niryuu/sni-each-converted huggingface dataset:
- Strengths:   - The paper is clearly written and well-structured.    - The system newly applied several techniques including global optimization to end-to-end neural relation extraction, and the direct incorporation of the parser representation is interesting.   - The proposed system has achieved the state-of-the-art performance on both ACE05 and CONLL04 data sets.   - The authors include several analyses.  - Weaknesses:   - The approach is incremental and seems like just a combination of existing methods.     - The improvements on the performance (1.2 percent points on dev) are relatively small, and no significance test results are provided.  - General Discussion:  - Major comments:   - The model employed a recent parser and glove word embeddings. How did they affect the relation extraction performance?   - In prediction, how did the authors deal with illegal predictions?  - Minor comments:   - Local optimization is not completely "local". It "considers structural correspondences between incremental decisions," so this explanation in the introduction is misleading.   - Points in Figures 6 and 7 should be connected with straight lines, not curves.   - How are entities represented in "-segment"?   - Some citations are incomplete. Kingma et al. (2014) is accepted to ICLR, and Li et al. (2014) misses pages.
Output: en
This work reframes paragraph vectors from a generative point of view and in so doing, motivates the existing method of inferring paragraph vectors as well as applying a L2 regularizer on the paragraph embeddings. The work also motivates joint learning of a classifier on the paragraph vectors to perform text classification.  The paper has numerous citation issues both in formatting within the text and the formatting of the bibliography, e.g. on some occasions including first names, on others not. I suggest the authors use a software package like BibTex to have a more consistent bibliography. There seems to be little novelty in this work.   The authors claim that there is no proposed method for inferring unseen documents for paragraph vectors. This is untrue. In the original paragraph vector paper, the authors show that to get a new vector, the rest of the model parameters are held fixed and gradient descent is performed on the new paragraph vector. This means the original dataset is not needed when inferring a paragraph vector for new text. This work seems to be essentially doing the same thing when finding the MAP estimate for a new vector. Thus the only contribution from the generative paragraph vector framing is the regularization on the embedding matrix.  The supervised generative paragraph vector amounts to jointly training a linear classifier on the paragraph vectors, while inference for the paragraph vector is unchanged. For the n-gram based approach, the authors should cite Li et al., 2015.  In the experiments, table 1 and 2 are badly formatted with .0 being truncated. The authors also do not state the size of the paragraph vector. Finally the SGPV results are actually worse than that reported in the original paragraph vector paper where SST-1 got 48.7 and SST-2 got 86.3.  Bofang Li, Tao Liu, Xiaoyong Du, Deyuan Zhang, Zhe Zhao, Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews, 2015.
Output: en
Me parece un trabajo interesante, principalmente por aportar evidencia empírica sobre técnicas de co-creación. Su contenido y estructura están técnicamente bien presentados. No obstante, el trabajo tiene omisiones relevantes para valorar su contribución científica. Por ejemplo, no presenta análisis estadístico de los datos arrojados. Solo expone una distribución de los resultados sin evidenciar significancia estadística. También extraño una sección (o en las conclusiones) sobre limitaciones a la validez del experimento. Creo que tiene varias amenazas a la validez que no han sido tratadas. Por nombrar algunas: no se explica si la asignación a los grupos fue aleatoriamente; el tiempo de las sesiones es distinta; el aprendizaje en sesiones anteriores, etc.  Otras sugerencias:  pág. 1	Título	Creo que no es adecuado el título. No representa el core del trabajo que es un experimento. pág. 4	Segunda columna	Errores en formato de viñetas  pág. 4	Tercera viñeta segunda columna	cada VEZ que  pág. 5	Última viñeta primera columna	paréntesis
Output: es
Interesting application domain.  Nothing new from a machine learning perspective. They authors should provide more information about the models they have obtained with the different classifiers (feature subset selection used, selected features, accuracies, statistical significance on the differences on accuracies, the models themselves, ...). Semi-supervised classification is the appropriate method for the last paragraph in the "Methodology" section.
Output: en
El artículo muestra el diseño de una investigación sobre el impacto de las competencias TI en la percepción del éxito profesional, pero no es lo que señala en su título. Se observa una sección correspondiente a una extensa revisión de trabajos previos y definiciones, en comparación con la sección que muestra el diseño de la investigación, la cual es bastante breve. En mi opinión era conveniente haber presentado el artículo como una revisión sistemática sobre la literatura existente del tema con el objeto de encontrar brechas y/o similitudes en el diseño de la investigación, o haber puesto más énfasis en esto último. Si bien el tema puede ser novedoso, el artículo no lo demuestra, ya que solo es la presentación de un diseño de investigación.  En la sección de conclusiones, no se muestran conclusiones concretas a partir del trabajo realizado, solo se plantean posibles escenarios de un trabajo futuro.  Con respecto a la forma, el artículo se encuentra bien estructurado, pero se observan detalles a corregir:  - abuso de “TI” - en algunos párrafos se señalan trabajos previos sin ser referenciados - la figura del modelo es muy básica (no es un aporte) - párrafos no alineados (justificados) - la figura 1 debió ser una tabla - error en la correlación de figuras
Output: es
El artículo presenta una propuesta para una plataforma de generación de contenidos educacionales que permite acceso ubicuo. Este tema es de mucha actualidad e interés.  Al parecer habría sido presentado en otro congreso, con muy pequeñas variaciones y que sería bueno destacar y especificar esas similitudes y/o diferencias.  La redacción y uso de referencias es correcta.
