternative. 3- Paper is unfriendly to colorblind readers (or those with B/W printers)  Overall, this paper is a reasonable review of where we are in terms of SOTA vision architectures, but doesn't provide much new insight. I found most interesting the clear illustration that VGG models stand out in terms of being a bad tradeoff in resource-constrained environments (too many researchers are tempted to benchmark their model compression algorithm on VGG-class models because that's always where one can show 10x improvements without doing much.)

SOLUTION:
en