niryuu/sni-each-converted huggingface dataset:
The paper looks at the problem of transferring a policy learned in a simulator to  a target real-world system.  The proposed approach considers using an ensemble of simulated source domains, along with adversarial training, to learn a robust policy that is able to generalize to several target domains.  Overall, the paper tackles an interesting problem, and provides a reasonable solution.  The notion of adversarial training used here does not seem the same as other recent literature (e.g. on GANs).  It would be useful to add more details on a few components, as discussed in the question/response round.  I also encourage including the results with alternative policy gradient subroutines, even if they don’t perform well (e.g. Reinforce), as well as results with and without the baseline on the value function. Such results are very useful to other researchers.
Output: en
El documento describe una forma de aplicar técnicas actuales de ingeniería informática al problema de líneas de productos de software. El documento está bien explicado, pero se considera necesario profundizar en los resultados y la evaluación. El trabajo descrito es interesante ya que aplica Algoritmos Genéticos a un problema concreto, pero carece de  impacto innovador en el dominio. Se recomienda revisar la redacción (en general) del documento.
Output: es
Update: After reading the rebuttal comments and the revised paper, I'm leaving the rating as it was before.  This paper proposes an unsupervised algorithm for transferring samples from one domain to another (related) domain under the constraint that some predefined f returns same result for the input and the result.  Pros: 1. The paper presents an interesting idea of comparing samples from different domains using a fixed perceptual function f.  2. The proposed method produces visually appealing results on several datasets  3. The authors demonstrate how their approach can be used for domain adaptation and obtain improved results on the SVHN->MNIST task  4. The paper is well-written and easy to read  Cons: 1. The novelty of the method is relatively minor (I consider f-constancy term as the main contribution)  2. It feels like the proposed approach would break for more dissimilar domains. The method relies on a fixed f which is trained on the source domain. This f can potentially drop information important for obtaining 1) better reconstructions in the target domain  2) more tightly related x and g(f(x)). I think the authors should consider either training all the modules in the model end-to-end or incorporating target samples into the training of f.  3. A single domain adaptation experiment is definitely not enough to consider the proposed method as a universal alternative to the existing DA approaches.  I would also like to point out that using super-resolved outputs as opposed to the actual model’s outputs can produce a false impression of the visual quality of the transferred samples. I’d suggest moving original outputs from the appendix into the main part.
Output: en
Paper Strengths:  -- Elegant use of MoE for expanding model capacity and enabling training large models necessary for exploiting  very large datasets in a computationally feasible manner  -- The effective batch size for training the MoE drastically increased also  -- Interesting experimental results on the effects of increasing the number of MoEs, which is expected.   Paper Weaknesses:  --- there are many different ways of increasing model capacity to enable the exploitation of very large datasets; it would be very nice to discuss  the use of MoE and other alternatives in terms of computational efficiency and other factors.
Output: en
El artículo presenta recomendaciones prácticas para el desarrollo de software seguro. Se describen las mejores prácticas recomendadas para desarrollar software que sea proactivo ante los ataques, y se realiza un análisis de costos de estas prácticas en desarrollo de software. Todo basado en una revisión de prácticas propuestas en la bibliografía y su contraste con datos obtenidos de una encuesta en empresas. Finalmente se recomienda una guía.  Sería ideal aplicar la guía propuesta a empresas no involucradas en la encuesta que sirvió para originarla de modo de poder evaluar su efectividad en forma independiente.
Output: es
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".
Output: es
[Summary] This paper proposes a new way for knowledge base completion which highlights: 1) adopting an implicit shared memory, which makes no assumption about its structure and is completely learned during training; 2) modeling a multi-step search process that can decide when to terminate.  The experimental results on WN18 and FB15k seem pretty good. The authors also perform an analysis on a shortest path synthetic task, and demonstrate that this model is better than standard seq2seq.  The paper is well-written and it is easy to follow.  [Major comments] I actually do like the idea and am also impressed that this model can work well. The main concern is that this paper presents too little analysis about how it works and whether it is sensitive to the hyper-parameters, besides that only reporting a final model on WN18 and FB15k.  One key hyper-parameter I believe is the size of shared memory (using 64 for the experiments). I don’t think that this number should be fixed for all tasks, at least it should depend on the KB scale. Could you verify this in your experiments? Would it be even possible to make a memory structure with dynamic size?  The RL setting (stochastic search process) is also one highlight of the paper, but could you demonstrate that how much it does really help? I think it is necessary to compare to the following: remove the termination gate and fix the number of inference steps and see how well the model does? Also show how the performance varies on # of steps?  I appreciate your attempts on the shortest path synthetic task. However, I think it would be much better if you can demonstrate that under a real KB setting. You can still perform the shortest path analysis, but using KB  (e.g., Freebase) entities and relations.  [Minor comments] I am afraid that the output gate illustrated in Figure 1 is a bit confusing. There should be only one output, depending on when the search process is terminated.
