niryuu/sni-each-converted huggingface dataset:
Los autores presentan una propuesta de herramienta para la medición de valor y trazabilidad, sin embargo, a raíz de los errores de forma y redacción, no queda claro el aporte a la disciplina.  Sería deseable la exposición de la aplicación de la herramienta propuesta, pero sobre actividades de la disciplina.   Forma: - Falta el abstract en inglés. - La tabla 1 se encuentra cortada entre páginas. - El formato posee mucho margen entre el texto y el borde de la página. - No se cita correctamente las figuras. - La introducción es MUY extensa, se puede reducir el número de párrafos y mantener la claridad de lo expuesto. - Las variables definidas en el documento: Expectativas, Calidad, etc. deberían ser enumeradas. - La enumeración de los capítulos tienen distinta fuente. - ".. se validó la el modelo.." ?
Output: es
Se presentan los siguientes problemas de forma:  - Figura 1: el texto de la figura no es legible y además se encuentra deformado. - Figura 2: no representa la problemática expuesta en el trabajo, además no se encuentra referenciada. - Ortografía: ".. del uso de La tecnologia NFC ..", debe ser: ".. del uso de la tecnología NFC ..". - Faltan puntos a parte. - Ortografía: ".. aplicaciones com relacion al uso .." debe ser: ".. aplicaciones con relación al ..". - Tabla 1: Título incorrecto y tabla cortaba en su final. - Cambio de tamaño de letra después de la tabla 1 - Tabla 2: Texto deformado. - A lo largo del texto no posee referencias a las afirmaciones: "es muy difícil intervenir una comunicación NFC". - Las etapas del proceso de transacción deben estar enumeradas. - Las tablas se encuentran mal numeradas. - Las páginas no se encuentran numeradas. - La descripción de indicadores (sección 7) no aporta al trabajo propuesto.   Problemas de fondo:  - No presenta una solución formal al pago vía tecnología NFC, a pesar que es el punto central del trabajo. - Las interfaces gráficas presentadas no se encuentran definidas en conformidad con las normas gráficas de aplicaciones móviles. - Las conclusiones no arrojan luz sobre la problemáticas planteada.  El trabajo desarrollado no es claro sobre la problemática planteada, debería incluir un análisis más detallado de tecnologías ya implementadas: UberTaxi, etc.
Output: es
• Los títulos no van con “.” • Resumen   o“El objetivo de este documento es intentar recolectar…” --> sugiero quitar la palabra intentar •Abstract   oRevisar la gramática ya que se encuentran falta de artículos y algunas frases mal redactadas por ejemplo:    -	The images emerging technologies -->  the images technology emerging    -	such as machine vision systems and scanning --> such as the machine vision systems and the scanning…    -	…The objective of this paper is to try to collect… --> The objective of this paper is to try to collect    -	 the state of the art processing for 3d point cloud processing, going from the filters to the segmentation --> the state of the art of  processing the 3d point for the cloud processing going from the filters to the segmentation   oTexto en general revisar la falta de acentos y las palabras duplicadas, entre otros:    -Introducción: máquinas    -Trabajo realizado:       •	…que en general, que --> segundo que sobra       •	Trabajo realizado   oSimplificación de puntos, la segunda vez que pone la referencia [3] sobra    - La primera vez que aparecen unas siglas se deben incluir su significado RANSAC?       •	Segmentación   o...Muchas investigaciones --> cuáles? Incluir referencia de ese conjunto de muchas investigaciones   oExiste la palabra convolución? En la RAE no   oPresen-tan --> presentan       • Conclusiones   oFormato de las conclusiones       •	Referencias   oCompletar la referencias: 2,3,4,5,6,7 y 11       •	El artículo se queda en una recopilación de los trabajos enfocados en filtrado y documentación de la nube de puntos, recomiendo ir más allá, esto es hacer un mapeo de los trabajos que mencionas, el primero de los de simplificación y el segundo de los de segmentación basado en las características, alcance, debilidades y oportunidades de mejora identificadas, esto para que realmente hagas una aportación con tu artículo
Output: es
