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]: Building on earlier work on a model called NICE, this paper presents an approach to constructing deep feed-forward generative models. The model is evaluated on several datasets. While it does not achieve state-of-the-art performance, it advances an interesting class of models. The paper is mostly well written and clear.  Given that inference and generation are both efficient and exact, and given that this represents a main advantage over other models, it would be great if the authors could provide some motivating example applications where this is needed/would be useful.  The authors claim that “unlike both variational autoencoders and GANs, our technique is able to learn a semantically meaningful latent space which is as high dimensional as the input space.” Where is the evidence for this claim? I didn’t see any analysis of the semantic meaningfulness of the latent space learned by real NVP. Stronger evidence that the learned representations are actually useful for downstream tasks would be nice.  I still think the author’s intuitions around the “fixed reconstruction cost of L2” are very vague. The factorial Gaussian assumption itself does not limit the generative model, it merely smoothes an otherwise arbitrary distribution, and to a degree which can be arbitrarily small, p(x) = \int p(z) N(x | f(z), \sigma^2) dz. How a lose lower bound plays into this is not clear from the paper.
[EX A]: en

[EX Q]: El trabajo presentado a Infonor - Chile, cumple con las expectativas siguientes: 1. Realiza una acertada discusión bibliográfica 2. Incorpora un algoritmo híbrido de búsqueda novedosa 3. Los resultados obtenidos con scheduling con restricciones usando el algoritmo (recursos limitados) muestra resultados alentadores.
[EX A]: es

[EX Q]: El artículo presenta una revisión de los SGBD móviles y NoSQL.  No obstante lo anterior, el trabajo es más bien descriptivo y carece de resultados experimentales.  Lo que señala en la página 8, metodología, considera dos fases, de las cuales solo se presenta, al parecer los resultados de la primera fase.  Se sugiere realizar la segunda fase, que conlleva un trabajo experimental de recolección de datos y su análisis, y luego generar un artículo con estos resultados.
[EX A]:
es