Output: en
Summary: The paper proposes a neural model for predicting Python syntax trees from text descriptions. Guided by the actual Python grammar, the model generates tree nodes sequentially in a depth-first fashion. Key ideas include injecting the information from the parent node as part of the LSTM input, a pointer network for copying the terminals, and unary closure which collapses chains of unary productions to reduce the tree size. The model is evaluated on three datasets from different domains and outperforms almost all previous work.  Strengths:  The paper is overall very well-written. The explanation of system is clear, and the analysis is thorough.  The system itself is a natural extension of various ideas. The most similar work include tree-based generation with parent feeding (Dong and Lapata, 2016) and various RNN-based semantic parsing with copy mechanism (Jia and Liang, 2016; Ling et al., 2016). [The guidance of parsing based on grammar is also explored in Chen Liang et al., 2016 ([Link] where a code-assist system is used to ensure that the code is valid.] Nevertheless, the model is this paper stands out as it is able to generate much longer and more complex programs than most previous work mentioned.   Weaknesses:  The evaluation is done on code accuracy (exact match) and BLEU score. These metrics (especially BLEU) might not be the best metrics for evaluating the correctness of programs. For instance, the first example in Table 5 shows that while the first two lines in boxes A and B are different, they have the same semantics. Another example is that variable names can be different. Evaluation based on what the code does (e.g., using test cases or static code analysis) would be more convincing.  Another point about evaluation: other systems (e.g., NMT baseline) may generate code with syntactic error. Would it be possible to include the result on the highest-scoring well-formed code (e.g., using beam search) that these baseline systems generate? This would give a fairer comparison since these system can choose to prune malformed code.  General Discussion:  * Lines 120-121: some approaches that use domain-specific languages were also guided by a grammar. One example is Berant and Liang, 2014, which uses a pretty limited grammar for logical forms (Table 1). In addition to comparing to that line of work, emphasizing that the grammar in this paper is much larger than most previous work would make this work stronger.  * Lines 389-397: For the parent feeding mechanism, is the child index being used? In other words, is p_t different when generating a first child versus a second child? In Seq2Tree (Dong and Lapata, 2016) the two non-terminals would have different hidden states.  * Line 373: Are the possible tokens embedded? Is it assumed that the set of possible tokens is known beforehand?  * The examples in the appendix are nice.  ---  I have read the author response.
Output: en
The paper introduces a variation to the CNN-based texture synthesis procedure of Gatys et al. that matches correlations between spatially shifted feature responses in addition to the correlations between feature responses at the same position in the feature maps.  The paper claims that this  a) improves texture synthesis for textures with long-range regular structures, that are not preserved with the Gatys et al. method b) improves performance on texture inpainting tasks compared to the Gatys et al. method c) improves results in season transfer when combined with the style transfer method by Gatys et al.  Furthermore the paper shows that d) by matching correlations between spatially flipped feature maps, symmetry properties around the flipping axis can be preserved.  I agree with claim a). However, the generated textures still have some issues such as greyish regions so the problem is not solved. Additionally, the procedure proposed is very costly which makes an already slow texture synthesis method substantially slower. For example, in comparison, the concurrent work by Liu et al. (
Output: en
Authors' response well answered my questions. Thanks.  Evaluation not changed.  ###  This paper proposes a neural model for generating tree structure output from scratch. The model does 1) separate the recurrence between depths and siblings; 2) separate the topology and label generation, and outperforms previous methods on a benchmark IFTTT dataset. Compared to previous tree-decoding methods, the model avoids manually annotating subtrees with special tokens, and thus is a very good alternative to such problems. The paper does solid experiments on one synthetic dataset, and outperforms alternative methods on one real-world IFTTT dataset.   There are couple of interesting results in the paper that I believe is worth further investigation. Firstly, on the synthetic dataset, the precision drops rapidly with the number of nodes. Is it because that the vector representation of the sequential encoder fails to provide sufficient information of long sequences, such that the tree decoder can not do a good job? Or is it because that such tree decoder is not tolerant to the long sequence input, i.e., large tree structure? I believe that it is important to understand this before a better model can be developed. For example, if it is the fault of encoder, maybe an attention layer can be added, as in a seq-to-seq model, to preserve more information of the input sequence.   Moreover, besides only showing how the precision changes with the number of nodes in the tree, it might be interesting to investigate how it goes with 1) number of depths; 2) number of widths; 3) symmetricity; etc. Moreover, as greedy search is used in decoding, it might be interesting to see how it helps, if it does, to use beam-search in tree decoding.   On the IFTTT dataset, listing more statistics about this dataset might be helpful for better understanding the difficulty of this task. How deep are the trees? How large are the vocabularies on both language and program sides?  The paper is well written, except for minor typo as mentioned in my pre-review questions.   In general, I believe this is a solid paper, and more can be explored in this direction. So I tend to accept it.