This paper describes a correction technique to combine updates from multiple SGD to make it statistically equivalent to sequential technique. Comments 1) The proposed method is novel and interesting to allow update to be corrected even when the update is delayed. 2) The proposed theory can only be applied to square loss setting (with linear update rule), making it somewhat limited. This paper would be much more interesting to ICLR community, if the technique is applicable to general objective function and settings of deep neural networks. 3) The resulting technique requires book-keeping of a dimensional reduced combiner matrix, which causes more computation in terms of complexity. The authors argue that the overhead can be canceled with SIMD support for symbolic update. However, the normal update of SGD might also benefit from SIMD, especially when the dataset is dense.  Overall, even though the practical value of this work is limited by 2) and 3), the technique(specifically the correction rule) proposed in the paper could be of interest to people scaling up learning. I would encourage the author to extend the method to the cases of non-linear objective function which could make it more interesting to the ICLR community
Output: en
- Strengths: The idea of hard monotonic attention is new and substantially different from others.  - Weaknesses: The experiment results on morphological inflection generation is somewhat mixed. The proposed model is effective if the amount of training data is small (such as CELEX). It is also effective if the alignment is mostly monotonic and less context sensitive (such as Russian, German and Spanish).  - General Discussion:  The authors proposed a novel neural model for morphological inflection generation which uses "hard attention", character alignments separately obtained by using a Bayesian method for transliteration. It is substantially different from the previous state of the art neural model for the task which uses "soft attention", where character alignment and conversion are solved jointly in the probabilistic model.  The idea is novel and sound. The paper is clearly written. The experiment is comprehensive. The only concern is that the proposed method is not necessarily the state of the art in all conditions. It is suitable for the task with mostly monotonic alignment and with less context sensitive phenomena. The paper would be more convincing if it describe the practical merits of the proposed method, such as the ease of implementation and computational cost.
Output: en
El trabajo presenta una investigación en desarrollo relacionada con el costo que tiene la contratación de servicios de TI para las organizaciones, específicamente se propone un caso de estudio en la Industria Minera de Chile. El trabajo está enfocado en un caso de negocios, sin embargo aunque el documento hace énfasis en la recopilación teórica de los conceptos asociados a los aspectos financieros y de costos de los servicios contratados de TI, el caso de estudio no está claro y el artículo parece incompleto. La estructura del documento genera confusión debido a que en la introducción se establecen objetivos asociados al caso de estudio pero al finalizar el documento los resultados no se presentan porque la investigación está en desarrollo. Se sugiere acotar el artículo y estructurarlo mejor, de tal forma que se puedan presentar los resultados preliminares obtenidos hasta el punto en el que se encuentra el proyecto de investigación, es decir una revisión conceptual de aspectos financieros para Servicios de TI, sin generar expectativas frente al desarrollo de estadísticas o análisis de costos que todavía no se han calculado. Para tener un fuerte énfasis en la revisión de conceptos, el artículo tiene pocas referencias. Se recomienda mejorar los conceptos y la base conceptual a partir de una revisión de literatura amplia que permita fundamentar la investigación y presentar resultados parciales de la revisión de conceptos. Existen deficiencias acentuadas en la redacción del documento. Desde el resumen faltan letras y los signos de puntuación no son claros. En párrafos como el primero de la segunda columna de la página 3 es difícil de entender la idea y eso hace difícil la lectura del documento. En apartados de definición de conceptos tales como Costos de TI (pag 3) es necesario referenciar las afirmaciones conceptuales (base fundamental del artículo). Se recomienda tener en cuenta los términos en inglés unificar la forma de citar términos o conceptos. Algunos no pueden ser traducidos, pero es recomendable manejar conceptos en un solo idioma. Además, es recomendable definir el objetivo del artículo y enfocarlo a un solo fin. Es decir, si se quiere presentar la metodología paras el diseño de la investigación se debe hacer énfasis en ella. O bien, si lo que se quiere es hacer énfasis en los cálculos establecidos o diseñados por medio de la herramienta enfocar entonces el artículo en este objetivo. Como recomendación general se sugiere enfocar el artículo en una revisión conceptual de Costos financieros de la contratación de Servicios de TI en las empresas de Chile, teniendo en cuenta que la investigación está en una etapa inicial.