Output: es
UPDATE: The authors addressed all my concerns in the new version of the paper, so I raised my score and now recommend acceptance. -------------- This paper combines the recent progress in variational autoencoder and autoregressive density modeling in the proposed PixelVAE model. The paper shows that it can match the NLL performance of a PixelCNN with a PixelVAE that has a much shallower PixelCNN decoder. I think the idea of capturing the global structure with a VAE and modeling the local structure with a PixelCNN decoder makes a lot of sense and can prevent the blurry reconstruction/samples of VAE. I specially like the hierarchical image generation experiments.  I have the following suggestions/concerns about the paper:  1) Is there any experiment showing that using the PixelCNN as the decoder of VAE will result in better disentangling of high-level factors of variations in the hidden code? For example, the authors can train a PixelVAE and VAE on MNIST with 2D hidden code and visualize the 2D hidden code for test images and color code each hidden code based on the digit and show that the digits have a better separation in the PixelVAE representation. A semi-supervised classification comparison between VAE and the PixelVAE will also significantly improve the quality of the paper.  2) A similar idea is also presented in a concurrent ICLR submission "Variational Lossy Autoencoder". It would be interesting to have a discussion included in the paper and compare these works.  3) The answer to the pre-review questions made the architecture details of the paper much more clear, but I still ask the authors to include the exact architecture details of all the experiments in the paper and/or open source the code. The clarity of the presentation is not satisfying and the experiments are difficult to reproduce.  4) As pointed out in my pre-review question, it would be great to include two sets of MNIST samples maybe in an appendix section. One with PixelCNN and the other with PixelVAE with the same pixelcnn depth to illustrate the hidden code in PixelVAE actually captures the global structure.  I will gladly raise the score if the authors address my concerns.
Output: en
Este trabajo muestra los resultados del desarrollo de un CBIR basado en los índices de distancias métricas, comparándolo con sistemas existentes a través de la precisión de recuperación y comparando los resultados de eficiencia en tiempo para distintos enfoques de indexación como GNAT, NAT, etc. El trabajo aborda un problema importante de los sistemas de recuperación, su estructura es correcta y los resultados aparentan ser fundamentados.  Quizás los criterios de evaluación no son suficientes para poder determinar la superioridad de un enfoque sobre otro, aunque los autores reconocen las principales falencias respecto a este punto y visualizan trabajos futuros en esta dirección. La explicación de la sección 2.3, sobre la precisión, es poco clara en definir como se conoce de ante mano sobre las correctas imágenes más similares a una consulta para poder calcular la precisión. Los gráficos de tiempo de la figura 3, no indican la escala.
Output: es
Summary: This paper introduces a heuristic approach for training a deep directed generative model, where similar to the transition operator of a Markov chain each layer samples from the same conditional distribution. Similar to optimizing a variational lower bound, the approach is to approximate the gradient by replacing the posterior over latents with an alternative distribution. However, the approximating distribution is not updated to improve the lower bound but heuristically constructed in each step. A further difference to variational optimization is that the conditional distributions are optimized greedily rather than following the gradient of the joint log-likelihood.  Review: The proposed approach is interesting and to me seems worth exploring more. Given that there are approaches for training the same class of models which are 1) theoretically more sound, 2) of similar computational complexity, and 3) work well in practice (e.g. Rezende & Mohamed, 2015), I am nevertheless not sure of its potential to generate impact. My bigger concern, however, is that the empirical evaluation is still quite limited.  I appreciate the authors included proper estimates of the log-likelihood. This will enable and encourage future comparisons with this method on continuous MNIST. However, the authors should point out that the numbers taken from Wu et al. (2016) are not representative of the performance of a VAE. (From the paper: “Therefore, the log-likelihood values we report should not be compared directly against networks which have a more flexible observation model.” “Such observation models can easily achieve much higher log-likelihood scores, […].”)  Comparisons with inpainting results using other methods would have been nice. How practical is the proposed approach compared to other approaches? Similar to the diffusion approach by Sohl-Dickstein et al. (2015), the proposed approach seems to be both efficient and effective for inpainting. Not making this a bigger point and performing the proper evaluations seems like a missed opportunity.  Minor: – I am missing citations for “ordered visible dimension sampling” – Typos and frequent incorrect use of \citet and \citep
Output: en
El trabajo nos presenta una alternativa de plataforma factible de ser considerada en robótica con fines educativos, dado que dependiendo del nivel en que se utilice puede enfocarse el estudio del móvil a alguna de las áreas que se ven involucradas, como la instrumentación (sensores y actuadores) o bien en la programación (navegación del móvil). Si bien el autor no hace referencia en profundidad al aspecto electrónico, es evidente la posibilidad de enfocarse también en la comunicación existente entre las placas o bien en el sistema de control de los motores, que ya por si sólo constituye una problemática de interés. Además se plantea la posibilidad de realizar mejoras, tal vez incorporando sensores de diversos tipos que permitan captar mayor información del entorno. Todo esto contribuye a que la plataforma Amadeus pueda ser considerada como una referencia para la didáctica de la robótica y la investigación.  Tanto el desarrollo del tema como las conclusiones planteadas tienen coherencia con lo presentado en el resumen del artículo, se destaca su utilización en cursos de pre y post-grado, lo cual fortalece su postura como herramienta didáctica y de investigación.