Output: en
The authors propose a simple idea. They penalize confident predictions by using the entropy of the predictive distribution as a regularizer. The authors consider two variations on this idea. In one, they penalize the divergence from the uniform distribution. In the other variation, they penalize distance from the base rates. They term this variation "unigram" but I find the name odd as I've never seen multi-class labels described as unigrams before. What would a bigram be?   The idea is simple,  and while it's been used in the context of reinforcement learning, it hasn't been popularized as a regularizer for improving generalization in supervised learning.   The justifications for the idea still lacks analysis. And the author responses comparing it to L2 regularization have some holes. A simple number line example with polynomial regression makes clear how L2 regularization could prevent a model from badly overfitting to accommodate every data point. In contrast, it seems trivial to fit every data point and satisfy arbitrarily high entropy. Of course, the un-regularized optimization is to maximize log likelihood, not simply to maximize accuracy.  And perhaps something interesting may be happening at the interplay between the log likelihood objective and the regularization objective. But the paper doesn't indicate precisely what.  I could imagine the following scenario: when the network outputs probabilities near 0, it can get high loss (if the label is 1). The entropy regularization could be stabilizing the gradient, preventing sharp loss on outlier examples. The regularization then might owe mainly to faster convergence. Could the authors analyze the effect empirically, on the distribution of the gradient norms?   The strength of this paper is its empirical rigor. The authors take their idea and put it through its paces on a host of popular and classic benchmarks spanning CNNs and RNNs. It appears that on some datasets, especially language modeling, the confidence penalty outperforms label smoothing.   At present, I rate this paper as a borderline contribution but I'm open to revising my review pending further modifications.   Typo: In related work: "Penalizing entropy" - you mean penalizing low entropy
Output: en
SUMMARY.  The paper proposes a machine reading approach for cloze-style question answering. The proposed system first encodes the query and the document using a bidirectional gru. These two representations are combined together using a Gated Attention (GA). GA calculates the compatibility of each word in the document and the query as a probability distribution. For each word in the document a gate is calculated weighting the query representation according to the word compatibility. Ultimately, the gate is applied to the gru-encoded document word. The resulting word vectors are re-encoded with a bidirectional GRU. This process is performed for multiple hops. After k hops, the probability of a word to be part of the answer is calculated by a log-linear model that take as input the last word representations, and the concatenation of the last query representation before and after the cloze token. The probability of a candidate being the answer to the question is given by a linear combination of the single word probabilities.  The proposed model is tested on 4 different dataset.  The authors shown that the proposed model works well (state-of-the-art performance) for 3 out of 4 benchmarks.   ----------  OVERALL JUDGMENT The main contribution of the paper is the gated attention mechanism, that in my opinion, is a simple and interesting idea. The paper is well thought, and the ablation study on the benefits given by the gated attention are convincing. The GA reader as whole model outperforms previous state-of-the-art models on 3 benchmarks and seems very promising also on the CBT dataset. I would have liked to see some discussion on why the model works less well on the CBT dataset, though.   ----------  DETAILED COMMENTS  minor. In the introduction, Weston et al., 2014 do not use any attention mechanism.
Output: en
This work proposes a convolutional architecture for any graph-like input data (where the structure is example-dependent), or more generally, any data where the input dimensions that are related by a similarity matrix. If instead each input example is associated with a transition matrix, then a random walk algorithm is used generate a similarity matrix.  Developing convolutional or recurrent architectures for graph-like data is an important problem because we would like to develop neural networks that can handle inputs such as molecule structures or social networks. However, I don't think this work contributes anything significant to the work that has already been done in this area.   The two main proposals I see in this paper are: 1) For data associated with a transition matrix, this paper proposes that the transition matrix be converted to a similarity matrix. This seems obvious. 2) For data associated with a similarity matrix, the k nearest neighbors of each node are computed and supply the context information for that node. This also seems obvious.  Perhaps I have misunderstood the contribution, but the presentation also lacks clarity, and I cannot recommend this paper for publication.   Specific Comments: 1) On page 4: "An interesting attribute of this convolution, as compared to other convolutions on graphs is that, it preserves locality while still being applicable over different graphs with different structures."  This is false; the other proposed architectures can be applied to inputs with different structures (e.g. Duvenaud et. al., Lusci et. al. for NN architectures on molecules specifically).