Output: es
Un buen tema, en términos de convocar a la sociedad en las utilidades y aplicaciones de las TICs   Se dice en la propuesta que: "se desarrollaran" métodos, "se proporcionará" una arquitectura de apoyo a la toma de decisiones, "se podrá" medir la receptividad, "se desarrollará" un inventario de las TICs. Desgraciadamente, nada de lo anterior se pudo concretar con estudios que sustenten lo anterior. Se hacen juicios de valor sin tener el respaldo suficiente. No se menciona en la introducción la información de organismos internacional en el contexto de la propuesta, como OECD o World Economic Forum, entre otros. Además, no se fundamenta la diferencia, aporte e innovación de la presente propuesta con la que se presentó al Proceedings of the 3rd International Conference on Theory and Practice of Electronic Governance.Bogota Noviembre 2009.
Output: es
This paper is a well written paper. This paper can be divided into 2 parts: 1.Adversary training on ImageNet  2.Empirical study of label leak, single/multiple step attack, transferability and importance of model capacity  For part [1], I don’t think training without clean example will not make reasonable ImageNet level model. Ian’s experiment in “Explaining and Harnessing Adversarial Examples” didn't use BatchNorm, which may be important for training large scale model. This part looks like an extension to Ian’s work with Inception-V3 model. I suggest to add an experiment of training without clean samples.  For part [2], The experiments cover most variables in adversary training, yet lack technical depth.  The depth, model capacity experiments can be explained by regularizer effect of adv training;  Label leaking is novel; In transferability experiment with FGSM, if we do careful observe on some special MNIST FGSM example, we can find augmentation effect on numbers, which makes grey part on image to make the number look more like the other numbers. Although this effect is hard to be observed with complex data such as CIFAR-10 or ImageNet, they may be related to the authors' observation "FGSM examples are most transferable".    In this part the authors raise many interesting problems or guess, but lack theoretical explanations.   Overall I think these empirical observations are useful for future work.
Output: en
El artículo presenta una implementación del caso de una "red espacial" en el sistema administrador de bases de datos espacial PostGIS. El artículo se centra en el modelamiento de caso planteado, lo cual resulta en un modelo clásico de bases de datos. Lamentablemente, hay  pocas luces sobre la posterior implementación y el trabajo de "procesar consultas en redes espaciales en bases de datos espaciales" como indica el artículo. Hay poca o nula información sobre los resultados obtenidos. Claramente se trata de un trabajo en desarrollo, del cual aún faltan aspectos esenciales para su real valorización.  La figura 2. tiene problemas, aparece corrida en la versión pdf, vista desde Acrobat Reader.
Output: es
El trabajo expone de manera clara y con buenos argumentos un análisis de la colaboración entre distintos investigadores e instituciones. Es interesante el aporte realizado, primero como identificación de las oportunidades de mejora en el trabajo conjunto, y segundo, como análisis de la publicación local.  Sería interesante aplicar la misma metodología sobre otros congresos del área, ya sea Chilenos o latino-americanos y ver los resultados obtenidos.
Output: es
- Strengths:  - this article puts two fields together: text readability for humans and machine comprehension of texts  - Weaknesses:  - The goal of your paper is not entirely clear. I had to read the paper 4 times and I still do not understand what you are talking about! - The article is highly ambiguous what it talks about - machine comprehension or text readability for humans - you miss important work in the readability field - Section 2.2. has completely unrelated discussion of theoretical topics. - I have the feeling that this paper is trying to answer too many questions in the same time, by this making itself quite weak. Questions such as “does text readability have impact on RC datasets” should be analyzed separately from all these prerequisite skills.  - General Discussion:  - The title is a bit ambiguous, it would be good to clarify that you are referring to machine comprehension of text, and not human reading comprehension, because “reading comprehension” and “readability” usually mean that. - You say that your “dataset analysis suggested that the readability of RC datasets does not directly affect the question difficulty”, but this depends on the method/features used for answer detection, e.g. if you use POS/dependency parse features. - You need to proofread the English of your paper, there are some important omissions, like “the question is easy to solve simply look..” on page 1. - How do you annotate datasets with “metrics”?? - Here you are mixing machine reading comprehension of texts and human reading comprehension of texts, which, although somewhat similar, are also quite different, and also large areas. - “readability of text” is not “difficulty of reading contents”. Check this: DuBay, W.H. 2004. The Principles of Readability. Costa Mesa, CA: Impact information.  - it would be good if you put more pointers distinguishing your work from readability of questions for humans, because this article is highly ambiguous. E.g. on page 1 “These two examples show that the readability of the text does not necessarily correlate with the difficulty of the questions” you should add “for machine comprehension” - Section 3.1. - Again: are you referring to such skills for humans or for machines? If for machines, why are you citing papers for humans, and how sure are you they are referring to machines too? - How many questions the annotators had to annotate? Were the annotators clear they annotate the questions keeping in mind machines and not people?