Output: es
Thank you for an interesting read.   To my knowledge, very few papers have looked at transfer learning with **no** target domain data (the authors called this task as "extrapolation"). This paper clearly shows that the knowledge of the underlying system dynamics is crucial in this case. The experiments clearly showed the promising potential of the proposed EQL model. I think EQL is very interesting also from the perspective of interpretability, which is crucial for data analysis in scientific domains.  Quesions and comments:  1. Multiplication units. By the universal approximation theorem, multiplication can also be represented by a neural network in the usual sense. I agree with the authors' explanation of interpolation and extrapolation, but I still don't quite understand why multiplication unit is crucial here. I guess is it because this representation generalises better when training data is not that representative for the future?  2. Fitting an EQL vs. fitting a polynomial. It seems to me that the number of layers in EQL has some connections to the degree of the polynomial. Assume we know the underlying dynamics we want to learn can be represented by a polynomial. Then what's the difference between fitting a polynomial (with model selection techniques to determine the degree) and fitting an EQL (with model selection techniques to determine the number of layers)? Also your experiments showed that the selection of basis functions (specific to the underlying dynamics you want to learn) is crucial for the performance. This means you need to have some prior knowledge on the form of the equation anyway!  3. Ben-David et al. 2010 has presented some error bounds for the hypothesis that is trained on source data but tested on the target data. I wonder if your EQL model can achieve better error bounds?  4. Can you comment on the comparison of your method to those who modelled the extrapolation data with **uncertainty**?
Output: en
RESUMEN:  El trabajo se presenta y describa la aplicación de una red social para noticias (4x4News), donde las noticias más relevantes estarán presentes (entre otras características). Esta aplicación se caracteriza por una pantalla divida en una matriz de 16x16, donde cada celda  es una noticia.   EVALUACIÓN GENERAL:  El trabajo está bien escrito y sigue un formato (casi) completamente adecuado para INFONOR. La aplicación suena interesante y potencialmente útil como un add-on para redes sociales ya comúnmente usadas (ej. Facebook).  Hay varios puntos independientes que considero que son importantes de resolver:  1) Si bien es un trabajo de ingeniería interesante, no puedo ver cuál es el aporte/contribución desde el punto científico ¿Qué se puede re-utilizar de este trabajo para un futuro trabajo científico?  2) Dado que ustedes ofrecen una aplicación, creo que es fundamental un link para (com)probar esta aplicación.  3) En mi opinión, el sitio Web [Link] se aproxima a lo propuesto por este trabajo. Estó es así, porque terra permite registro de usuarios (perfiles), comentar noticias, valorar noticias, y configurar el sitio Web para mostrar las noticias que un usuario desea.  4) Este podría ser un detalle menor, pienso que el uso de redes sociales se podría usar para algo más que solamente mostrar la noticia más importante (ej. mostrar una especie de tracking (social) de una(s) noticia(s) similar).  DETALLES MENORES:  - Al final de la introducción, el uso de BOLD no es recomendado. - “Los titulares no realizan un buen análisis”. ¿Qué es un “buen” análisis?. Durante el trabajo, se observa varios adjetivos usados de manera ambiguas y que son innecesarios (ej. “complejo proceso” en la sección 2.1). - Dentro de la sección 2: “Desarrollo”, se encuentra el trabajo relacionado (sección 2.1). Esta sección debería ser re-estructurada. - Las citas del tipo “X, y otros, AÑO” (ej. sección 2.2 “Wilson, y otros, 2014”), creo que están incorrectas. Deberían usar “et al”. - Sección 2.2, ¿la coincidencia de palabras es suficiente para decir que dos noticias son similares? Creo que un análsis más profundo es un necesario. - Explicar un poco más el concepto “bundles”. - En las referencias se usan números [1], pero en el texto no usa números. Recomiendo seguir el formato de INFONOR.