Output: en
Es un trabajo bien hecho sin lugar a dudas, el autor dice que hace una contrastación en varias organizaciones sobre el tema.  Faltan antecedentes en relación a los ERP investigados. El modelo que él plantea no es novedoso, es más bien lógico y práctico. Es una investigación normal y su aporte es regular. Creo que un análisis cómo este requiere un análisis más profundo en aspectos económicos y financieros, porque las decisiones para la adquisición de este tipo de herramientas son: operacionales, económicas y financieras.
Output: es
El trabajo presenta una encuesta a dos mineras sobre la etapa de arquitectura del proceso de outsourcing de TI. El trabajo es exploratorio y por lo tanto su contribución es reducida ya que aún no se presentan resultados. Creo que en este estado es inadecuado para la conferencia donde se espera la presentación de resultados y contribuciones a la investigación. El documento debe ser mejorado en cuanto a su redacción.
Output: es
Se presenta la necesidad de usar Objetos de Aprendizaje (OA) para la enseñanza del curso "Introducción a la Teoría de Autómatas". Sin embargo es muy poca la información dedicada en el artículo a los OA elegidos para el curso, no hay información de resultados de su aplicación ni sus consecuencias (resultados de aprendizaje, encuesta a los alumnos para validarlo, etc.). Es por ello que no se recomienda aceptar el artículo, salvo que se agregue información que respalde la aplicación de OA en este curso.
Output: es
This paper essentially presents a new inductive bias in the architecture of (convolutional) neural networks (CNN). The mathematical motivations/derivations of the proposed architecture are detailed and rigorous. The proposed architecture promises to produce equivariant representations with steerable features using fewer parameters than traditional CNNs, which is particularly useful in small data regimes. Interesting and novel connections are presented between steerable filters and so called “steerable fibers”. The architecture is strongly inspired by the author’s previous work, as well as that of “capsules” (Hinton, 2011). The proposed architecture is compared on CIFAR10 against state-of-the-art inspired architectures (ResNets), and is shown to be superior particularly in the small data regime. The lack of empirical comparison on large scale dataset, such as ImageNet or COCO makes this largely a theoretical contribution. I would have also liked to see more empirical evaluation of the equivariance properties. It is not intuitively clear exactly why this architecture performs better on CIFAR10 as it is not clear that capturing equivariances helps to classify different instances of object categories. Wouldn’t action-recognition in videos, for example, not be a better illustrative dataset?
Output: en
This nicely written paper presents an end-to-end learning method for image compression. By optimizing for rate-distortion performance and a clever relaxation the method is able to learn an efficient image compression method by optimizing over a database of natural images.  As the method is interesting, results are interesting and analysis is quite thorough it's easy for me to recommend acceptance.
Output: en
Comentarios generales: • Se recomienda revisar todo el escrito ya que hay palabras de más o palabras escritas de manera equivocada por ejemplo: imcompletas, ítemes (pag 3);  quedebe (pag 4), del de (pag 5)… etc • Revisar el espaciado de las palabras • Revisar la numeración de la bibliografía ya que tiene que ser listada en el orden en que ésta aparece. • Revisar que el texto sea del mismo tamaño  (ejemplo: final primera columna página 1)  Comentarios por secciones  II SCRUM YSPEM • Referenciar la parte dedicada a SCRUM así como la figura ya que no es propia de los autores si no tomada de alguien más  III LEVANTAMIENTO DE REQUERIMIENTOS UTILIZANDO SPEM • Revisar el concepto de stakeholder o referenciar de que referencia utilizó ese concepto, por ejemplo incluyo los conceptos de stakeholder para CMMI. “Parte interesada (stakeholder) Un grupo o individuo que se ve afectado por o es de alguna manera responsable del resultado de una empresa” “Parte interesada (stakeholder) Un grupo o individuo que se ve afectado por o es de alguna manera responsable del resultado de una empresa”  IV DESARROLLO DE SOFTWARE CON SCRUM • Título de Figura 5, puede mejorarse el título de la figura por ejemplo: modelo del proceso de desarrollo de software basado en SCRUM y utilizando SPEM para su modelado • Se recomienda reforzar la sección agregando referencias para los términos  V METRICAS PARA DEFINIR LA CALIDAD DEL DESARROLLO DEL SOFTWARE • Se recomienda agregar referencias cuando describe los conceptos de métricas  VI UN CASO PRÁCTICO • Se recomienda mejorar la descripción de la Tabla I (contenido de columnas y operación u operaciones llevadas a cabo para establecer las desviaciones estándar de peso y evaluación) • Hay una viñeta sin texto debajo de la Tabla 1 • Se recomienda apoyar la sección con una gráfica o tabla en la que se muestra un resumen de la medición de la calidad, para entender la medición “just in time” de la que habla en las conclusiones
Output: es
Es un buen artículo, y los autores son investigadores con bastante experiencia en el tema. Se recomienda su aceptación en su forma actual.
