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]: El artículo habla (en el resumen y un poco en la introducción) de una experiencia tecnológica de inter-operatividad entre plataformas de enseñanza, pero no se evidencia tal experiencia en sus líneas. Los resultados no son concretos, no hay experimentación o análisis de resultados que sustenten lo afirmado en el apartado de conclusiones. El apartado de conclusiones no se relaciona con lo dicho en el resumen no expresado en la introducción como objetivo del trabajo. A pesar de ser un estudio interesante respecto a estándares y tecnologías como e-learning o t-learning, los autores no logran describir la metodología de trabajo, procesos, experimentación, resultados y valoración de los resultados. En el apartado Resultados (al final) hablan de pruebas en dispositivos pero no se describen dichas pruebas, no hay evidencia de dichas pruebas en el documento.  Por otro lado, el texto tiene carencias importantes como las siguientes: 1. Tanto el Resumen como el Abstract tienen ideas difíciles de entender o están poco elaborados. 2. Se detectan errores ortográficos en el documento. 3. Lo dicho en la Introducción coincide poco con lo dicho en el Resumen. 4. Uso de siglas (abreviaturas) no descritas en el documento (como por ejemplo ETSI), no hay un glosario de términos. 5. Párrafos que contribuyen muy poco a entender el documento, como por ejemplo el que dice "La Tabla 1 muestra el análisis de las características de las normas y estándares" o el párrafo que dice "Pruebas de acceso y despliegue sobre el BlackBerry Thorch".
[EX A]: es

[EX Q]: This paper explores different strategies for instance-level image retrieval with deep CNNs. The approach consists of extracting features from a network pre-trained for image classification (e.g. VGG), and post-process them for image retrieval. In other words, the network is off-the-shelf and solely acts as a feature extractor. The post-processing strategies are borrowed from traditional retrieval pipelines relying on hand-crafted features (e.g. SIFT + Fisher Vectors), denoted by the authors as "traditional wisdom".  Specifically, the authors examine where to extract features in the network (i.e. features are neurons activations of a convolution layer), which type of feature aggregation and normalization performs best, whether resizing images helps, whether combining multiple scales helps, and so on.   While this type of experimental study is reasonable and well motivated, it suffers from a huge problem. Namely it "ignores" 2 major recent works that are in direct contradictions with many claims of the paper ([a] "End-to-end Learning of Deep Visual Representations for Image Retrieval" by  Gordo et al. and [b] "CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples" by Radenović et al., both ECCV'16 papers). These works have shown that training for retrieval can be achieved with a siamese architectures and have demonstrated outstanding performance. As a result, many claims and findings of the paper are either outdated, questionable or just wrong.  Here are some of the misleading claims:     - "Features aggregated from these feature maps have been exploited for image retrieval tasks and achieved state-of-the-art performances in recent years."   Until [a] (not cited), the state-of-the-art was still largely dominated by sparse invariant features based methods (see last Table in [a]).      - "the proposed method [...] outperforms the state-of-the-art methods on four typical datasets"   That is not true, for the same reasons than above, and also because the state-of-the-art is now dominated by [a] and [b].      - "Also in situations where a large numbers of training samples are not available, instance retrieval using unsupervised method is still preferable and may be the only option.".   This is a questionable opinion. The method exposed in "End-to-end Learning of Deep Visual Representations for Image Retrieval" by Gordo et al. outperforms the state-of-the-art on the UKB dataset (3.84 without QE or DBA) whereas it was trained for landmarks retrieval and not objects, i.e. in a different retrieval context. This demonstrates that in spite of insufficient training data, training is still possible and beneficial.    - Finally, most findings are not even new or surprising (e.g. aggregate several regions in a multi-scale manner was already achieved by Tolias at al, etc.). So the interest of the paper is limited overall.  In addition, there are some problems in the experiments. For instance, the tuning experiments are only conducted on the Oxford dataset and using a single network (VGG-19), whereas it is not clear whether these conditions are well representative of all datasets and all networks (it is well known that the Oxford dataset behaves very differently than the Holidays dataset, for instance). In addition, tuning is performed very aggressively, making it look like the authors are tuning on the test set (e.g. see Table 3).   To conclude, the paper is one year too late with respect to recent developments in the state of the art.
[EX A]: en

[EX Q]: In this paper a well known soft mixture of experts model is adapted for, and applied to, a specific type of transfer learning problem in reinforcement learning (RL), namely transfer of action policies and value functions between similar tasks. Although not treated as such, the experimental setup is reminiscent of hierarchical RL works, an aspect which the paper does not consider at length, regrettably. One possible implication of this work is that architecture and even learning algorithm choices could simply be stated in terms of the objective of the target task, rather than being hand-engineered by the experimenter. This is clearly an interesting direction of future work which the paper illuminates.   Pros: The paper diligently explains how the network architecture fits in with various widely used reinforcement learning setups, which does facilitate continuation of this work. The experiments are good proofs of concept, but do not go beyond that i.m.h.o.  Even so, this work provides convincing clues that collections of deep networks, which were trained on not entirely different tasks, generalize better to related tasks when used together rather than through conventional transfer learning (e.g. fine-tuning).  Cons: As the paper well recounts in the related work section, libraries of fixed policies have long been formally proposed for reuse while learning similar tasks. Indeed, it is well understood in hierarchical RL literature that it can be beneficial to reuse libraries of fixed (Fernandez & Veloso 2006) or jointly learned policies which may not apply to the entire state space, e.g. options (Pricop et. al). What is not well understood is how to build such libraries, and this paper does not convincingly shed light in that direction, as far as I can tell. The transfer tasks have been picked to effectively illustrate the potential of the proposed architecture, but the paper does not tackle negative transfer or compositional reuse in well known challenging situations outlined in previous work (e.g. Parisotto et. al 2015, Rusu el. al 2015, 2016). Since the main contributions are of an empirical nature, I am curious how the results shown in figures 6 & 7 look plotted against wall-clock time, since relatively low data efficiency is not a limitation for achieving perfect play in Pong (see Mnih. et al, 2015). It would be more illuminating to consider tasks where final performance is plausibly limited by data availability. It would also be interesting if the presented results were achieved with reduced amounts of computation, or reduced representation sizes compared to learning from scratch, especially when one of the useful source tasks is an actual policy trained on the target task. Finally, it is perhaps underwhelming that it takes a quarter of the data required for learning Pong from scratch just to figure out that a perfect Pong policy is already in the expert library. Simply evaluating each expert for 10 episodes and using an  average-score-weighted majority vote to mix action choices would probably achieve the same final performance for a smaller fraction of the data.
[EX A]:
en