Output: es
- Strengths: The authors have nice coverage of a different range of language settings to isolate the way that relatedness and amount of morphology interact (i.e., translating between closely related morphologically rich languages vs distant ones) in affecting what the system learns about morphology. They include an illuminating analysis of what parts of the architecture end up being responsible for learning morphology, particularly in examining how the attention mechanism leads to more impoverished target side representations. Their findings are of high interest and practical usefulness for other users of NMT.   - Weaknesses: They gloss over the details of their character-based encoder. There are many different ways to learn character-based representations, and omitting a discussion of how they do this leaves open questions about the generality of their findings. Also, their analysis could've been made more interesting had they chosen languages with richer and more challenging morphology such as Turkish or Finnish, accompanied by finer-grained morphology prediction and analysis.  - General Discussion: This paper brings insight into what NMT models learn about morphology by training NMT systems and using the encoder or decoder representations, respectively, as input feature representations to a POS- or morphology-tagging classification task. This paper is a straightforward extension of "Does String-Based Neural MT Learn Source Syntax?," using the same methodology but this time applied to morphology. Their findings offer useful insights into what NMT systems learn.
Output: en
- Strengths: The paper addresses a relevant topic: learning the mapping between natural language and KB relations, in the context of QA (where we have only partial information for one of the arguments), and in the case of having a very large number of possible target relations.  The proposal consists in a new method to combine two different representations of the input text: a word level representation (i.e. with segmentation of the target relation names and also the input text), and relations as a single token (i.e without segmentation of relation names nor input text).   It seems, that the main contribution in QA is the ability to re-rank entities after the Entity Linking step.  Results show an improvement compared with the state of the art.   - Weaknesses: The approach has been evaluated in a limited dataset.   - General Discussion:  I think, section 3.1 fits better inside related work, so the 3.2 can become section 3 with the proposal. Thus, new section 3 can be splitted more properly.
Output: en
Me parece un artículo interesante que pudo centrarse más en el desarrollo de una aplicación sobre Kurogo (un producto atractivo descrito en el artículo) que en el estudio de la ubicuidad de tecnologías para su interoperabilidad.  En este punto me saltan diversas dudas, ya que el estudio concluye la siguiente configuración de interoperabilidad:   "DVB-2T como el estándar internacional de transmisión de TVDi escogido por Colombia, MHP como el middleware para la TVDi, MPEG como el formato de codificación y decodificación de audio y video de los contenidos y SCORM como estándar óptimo para la generación de contenidos."  Eso significa que en los estudios se debió utilizar transmisión por tv digital??? Y, por lo que entiendo, se usó un "logitech revenue" lo que convierte al tv en una salida de una conexión a internet, por lo que sería lo mismo que las comparaciones sobre un PC común. ¿Estoy en lo correcto?  Con esta duda, aún me parece que el artículo puede ser interesante de considerar (weak accept) y aclarar los conceptos en la presentación del mismo.
Output: es
Muy buen artículo para comprender la ubicación espacial de objetos, con propósitos de control, en este caso aplicable a un conjunto de robot en un juego de futbol.
Output: es
This paper presents a generative model of video sequence data where the frames are assumed to be generated by a static background with a 2d sprite composited onto it at each timestep.  The sprite itself is allowed to dynamically change its appearance and location within the image from frame to frame.  This paper follows the VAE (Variational Autoencoder) approach, where a recognition/inference network allows them to recover the latent state at each timestep.  Some results are presented on simple synthetic data (such as a moving rectangle on a black background or the “Moving MNIST” data.  However, the results are preliminary and I suspect that the assumptions used in the paper are far too strong too be useful in real videos.  On the Moving MNIST data, the numerical results are not competitive to state of the art numbers.  The model itself is also not particularly novel and the work currently misses some relevant citations.  The form of the forward model, for example, could be viewed as a variation on the DRAW paper by Gregor et al (ICML 2014).  Efficient Inference in Occlusion-Aware Generative Models of Images by Huang & Murphy (ICLR) is another relevant work, which used a variational auto-encoder with a spatial transformer and an RNN-like sequence model to model the appearance of multiple sprites on a background.  Finally, the exposition in this paper is short on many details and I don’t believe that the paper is reproducible from the text alone.  For example, it is not clear what the form of the recognition model is…  Low-level details (which are very important) are also not presented, such as initialization strategy.