Output: es
El presente paper realiza una categorización de formas de integrar reglas de negocio en un diseño utilizando el paradigma orientado a aspectos y propone una plantilla de documentación para reglas de negocio basado en dicha categorización.  El paper entrega información útil sobre el problema en particular, así como una categorización razonable de diferentes aspectos en este problema. Es un buen punto de partida para una investigación en esa línea.  Aunque la información entregada en el paper es de suma relevancia para una investigación en este tema, es insuficiente para aceptarlo como un trabajo completo y autocontenido. La razón es que el paper se enfoca en identificar problemas y proponer un artefacto (la plantilla) que debería resolver dichos problemas. Sin embargo no presenta evidencias de por qué esta plantilla es una buena alternativa para resolver dichos problemas. Para ello se requieren los elementos mencionados en el trabajo futuro, específicamente la especificación formal y la herramienta automática para crear plantillas y mapearlas a AOP.  Mi sugerencia es esperar a que estos elementos estén listos para completar este paper y enviarlo a una nueva conferencia.
Output: es
Approaches like adaptive dropout also have the binary mask as a function of input to a neuron very similar to the proposed approach. It is not clear, even from the new draft, how the proposed approach differs to Adaptive dropout in terms of functionality. The experimental validation is also not extensive since comparison to SOTA is not included.
Output: en
This is a solidly executed paper that received good reviews. However, the originality is a bit lacking. In addition, the paper would have been stronger with a comparison to the method proposed in Zweig et al. (2013). We recommend this paper for the workshop.
Output: en
The paper presents a simple but effective approach for pruning ConvNet filters with extensive evaluation using several architectures on ImageNet and CIFAR-10.
Output: en
this proposes a multi-view learning approach for learning representations for acoustic sequences. they investigate the use of bidirectional LSTM with contrastive losses. experiments show improvement over the previous work.  although I have no expertise in speech processing, I am in favor of accepting this paper because of following contributions: - investigating the use of fairly known architecture on a new domain. - providing novel objectives specific to the domain - setting up new benchmarks designed for evaluating multi-view models  I hope authors open-source their implementation so that people can replicate results, compare their work, and improve on this work.
Output: en
Este artículo presenta para medir la calidad de los procesos de software. El título ya fue algo que me sorprendió. Existen miles de debates en qué se entiende por calidad en los procesos de software y los autores no concretan cuáles son la base de la que parten. Además, la clasificación que hacen en los apartados desde las etapas de desarrollo, los procesos de desarrollo es algo de lo que existen muchas alternativas en la literatura y los autores se centran en una que parecen dar o tomar como el referente más adecuado. La visión práctica en el caso concreto de INAPI sí que es de interés pero creo que hubiera sido más importante el decir porqué se aplican las cosas que intentar definir una visión global y como estándar de los conceptos con los que trabajo. Por otro lado, hay otras propuestas metodológicas para aplicar 9001 en el contexto del software. Creo que habría que potenciar de manera más adecuada el que se defienda qué aporta esta en realidad.
Output: es
From my original comments:  The results looks good but the baselines proposed are quite bad.  For instance in the table 2 "Misclassification rate for a 784-1024-1024-1024-10 " the result for the FC with floating point is 1.33%. Well far from what we can obtain from this topology, near to 0.8%. I would like to see "significant" compression levels on state of the art results or good baselines. I can get 0,6% with two FC hidden layers...  In CIFAR-10 experiments, i do not understand  why "Sparsely-Connected 90% + Single-Precision Floating-Point" is worse than "Sparsely-Connected 90% + BinaryConnect". So it is better to use binary than float.   Again i think that in the experiments the authors are not using all the techniques that can be easily applied to float but not to binary (gaussian noise or other regularizations). Therefore under my point of view the comparison between float and binary is not fair. This is a critic also for the original papers about binary and ternary precision.   In fact with this convolutional network, floating (standard) precision we can get lower that 9% of error rate. Again bad baselines.  ----  The authors reply still does not convince me.  I still think that the same technique should be applied on more challenging scenarios.