Output: en
In this paper a well known soft mixture of experts model is adapted for, and applied to, a specific type of transfer learning problem in reinforcement learning (RL), namely transfer of action policies and value functions between similar tasks. Although not treated as such, the experimental setup is reminiscent of hierarchical RL works, an aspect which the paper does not consider at length, regrettably. One possible implication of this work is that architecture and even learning algorithm choices could simply be stated in terms of the objective of the target task, rather than being hand-engineered by the experimenter. This is clearly an interesting direction of future work which the paper illuminates.   Pros: The paper diligently explains how the network architecture fits in with various widely used reinforcement learning setups, which does facilitate continuation of this work. The experiments are good proofs of concept, but do not go beyond that i.m.h.o.  Even so, this work provides convincing clues that collections of deep networks, which were trained on not entirely different tasks, generalize better to related tasks when used together rather than through conventional transfer learning (e.g. fine-tuning).  Cons: As the paper well recounts in the related work section, libraries of fixed policies have long been formally proposed for reuse while learning similar tasks. Indeed, it is well understood in hierarchical RL literature that it can be beneficial to reuse libraries of fixed (Fernandez & Veloso 2006) or jointly learned policies which may not apply to the entire state space, e.g. options (Pricop et. al). What is not well understood is how to build such libraries, and this paper does not convincingly shed light in that direction, as far as I can tell. The transfer tasks have been picked to effectively illustrate the potential of the proposed architecture, but the paper does not tackle negative transfer or compositional reuse in well known challenging situations outlined in previous work (e.g. Parisotto et. al 2015, Rusu el. al 2015, 2016). Since the main contributions are of an empirical nature, I am curious how the results shown in figures 6 & 7 look plotted against wall-clock time, since relatively low data efficiency is not a limitation for achieving perfect play in Pong (see Mnih. et al, 2015). It would be more illuminating to consider tasks where final performance is plausibly limited by data availability. It would also be interesting if the presented results were achieved with reduced amounts of computation, or reduced representation sizes compared to learning from scratch, especially when one of the useful source tasks is an actual policy trained on the target task. Finally, it is perhaps underwhelming that it takes a quarter of the data required for learning Pong from scratch just to figure out that a perfect Pong policy is already in the expert library. Simply evaluating each expert for 10 episodes and using an  average-score-weighted majority vote to mix action choices would probably achieve the same final performance for a smaller fraction of the data.
Output: en
The paper is well structured. It follows a logical sequence of sections.  The paper does not fix to the Infonor conference template.  The english grammar is poor. It makes the paper difficult to read and follow. Either:  a) rewrite the paper in spanish, or b) make the paper reviewed by a professional traslator  Most of the references are not recent (2005-2010). No new work since 2003? Some references do not indicate year.  Problems with equations, fonts, etc.  Figures 5-17 are not referenced/discussed/explained. It may be better to present these figures as a table.  Both, result analysis and conclusions, declare that GA CHC is better than Pearson´s correlation Algorithm. However, this analysis and conclusion is not supported for a wide variety of situations and conditions. Therefore, a true validation work is missing.
Output: en
Quality, Clarity: There is no consensus on this, with the readers having varying backgrounds, and one reviewer commenting that they found it to be unreadable.     Originality, Significance:   The reviews are mixed on this, with the high score (7) acknowledging a lack of expertise on program induction.  The paper is based on the published TerpreT system, and some think that it marginal and contradictory with respect to the TerpreT paper. In the rebuttal, point (3) from the authors points to the need to better understand gradient-based program search, even if it is not always better. This leaves me torn about a decision on this paper, although currently it does not have strong support from the most knowledgeable reviewers.  That said, due to the originality of this work, the PCs are inclined to invite this work to be presented as a workshop contribution.