Output: en
The authors proposed to use leaky rectified linear units replacing binary units in Gaussian RBM.  A sampling method was presented to train the leaky-ReLU RBM. In the experimental section, AIS estimated likelihood on Cifar10 and SVHN were reported.   It's interesting for trying different nonlinear hidden units for RBM. However, there are some concerns for the current work.  1. The author did not explain why the proposed sampling method (Alg. 2) is correct. And the additional computation cost (the inner loop and the projection) should be discussed.  2. The results (both the resulting likelihood and the generative samples) of Gaussian RBM are much worse than what we have experienced. It seems that the Gaussian RBM were not trained properly.  3. The representation learned from a good generative model often helps the classification task when there are fewer label samples. Gaussian RBM works well for texture synthesis tasks in which mixing is an important issue. The authors are encouraged to do more experiments in these two direction.
Output: en
This paper makes three main methodological contributions:  - definition of Neural Feature (NF) as the pixel average of the top N images that highly activation a neuron  - ranking of neurons based on color selectivity  - ranking of neurons based on class selectivity  The main weaknesses of the paper are that none of the methodological contributions are very significant, and no singularly significant result arises from the application of the methods.  However, the main strengths of the paper are its assortment of moderately-sized interesting conclusions about the basic behavior of neural nets. For example, a few are:  - “Indexing on class selectivity neurons we found highly class selective neurons like digital-clock at conv2, cardoon at conv3 and ladybug at conv5, much before the fully connected layers.” As far as I know, this had not been previously reported.  - Color selective neurons are found even in higher layers. (25% color selectivity in conv5)  - “our main color axis emerge (black-white, blue-yellow, orange-cyan and cyan- magenta). Curiously, these two observations correlate with evidences in the human visual system (Shapley & Hawken (2011)).” Great observation!  Overall, I’d recommend the paper be accepted, because although it’s difficult to predict at this time, there’s a fair chance that one of the “smaller conclusions” would turn out to be important in hindsight a few years hence.   Other small comments:  - The cite for “Learning to generate chairs…” is wrong (first two authors combined resulting in a confusing cite)   - What exactly is the Color Selectivity Index computing? The Opponent Color Space isn’t well defined and it wasn’t previously familiar to me. Intuitively it seems to be selecting for units that respond to a constant color, but the highest color selectivity NF in Fig 5 i for a unit with two colors, not one. Finally, the very last unit (lowest color selectivity) is almost the same edge pattern, but with white -> black instead of blue -> orange. Why are these considered to be so drastically different? This should probably be more clearly described.   - For the sake of argument, imagine a mushroom sensitive neuron in conv5 that fires highly for mushrooms of *any* color but not for anything else. If the dataset contains only red-capped mushrooms, would the color selectivity index for this neuron be high or low? If it is high, it’s somewhat misleading because the unit itself actually isn’t color selective; the dataset just happens only to have red mushrooms in it. (It’s a subtle point but worth considering and probably discussing in the paper)
Output: en
Es una aplicación interesante, sin embargo los KPI no deben ser equivalentes a las medidas que finalmente quedarán en la tabla de hechos. Por ejemplo, el indicador promedio de defunciones por año, se debe implementar a través de la medida "cantidad de defunciones" que es una medida aditiva y se puede agregar por los distintos niveles de la dimensión (así puede obtenerse el KPI promedio por dia, mes, trimestre, etc). Hay varias dimensiones que están agrupadas en el modelo, siendo que son distintas (por ejemplo, caracteristica_madre). Se debe corregir el diseño, es posible que los resultados obtenidos por el sistema estén con errores.
Output: es
The paper describes an experience concerning the automated inspection of spectra for the Pipeline Hubble Legacy Archive Grism data.  Comments:  In the conclusions the authors say that "We have identified two classes of flawed spectra which were not picked up by the automatic classification because of their very small number of training samples. Per construction machine learning techniques can not classify such outliers." This sentence should be further explained. As Hastie, Tibshirani, and Friedman say in The Elements of Statistical Learning (see Chapter 7): "it is too difficult to give a general rule on how much training data is enough; among other things, this depends on the signal-to-noise ratio of the underlying function, and the complexity of the models being fit to the data. " So, by just saying that the number of training samples is small you do not provide enough information to decide whether machine learning techniques are adequate or not to solve a problem.  Other minor comments:  + If reference [2] has not yet been written or published it should be deleted from the paper.  The dataset may be of scientific importance.  It is mainly descriptive.