Output: en
Trabajo muy mal presentado, con severos errores de ortografía y redacción. Pese al tema, interesante, no es posible encontrar un hilo adecuado en el desarrollo del trabajo.  Desde el inicio surgen los problemas, en el resumen se afirma que "el objetivo de este artículo es la realización de un análisis a un conjunto de registros de diagnósticos médicos, puestos a manera de investigación, en busca de una mejor perspectiva y visualización de los datos para responder a los indicadores que se plantearon" Primero, el objetivo del artículo no es la realización del análisis, sino mostrar el resultado de algún proceso (el proceso puede ser el análisis).La frase "puestos a manera de investigación" es del todo inadecuada.  No se responde a los indicadores..., ¿qué sentido tiene eso?  Las citas en itálico incluyen a los autores; faltó apoyo de alguien más experimentado en la escritura del artículo.  Lamentablemente, el trabajo está plagado de errores inaceptables.  No sólo de escritura.  La página 3 indica que uno de los problemas encontrados dice relación con la "Estructuración de datos". ¿Cuál es el problema? ¿Por qué se produce? ¿Cómo lo enfrenta el autor? ¿Es satisfactorio su enfoque?  Creo que esos antecedentes (que se repiten de manera similar en toda la extensión del texto), justifican mi rechazo.  Resumiendo, es muy pobre el trabajo.
Output: es
This paper introduces a polynomial linear model for supervised classification tasks. The model is based on a combination of the Tensor Train (TT) tensor decomposition method and a form of stochastic Riemannian  optimization. A few empirical experiments are performed that demonstrate the good performance of the proposed model relative to appropriate baselines.  From a theoretical standpoint, I think the approach is interesting and elegant. The main machinery underlying this work are the TT decomposition and the geometric structure of the manifold of tensors with fixed TT-rank, which have been established in prior work. The novelty of this paper is in the combination of this machinery to form an efficient polynomial linear model. As such, I would have hoped that the paper mainly focused on the efficacy of this combination and how it is superior to obvious alternatives. For example, I would have really appreciated seeing how FMs performed when optimized over the manifold of positive definite matrices, as another reviewer mentioned. Instead, there is a bit too much effort devoted to explaining prior work.  I think the empirical analysis could be substantially improved. I am particularly puzzled by the significant performance boost obtained from initializing with the ordinary logistic regression solution. I would have liked some further analysis of this effect, especially whether or not it is possible to obtain a similar performance boost with other models. Regarding the synthetic data, I think an important baseline would be against a vanilla feed forward neural network, which would help readers understand how complicated the interactions are and how difficult the dataset is to model. I agree with the previous reviewer regarding a variety of other possible improvements to the experimental section.  A few typos: 'Bernoulli distrbution', 'reproduce the experiemnts', 'generilize better'.  Overall, I am on the fence regarding this paper. The main idea is quite good, but insufficient attention was devoted to analyzing the aspects of the model that make it interesting and novel.
Output: en
Me ha gustado mucho  Lo dejaría tal cual.
Output: es
Se recomienda mejorar los aspectos de redacción tales como:  - Corregir y tener cuidado con la redacción del abstract en inglés. - La figura 1 es ilegible y se encuentra parcialmente cubierta por una tabla. - La justificación necesita ser re-escrita, no queda claro el propósito del trabajo. - La tabla 1 se encuentra cortada, debe estar en una sola página. - Entre la tabla 2 y la tabla 3 el texto no se encuentra justificado. - Figura 3, 4 y 5 se encuentran cortadas.  Problemas de fondo:  - Las teorías del dominio son muy extensas y no aportan a la problemática propuesta por el trabajo. - La aplicación posee serios problemas de usabilidad, entre ellos el contraste entre el texto y el fondo de la aplicación. Se recomienda estudiar un poco de [Link] - No se describe la tecnología utilizada para el desarrollo de la aplicación, pero al parecer es HTML. Para este tipo de aplicaciones el desarrollo debería ser nativo. - Se esperaba una evaluación de la utilización de la misma por parte de los usuarios. - Las conclusiones no hacen referencia expresa a la construcción y resultados obtenidos al utilizar la aplicación.
Output: es
El formato simplemente pésimo, la redacción deja también bastante que desear, las figuras borrosas, tablas escaneadas de algún lugar sin referencia. El contenido no existe. Este debe ser uno de los artículos más malos que he visto en mi vida,
Output: es
The authors agree with the reviewers that this manuscript is not yet ready.
