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].

El articulo muestra un estudio de aplicacion de Algoritmos Geneticos para resolver un sistema de ecuaciones en imagenes opticas de deteccion de tumores y seguimiento de tratamientos oncologicos. El trabajo esta bien escrito y tiene un alto nivel de detalle tecnico. Sin embargo, no muestra explicitamente cuales son sus principales contribuciones. En este sentido seria conveniente ampliar la Seccion de Introduccion para entregar al lector un detalle de las principales contribuciones del trabajo y ademas incluir en esta seccion (o en una seccion de Trabajo Relacionados o Estado del Arte) un mayor detalle de otros trabajos previos que esten relacionados con la propuesta de los autores. En el ambito de aplicacion de Algoritmos Geneticos para resolucion de sistemas de ecuaciones, este trabajo no presenta un aporte novedoso, ya que existen varios trabajos previos orientados a este tipo de solucion. Respecto al analisis de imagenes y reconocimiento de patrones, hay trabajos previos que ya utilizan Algoritmos Geneticos para este tipo de propuestas, tales como Dourdan, 2015. Seria conveniente haber tenido una comparativa con otros metodos de Machine Learning que se han aplicado ampliamente al reconocimiento de imagenes tales como k-means o PCA. Menores: - En la figura 10 no se visualiza bien la serie Solucion AG por la cantidad de puntos graficados. - Ultimo parrafo de Conclusiones. " ... la ventaja de trabajar con heuristicas como el algoritmo geneticos..." debiera ser "... la ventaja de trabajar con heuristicas como los algoritmos geneticos...". - Ultimo parrafo de Conclusiones. "... es factible que el AG de buena aproximacion..." creo que se debe cambiar la redaccion, no se entiende en la primera lectura. -- Referencias. Las referencias a trabajos de Pattern Recognition es antigua 1997, creo que hay referencias mas actualizadas y hay mayor cantidad de trabajos donde se aplican diversas tecnicas de machine learning para reconocimiento de patrones, desde supervised a unsupervised metodos.
es

- Strengths: A well written paper, examining the use of context in lexical entailment task is a great idea, a well defined approach and experimental set-up and good analysis of the results - Weaknesses: Some information is missing or insufficient, e.g., the table captions should be more descriptive, a clear description for each of the word type features should be given. General Discussion: The paper presents a proposal of consideration of context in lexical entailment task. The results from the experiments demonstrate that context-informed models do better than context-agnostic models on the entailment task. I liked the idea of creating negative examples to get negative annotations automatically in the two ways described in the paper based on WordNet positive examples. (new dataset; an interesting method to develop dataset) I also liked the idea of transforming already-used context-agnostic representations into contextualized representations, experimenting with different ways to get contextualized representations (i.e., mask vs contetx2vec), and testing the model on 3 different datasets (generalizability not just across different datasets but also cross-linguistically). Motivations for various decisions in the experimental design were good to see, e.g., why authors used the split they used for CONTEXT-PPDB (it showed that they thought out clearly what exactly they were doing and why). Lines 431-434: authors might want to state briefly how the class weights were determined and added to account for the unbalanced data in the CONTEXT-WN experiments. Would it affect direct comparisons with previous work, in what ways? Change in Line 589: directionality 4 --> directionality, as in Table 4 Suggested change in Line 696-697: is-a hierarchy of WordNet --> "is-a" hierarchy of WordNet For the sake of completeness, represent "mask" also in Figure 1. I have read the author response.
en

This paper proposes an alternative to Conditional Variational Auto-Encoders and Conditional MultiModal Auto-Encoders to perform inference of missing modalities in dataset with multiple modalities. The proposed approach is a Variational Auto-Encoder jointly on all the modalities with additional KL divergence penalties between the approximate posterior given all the modalities and the approximate posterior given a subset of the modalities. The approach is named Joint Multimodal Variational Auto-Encoder. The authors make a connection between this approach and the Variation of Information. It is unclear why the authors chose the JMVAE approach instead of a more elegant Variation of Information approach. Another unaddressed issue is the scalability of the method. As far as I can tell (given that no code is provided and the specification of the encoder is missing), this approach requires a new encoder per subset of missing modalities. Right now this approach seems to scale since there are only two modalities. The fact that the estimating the log-likelihood log(p(x)) using multiple modalities provide a lower value than with just one in Table 1 is a bit odd. Could you explain that ? The comparison with between the representation learned by JMVAE and CVAE might be unfair given that the representation of CVAE is learned conditionally, on the label in the case of MNIST, and should therefore not consider the label in this representation. Intuitively, this representation could represent "style" as shown in (Kingma et al., 2014) in their conditional generation figure. For CelebA, comparing log-likelihood on models that use GANs is probably not significant since GAN does not optimizes log-likelihood. Overall this is an interesting problem and there are also interesting ideas worth exploring further, but the execution of the paper requires more work.
en