Output: en
I reviewed the manuscript as of December 7th.  Summary: The authors investigate the transferability of adversarial examples in deep networks. The authors confirm that transferability exists even in large models but demonstrate that it is difficult to manipulate the network to adversarially perturb an image into a specifically desired label. The authors additionally demonstrate real world attacks on a vision web service and explore the geometric properties of adversarial examples.  Major Comments: 1. The paper contains a list of many results and it is not clear what single message this paper provides. As mentioned in the comments, this paper is effectively 15 pages and 9 page of results in the Appendix heavily discussed throughout the main body of the paper. Although there is no strict page limit for this conference, I do feel this pushes the spirit of a conference publication. I do not rule out this paper for acceptance based on the length but I do hold it as a negative because clarity of presentation is an important quality. If this paper is ultimately accepted, I would suggest that the authors make some effort to cut down the length even further beyond the 13 pages posted elsewhere. I have marked some sections to highlight areas that may be trimmed.  2. The section of geometric understanding is similar to results of 'Adversarial Perturbations of Deep Neural Networks' in Warde-Farley and Goodfellow (2015). See Figure 1.2. I am not clear what the authors show above-and-beyond these results. If there are additional findings, the authors should emphasize them.  3. The authors expand on observations by Goodfellow et al (2014) and Szegedy et al (2013) demonstrating that large-scale models are susceptible to adversarial perturbations (see also Kurakin et al (2016)). The authors additionally demonstrate that attempting to perform adversarial manipulation to convert an image to a particular, desired label is more difficult.  4. The authors demonstrate that they can target a real-world vision API. These results are compelling but it is not clear what these results demonstrate above-and-beyond Papernot et al (2016).   As far I can understand, I think that the most interesting result from this paper not previously described in the literature is to note about the unique difficulty about performing adversarial manipulation to convert an image to a particular, desired label. The rest of the results appear to expand on other results that have already appeared in the literature and the authors need to better explain what these makes these results unique above-and-beyond previous work.  Areas to Trim the Paper: - Table 1 is not necessary. Just cite other results or write the Top-1 numbers in the text. - Condense Section 2.2.1 and cite heavily. - Figure 2 panels may be overlaid to highlight a comparison.
Output: en
This paper describes a way to speed up convergence through sudden increases of otherwise monotonically decreasing learning rates. Several techniques are presented in a clear way and parameterized method is proposed and evaluated on the CIFAR task. The concept is easy to understand and the authors chose state-of-the-art models to show the performance of their algorithm. The relevance of these results goes beyond image classification.   Pros:  - Simple and effective method to improve convergence - Good evaluation on well known database   Cons:  - Connection of introduction and topic of the paper is a bit unclear - Fig 2, 4 and 5 are hard to read. Lines are out of bounds and maybe only the best setting for T_0 and T_mult would be clearer. The baseline also doesn't seem to converge  Remarks: An loss surface for T_0 against T_mult would be very helpful. Also understanding the relationship of network depth and the performance of this method would add value to this analysis.
Output: en
A pesar de que el tema referente a la problemática de la navegación autónoma de robots móviles es de interés, el trabajo presentado no necesariamente tendría que serlo. Sin embargo a medida que el lector aborda en profundidad el documento es posible darse cuenta que el trabajo presentado constituye un aporte al tema. Ya desde el título se plantea con claridad lo que el lector encontrará en el documento, lo cual se ve reforzado con un planteamiento sencillo del tema por parte del autor facilitando la comprensión del trabajo realizado. Por otra parte, se presenta un prototipo cuya implementación es posible de replicar en laboratorio por quien se sienta interesado en el tema, lo cual contribuye a que este trabajo sea considerado como posible referencia en el ámbito educacional.
Output: es
Here is a summary of the reviews:    Strengths  Experiments are done on state-of-the-art networks, on a real speech recognition problem (R3, R1)  Networks themselves are of a very large size (R3)  Computational gains are substantial (R3, R4)  Paper is clear (R1)    Weaknesses  Experiments are all done on a private dataset (R3)  No comparison to other pruning approaches (e.g. Han et al.) (R3); AC notes that reviewers added new results which compare to an existing pruning method  No comparison to distillation techniques (R1)  Paper doesn't present much novelty in terms of ideas (R3)    The AC encouraged feedback from the reviewers following author rebuttal and paper improvements. Reviewers stated that the improvements made to the paper made it publishable but was still closer to the threshold. R1 who had originally rated the paper 3: a "clear reject" updated the score to 6 (just above acceptance).    Considering the reviews and discussions, the AC thinks that this paper is a poster accept. There are no serious flaws, the improvements made to the paper during the discussion paper have satisfied the reviewers, and this is an important topic with practical benefits; evaluated on a real large-scale problem.
Output: en
Este paper presenta una herramienta (SAVE) para la verificación de arquitectura de software. SAVE realiza un chequeo estático de reglas asociadas a dependencias permitidas y prohibidas entre elementos del diseño y del código, y permite entregar un reporte sobre la consistencia entre ambos niveles respecto a dichas reglas.  En general la herramienta parece bastante poderosa por dos motivos: el uso de reglas sencillas y la inclusión de sets de reglas pre-definidas para patrones de diseño de amplia utilización. Esto, a mi juicio, es la diferencia más importante con otros frameworks similares.  La redacción es, en algunos lugares, poco objetiva. En particular sugeriría evitar el uso excesivo de adjetivos para los cuales no se provee de evidencia que los soporte. Por ejemplo, en varios lugares noté el uso de "poderosa herramienta", "herramienta intuitiva" o similares (no solamente referidos a SAVE, sino a otros trabajos). En todos esos casos, no se provee en forma explícita una evidencia que soporte el uso de dichos adjetivos.  Otro detalle que requiere arreglo es el título que aparece en el archivo PDF: "Predicción de la dinámica del comportamiento...", el cual no coincide con el título del paper. Probablemente los autores utilizaron una plantilla de un paper anterior con dicho título.