Output: en
- Strengths:  This paper presents a sophisticated application of Grid-type Recurrent Neural Nets to the task of determining predicate-argument structures (PAS) in Japanese.  The approach does not use any explicit syntactic structure, and outperforms the current SOA systems that do include syntactic structure.  The authors give a clear and detailed description of the implementation and of the results.  In particular, they pay close attention to the performance on dropped arguments, zero pronouns, which are prevalent in Japanese and especially challenging with respect to PAS. Their multi-sequence model, which takes all of the predicates in the sentence into account, achieves the best performance for these examples.  The paper is detailed and clearly written.  - Weaknesses:  I really only have minor comments. There are some typos listed below, the correction of which would improve English fluency. I think it would be worth illustrating the point about the PRED including context around the "predicate" with the example from Fig 6 where the accusative marker is included with the verb in the PRED string.  I didn't understand the use of boldface in Table 2, p. 7.  - General Discussion:  Typos:  p1 :  error propagation does not need a "the", nor does "multi-predicate interactions" p2: As an solution -> As a solution, single-sequence model -> a single-sequence model,                    multi-sequence model -> a multi-sequence model  p. 3 Example in Fig 4.                    She ate a bread -> She ate bread. p. 4 assumes the independence -> assumed independence, the multi-predicate interactions -> multi-predicate interactions, the multi-sequence model -> a multi-sequence model p.7: the residual connections -> residual connections, the multi-predicate interactions -> multi-predicate interactions (twice) p8 NAIST Text Corpus -> the NAIST Text Corpus, the state-of-the-art result -> state-of-the-art results  I have read the author response and am satisfied with it.
Output: en
Thank you for an interesting read.  Given the huge interest in generative modelling nowadays, this paper is very timely and does provide very clear connections between methods that don't use maximum likelihood for training. It made a very useful observation that the generative and the discriminative loss do **not** need to be coupled with each other. I think this paper in summary provides some very useful insights to the practitioners on how to select the objective function to train the implicit generative model.  The only reason that I decided to hold back my strong acceptance recommendation is that I don't understand the acceptance criteria of ICLR. First this paper has the style very similar to the Sugiyama et al. papers that are cited (e.g. presenting in different perspectives that were all covered in those papers but in a different context), making me unsure about how to evaluate the novelty. Second this paper has no experiment nor mathematical theorem, and I'm not exactly sure what kinds of contributions the ICLR committee is looking for.
Output: en
The paper presents a method to learn graph embeddings in a unsupervised way using random walks. It is well written and the execution appears quite accurate. The area of learning whole graph representations does not seem to be very well explored in general, and the proposed approach enjoys having very few competitors.  In a nutshell, the idea is to linearize the graph using random walks and to compute the embedding of the central segment of each walk using the skip-thought criterion. Being not an expert in biology, I can not comment whether or not this makes sense, but the gains reported in Table 2 are quite significant.   An anonymous public comment compared this work to a number of others in which the problem of learning representations of nodes is considered. While this is arguably a different goal, one natural baseline would be to pool these representations using mean- or max- pooling. It would very interesting to do such a comparison, especially given that the considered approach heavily relies on pooling (see Figure 3(c))  To sum up, I think it is a nice paper, and with more baselines I would be ready to further increase the numerical score.
Output: en
- Strengths:  This paper tackles an interesting problem and provides a (to my knowledge) novel and reasonable way of learning and combining cognitive features with textual features for sentiment analysis and irony detection. The paper is  clearly written and organized, and the authors provided a lot of useful detail and informative example and plots. Most of the results are convincing, and the authors did a good job comparing their approach and results with previous work.  - Weaknesses:  1. Just from the reading abstract, I expected that the authors' approach would significantly outperform previous methods, and that using both the eye-gaze and textual features consistently yields the best results. Upon reading the actual results section, however, it seems like the findings were more mixed. I think it would be helpful to update the abstract and introduction to reflect this.  2. When evaluating the model on dataset 1 for sentiment analysis, were the sarcastic utterances included? Did the model do better on classifying the non-sarcastic utterances than the sarcastic ones? 3. I understand why the eye-movement data would be useful for sarcasm detection, but it wasn't as obvious to me why it would be helpful for (non-sarcastic) sentiment classification beyond the textual features.   - General Discussion:  This paper contains a lot of interesting content, and the approach seems solid and novel to me. The results were a little weaker than I had anticipated from the abstract, but I believe would still be interesting to the larger community and merits publication.
