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 Input:
This paper develops Submodular Sum Product Networks (SSPNs) and an efficient inference algorithm for approximately computing the most probable labeling of variables in the model. The main application in the paper is on scene parsing. In this context, SSPNs define an energy function with a grammar component for representing a hierarchy of labels and an MRF for encoding smoothness of labels over space. To perform inference, the authors develop a move-making algorithm, somewhat in the spirit of fusion moves (Lempitsky et al., 2010) that repeatedly improves a solution by considering a large neighborhood of alternative segmentations and solving an optimization problem to choose the best neighbor. Empirical results show that the proposed algorithm achieves better energy that belief propagation of alpha expansion and is much faster.  This is generally a well-executed paper. The model is interesting and clearly defined, the algorithm is well presented with proper analysis of the relevant runtimes and guarantees on the behavior. Overall, the algorithm seems effective at minimizing the energy of SSPN models.  Having said that, I don't think this paper is a great fit for ICLR. The model is even somewhat to the antithesis of the idea of learning representations, in that a highly structured form of energy function is asserted by the human modeller, and then inference is performed. I don't see the connection to learning representations. One additional issue is that while the proposed algorithm is faster than alternatives, the times are still on the order of 1-287 seconds per image, which means that the applicability of this method (as is) to something like training ConvNets is limited.  Finally, there is no attempt to argue that the model produces better segmentations than alternative models. The only evaluations in the paper are on energy values achieved and on training data.  So overall I think this is a good paper that should be published at a good machine learning conference, but I don't think ICLR is the right fit.

Ex Output:
en


Ex Input:
En esta investigación se trata un tema que cada vez es más importante en ámbitos académicos y empresariales. Por otra parte, se utilizan métodos de análisis de datos complejos y muy adecuados a los objetivos de la investigación y los autores los que sirven de referencia básica para la investigación son muy adecuados.  En la medida en que el objeto material de la investigación es el individuo, entiendo que, cuando se describe la muestra, es conveniente, no sólo describir el perfil de la empresa, sino el perfil del individuo entrevistado (edad, sexo, puesto que ocupa, etc.). •	El apartado conclusiones merece, a mi juicio, una mayor atención, que incluya no sólo los resultados de la investigación, sino una discusión más amplia. •	Las limitaciones de la investigación hacen referencia al tamaño de la muestra y al tipo de muestreo empleado, pero no hacen referencia a la dimensión del modelo. En este sentido, sería conveniente plantear, como línea de investigación futura, la ampliación del modelo con nuevas variables e indicadores •	las referencias bibliográficas son anteriores al año 2009. Sugiero consultar las siguientes o	The effect of organizational support on ERP implementation DonHee Lee,  Sang M. Lee, David L. Olson, Soong Hwan Chung.  Industrial Management + Data Systems.  Wembley:2010.  Vol. 110,  Iss. 2,  p. 269-283 o	Predicting the behavioral intention to use enterprise resource planning systems :An exploratory extension of the technology acceptance model Fethi Calisir,  Cigdem Altin Gumussoy,  Armagan Bayram.  Management Research News.  Patrington:2009.  Vol. 32,  Iss. 7,  p. 597-613 o	Organizational adoption of information technologies: Case of enterprise resource planning systems Onur Kerimoglu,  Nuri Basoglu,  Tugrul Daim.  Journal of High Technology Management Research.  Greenwich:2008.  Vol. 19,  Iss. 1,  p. 21

Ex Output:
es


Ex Input:
Nice idea but not complete, model size is not reduced by the large factors found in one of your references (Song 2016), where they go to 5 bits, but this is ontop of pruning which gives overall 49X reduction in model size of VGG (without loss of accuracy). You may achieve similar reductions with inclusion of pruning (or better since you go to 4 bits with no loss) but we should see this in the paper, so at the moment it is difficult to compare

Ex Output:

en