Output: es
El trabajo presenta un metodo de detección de fracturas en imágenes de pozos.  El trabajo en general, es muy poco convincente por varias razones:  1.- Invierte MUCHO espacio en describir los fundamentos pero no comparar y/o discutir con el estado del arte. Existen MUCHOS métodos adaptivos de clasificación de imágenes, y el problema que se presenta no tiene nada diferente, salvo una aplicación diferente.  2.- No está clara la hipótesis, por lo que no se puede medir la contribución del trabajo.  3.- El metodo de detección que se describe solo define formulas matemáticas para la caracterización geométrica de las fracturas. Esto es un procedimiento bastante poco robusto y más aún, los resultados evaluando el metodo tampoco son convincentes en  métricas estándares utilizadas (accuracy, error,  ROC curve, etc).  4.- La metodología experimental es muy débil, tampoco compara el metodo con el estado del arte, ni usa métricas estándares para analizar la precision/exactitud del metodo.
Output: es
The paper introduces an extension of the entity grid model. A convolutional neural network is used to learn sequences of entity transitions indicating coherence, permitting better generalisation over longer sequences of entities than the direct estimates of transition probabilities in the original model.  This is a nice and well-written paper. Instead of proposing a fully neural approach, the authors build on existing work and just use a neural network to overcome specific issues in one step. This is a valid approach, but it would be useful to expand the comparison to the existing neural coherence model of Li and Hovy. The authors admit being surprised by the very low score the Li and Hovy model achieves on their task. This makes the reader wonder if there was an error in the experimental setup, if the other model's low performance is corpus-dependent and, if so, what results the model proposed in this paper would achieve on a corpus or task where the other model is more successful. A deeper investigation of these factors would strengthen the argument considerably.  In general the paper is very fluent and readable, but in many places definite articles are missing (e.g. on lines 92, 132, 174, 488, 490, 547, 674, 764 and probably more). I would suggest proofreading the paper specifically with article usage in mind. The expression "...limits the model to do X...", which is used repeatedly, sounds a bit unusual. Maybe "limits the model's capacity to do X" or "stops the model from doing X" would be clearer.  --------------  Final recommendation adjusted to 4 after considering the author response. I agree that objective difficulties running other people's software shouldn't be held against the present authors. The efforts made to test the Li and Hovy system, and the problems encountered in doing so, should be documented in the paper. I would also suggest that the authors try to reproduce the results of Li and Hovy on their original data sets as a sanity check (unless they have already done so), just to see if that works for them.
Output: en
First, let me praise the authors for generating and releasing an NLP data set: a socially useful task.  The authors use an algorithm to generate a 500-cluster-per-language data set in semantic similarity. This brings up a few points.  1. If the point of using the algorithm is to be scalable, why release such a small data set? It's roughly the same order of magnitude as the data sets released in the SemEval tasks over the recent years. I would have expected something orders of magnitude larger.  2. The authors hand checked a small subset of the clusters: they found one where it was ambiguous, and should probably have been removed. Mechanical Turk can scale pretty well -- why not post-facto filter all of the clusters using MT? This is (in effect) how ImageNet was created, and it has millions of items.  3. Evaluating data set papers is an tricky issue. What makes a data set "good" or publishable? There are a number of medium-sized NLP data sets released every year (e.g., through SemEval). Those are designed to address tasks in NLP that people find interesting. I don't know of a data set that exactly addresses the task that the authors propose: the task is trying to address the idea of semantic similarity, which has had multiple data sets thrown at it since SemEval 2012. I wish that the paper had included comparisons to show that the particular task / data combination is better suited for analyzing semantic similarity than other existing data sets.  Two final notes:  A. This paper doesn't seem very well-suited to ICLR.  New NLP data sets may be indirectly useful for evaluating word embeddings (and hence representations). But, I didn't learn much from the paper: GloVe is empirically less good for semantic similarity than other embeddings? If true, why? That would be interesting.  B. The first proposal for the "put a word into a cluster and see if it stands out" task (in the context of human evaluation of topic models), is  Jonathan Chang, Jordan Boyd-Graber, Chong Wang, Sean Gerrish, and David M. Blei. Reading Tea Leaves: HowHumans Interpret Topic Models. Neural Information Processing Systems, 2009  which deserves a citation, I think.