Output: en
This paper describes an approach to learning the non-linear activation function in deep neural nets.  This is achieved by representing the activation function in a basis of non-linear functions and learning the coefficients.  Authors use Fourier basis in the paper.  A theoretical analysis of the proposed approach is also presented, using algorithmic stability arguments, to demonstrate good generalization behavior (vanishing generalization error with large data sets) of networks with learnt non-linearities.  The main question I have about this paper is that writing a non-linear activation function as a linear or affine combination of other non-linear basis functions is equivalent to making a larger network whose nodes have the basis functions as non-linearities and whose weights have certain constraints on them.  Thus, the value of the proposed approach of learning non-linearities over optimizing network capacity for a given task (with fixed non-linearities) is not clear to me.  Or could it be argued that the constrained implied by learnt non-linearity approach are somehow good thing to do?  Another question - In the two stage training process for CNNs, when ReLU activation is replaced by NPFC(L,T), is the NPFC(L,T) activation initialized to approximate ReLU, or is it initialized using random coefficients?  Few minor corrections/questions: - Pg 2. “ … the interval [-L+T, L+T] …” should be “ … the interval [-L+T, L-T] … “ ? - Pg 2., Equation for f(x), should it be “ (-L+T) i \pi x / L “ in both sin and cos terms, or without “ x “ ? - Theorem 4.2 “ … some algorithm \eps-uniformly stable …” remove the word “algorithm” - Theorem 4.5.  SGM undefined
Output: en
El artículo presenta el diseño de un Datamart para el registro de diagnósticos médicos. Además, presenta resultados "genéricos" de lo realizado.  No se visualiza una nueva propuesta, sino una experiencia práctica, que utiliza registros médicos de Practice Fusion, una comunidad de historial clínico electrónico. Presenta el artículo indicando que es muy común usar los métodos del paper en empresas de rubro comercial, y ahora la "propuesta" sería aplicarlos al área de salud. Sin embargo, lo más probable es que para el área de salud ya se haya considerado un trabajo similar.  Tampoco considera una situación real, para ver el resultado de su aplicación, lo cual podría haber sido más interesante.  Las Referencias 7 a la 11 no son usadas en el texto.  Otras observaciones menores: - Cambiar "Palabras Claves" por "Palabras Clave" - Hace referencia a "indicadores clave" e "indicadores claves" (la primera es la correcta) - No hace referencia a figuras en el texto. - Corregir "a al peso", "restringen a base", "indicadores plateados", "donde existente" - Cambiar "REFERENCES" por "REFERENCIAS".
Output: es
This work gives expected results and creates a structure for further research. I would suggest in addition of compass the use of radio waves for improving localization (WiFi for indoor and GPS for outdoor). In addition, there are some low cost platforms in the market that can be used and study, as for example Ardupilot or other similar systems. I was unable to see clearly enough, in the paper, the use of the camera; algorithms, environment settings, etc.; used for following robot. In Figure 7, it seems that camera trajectory is worse than that calculated with odometry, perhaps due to orientation error, but in my opinion it is not very clear during reading. Finally, I would suggest think if is it there some possible feedback of EKF results to odometry in order to reduce its error.
Output: en
The paper presents a general approach to modeling for natural language understanding problems with two distinct textual inputs (such as a question and a source text) that can be aligned in some way. In the approach, soft attention is first used to derive alignments between the tokens of the two texts, then a comparison function uses the resulting alignments (represented as pairs of attention queries and attention results) to derive a representations that are aggregated by CNN into a single vector from which an output can be computed. The paper both presents this as an overall modeling strategy that can be made to work quite well, and offers a detailed empirical analysis of the comparison component of the model.  This work is timely. Language understanding problems of this kind are a major open issue in NLP, and are just at the threshold of being addressable with representation learning methods. The work presents a general approach which is straightforward and reasonable, and shows that it can yield good results. The work borders on incremental (relative to their earlier work or that of Parikh et al.), but it contributes in enough substantial ways that I'd strongly recommend acceptance.  Detail:  - The model, at least as implemented for the problems with longer sequences (everything but SNLI), is not sensitive to word order. It is empirically competitive, but this insensitivity places a strong upper bound on its performance. The paper does make this clear, but it seems salient enough to warrant a brief mention in the introduction or discussion sections. - If I understand correctly, your attention strategy is based more closely on the general/bilinear strategy of Luong et al. '15 than it is on the earlier Bahdanau work. You should probably cite the former (or some other more directly relevant reference for that strategy). - Since the NTN risks overfitting because of its large number of parameters, did you try using a version with input dimension l and a smaller output dimension m (so an l*l*m tensor)? - You should probably note that SubMultNN looks a lot like the strategy for *sentence*-level matching in the Lili Mou paper you cite. - Is there a reason you use the same parameters for preprocessing the question and answer in (1)? These could require different things to be weighted highly.
Output: en
- El artículo describe varios enfoques para elaborar requisitos, y aplica uno de ellos (GAIA) a la construcción de sistemas multi-agentes (MAS).  - La Introducción es larguísima y cubre muchos temas; debería ser dividida en varias secciones, y dejar en Introducción sólo el contexto general y descripción del resto del artículo. - No hay un ejemplo para ilustrar la técnica, que es descrita en 1,5 páginas, siendo lo esencial de la contribución. - No hay validación de tipo alguno.
Output: es