Output: en
El artículo es interesante por la temática que aborda y los resultados de los análisis realizados, más que por la metodología en sí. Me quedan dudas acerca de cómo se alimentan los análisis con la nueva información y cómo se integra el sistema a los sistemas transaccionales de la USACH. Sin embargo, es una aplicación interesante.
Output: es
This is an interesting article, present develop of a mobile robot use as beach cleaner.  I have 2 comments about the design, I think will be interesting define what is the dimension of collector and storage compartment for know what is the capacity of this cleaner.  In page 2 in the first paragraph a typo mistake was found.
Output: en
Poca información sobre aspectos técnicos utilizados en el desarrollo del sistema.
Output: es
Problemas de forma identificados:  - Figura 7 no es clara en su contenido. - Figura 5 y 6 no aportan al trabajo desarrollado. - El proceso de transacción debería estar numerado. - Cuadro debería ser Tabla, además la tabla 2, 3 y 4 no son legibles. Se deberían re-escribir. - Página 3 el texto de la palabra Samsung no es del mismo tipo de fuente. - Figura 8 no tiene título. - Se debe corregir y mejorar la ortografía (conclusiones).   Problemas de fondo:  - El apartado 3.1.2 no se encuentra relacionado directamente con el problemática planteada. - La descripción de las arquitecturas y modalidades de utilización de NFC no aporta a la solución expuesta. - En la sección 4.1 se hace mención a la máquina virtual Dalvik, sin embargo, desde la versión de Android Lollipop hacia arriba, se utiliza ART y no Dalvik. - La tabla 6 no aporta el trabajo desarrollado. - El trabajo desarrollado utiliza 8 de 11 páginas en introducción y tecnologías, sin embargo, sólo se utiliza 1 página para mostrar las evidencias. - Se plantean algunos indicadores, sin embargo, no se da evidencia de los valores obtenidos. - Las figuras 10, 11 y 12 muestran capturas de la aplicación?, sin embargo, no se indican en detalle la forma de aplicación de las tecnologías en Android. - Falta evidencias de la aplicación descrita, en particular desde al área de la ingeniería de software.
Output: es
Un muy buen trabajo, presenta integración de varias tecnologías de una forma clara y coherente  A pesar de que lo mencionan como trabajo futuro, se debería tener en cuenta el HL7 desde ya
Output: es
Un muy buen artículo. Solo sugiero lo siguiente:  Las tablas en la sección de resultados deben ser explicadas.  Las tablas solo contienen el menor, mayor y media entre esos dos valores. Sería interesante considerar también la media general y la desviación estándar.  En las tablas 4, 5 y 6 solo algunas líneas contienen un valor destacado, las veces el mayor de la línea y otras el menor... Explicar mejor el significado de esas marcaciones en todas las tablas.  La referencia [12] comienza con "and" y no con el nombre del primer  autor (de echo parece que están con problemas también el resto de los autores).  La referencia [22] es la única que no usa iniciales para el nombre del autor.
Output: es
El artículo realiza un recorrido por definiciones de calidad, calidad de software y modelos de calidad. Algunos modelos se tratan primero en una sección preliminar y otros luego del análisis teórico, sugiero re-organizar la presentación de conceptos. En mi opinión, los conceptos de modelo de calidad y modelo conceptual no son equivalentes, aunque se tratan de forma conjunta en la introducción.  En la segunda sección se presentan varios modelos, pero no se establece claramente las relaciones entre ellos y cuál es el criterio para tenerlos en cuenta en esta línea de investigación. Existen múltiples propuestas de modelos de calidad de software y modelos específicos para web. Por otro lado, las guías de OWASP son relevantes para la seguridad de aplicaciones web, no son un modelo de calidad.  Sugiero exponer el análisis teórico (que incluye definiciones básicas de calidad) antes de la descripción y justificación de los modelos considerados. Por otro lado, se podrían agregar conceptos / atributos / modelos de la calidad específicos en el contexto de los sistemas bancarios.  En mi opinión, dedicar tanto espacio a definir el concepto de banca electrónica,  banca por internet y luego el de banca móvil, no resulta necesario en el estado del arte actual y el conocimiento público de estos conceptos. Sugiero centrarse en los objetivos de investigación de este trabajo en particular.  Las descripciones del marco metodológico DESMET y la metodología investigación – acción son muy breves. Es necesario establecer antes los objetivos de la investigación.  Los modelos conceptuales obtenidos son el resultado principal del trabajo. Se deben utilizar notaciones más legibles (sugiero también poner información extendida como anexo del artículo o en la web). Se debería explicar además como estos modelos fueron validados, para que sean confiables si son utilizados para definir ontologías o usados en trabajos posteriores.
Output: 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.
Output: en