Output: en
In this paper, the authors propose a Bayesian variant of the skipgram model to learn word embeddings. There are two important variant compared to the original model. First, aligned sentences from multiple languages are used to train the model. Therefore, the context words of a given target word can be either from the same sentence, or from an aligned sentence in a different language. This allows to learn multilingual embedding. The second difference is that each word is represented by multiple vectors, one for each of its different senses. A latent variable z models which sense should be used, given the context.  Overall, I believe that the idea of using a probabilistic model to capture polysemy is an interesting idea. The model introduced in this paper is a nice generalization of the skipgram model in that direction. However, I found the paper a bit hard to follow. The formulation might probably be simplified (e.g. why not consider a target word w and a context c, where c is either in the source or target language. Since all factors are independent, this should not change the model much, and would make the presentation easier). The performance of all models reported in Table 2 & 3 seem pretty low.  Overall, I like the main idea of the paper, which is to represent word senses by latent variables in a probabilistic model. I feel that the method could be presented more clearly, which would make the paper much stronger. I also have some concerns regarding the experimental results.  Pros: Interesting extension of skipgram to capture polysemy. Cons: The paper is not clearly written. Results reported in the paper seems pretty low.
Output: en
El artículo presenta una descripción de la integración entre CMMI y métodos ágiles de desarrollo. Un énfasis que hacen los autores es que esta integración es recomendable para PYME. El desarrollo de software para las más pequeñas empresas es un desafío aun no resuelto. Como tal, los autores toman un tema que es interesante y justifican bien la idea del tema. Sin embargo, creo que no logran desarrollar su punto en la forma que se esperaría desde un punto de vista científico. Aunque los autores indican que hacen una revisión de la literatura, esta no es lograda adecuadamente, ya que no se observa una metodología de revisión sistemática propia de esta meta. Por otra parte, el artículo en mi opinión hace un resumen adecuado para personas que no pertenecen al área pero están familiarizados con algunos conceptos claves descritos en el paper. Vale mencionar que me parece un artículo bien escrito y planteado, en general, con una estructura fácil de entender. Además, otra crítica que haría es que si bien las ideas de CMMI y desarrollo ágil me parecen bien descritas, no pasa lo mismo respecto a justificar adecuadamente su aplicación concreta en PYMEs. Pienso que el artículo podría ser mejorado sustancialmente. Una sugerencia que haría para ello sería la presentación de un caso que dé fuerza al argumento central del artículo, es decir, CMMI y desarrollo ágil son compatibles y especialmente útiles para PYMEs. De esa forma, podría darse menor énfasis a la revisión (que me parece insuficiente en ese sentido) y se alzaría la relevancia práctica del argumento. Si así fuera, el artículo podría tener una interesante proyección. Además, deberían mejorarse la inclusión de referencias ya que existen una gran cantidad de argumentos que merecen ser aclarados si pertenecen a una opinión de los autores o son extraídos de la teoría existente. Existen muy pequeños errores ortográficos y gramáticos.
Output: es
This paper presents an approach to learn to generate programs. Instead of directly trying to generate the program, the authors propose to train a neural net to estimate a fix set of attributes, which then condition a search procedure. This is an interesting approach, which make sense, as building a generative model of programs is a very complex task.  Faster computation times are shown in the experimental section with respect to baselines including DFS, Enumeration, etc. in a setup with very small programs of length up to 5 instructions have to be found.  It is not clear to me how the proposed approach scales to larger programs, where perhaps many attributes will be on. Is there still an advantage?  The authors use as metric the time to find a single program, whose execution will result in the set of 5 input-output pairs given as input. However, as mentioned in the paper, one is not after a generic program but after the best program, or a rank list of all programs (or top-k programs) that result in a correct execution. Could the authors show experiments in this setting? would still be useful to have the proposed approach? what would the challenges be in this more realistic scenario?  In the second experiment the authors show results where the length of the program at training time is different than the length at test time. However, the results are shown when only 20% of the programs are finished. Could you show results for finding all programs?   The paper is missing an analysis of the results. What type of programs are difficult? how often is the NNet wrong? how does this affect speed? what are the failure modes of the proposed method?  The authors proposed to have a fix-length representation of the each input-output pair, and then use average pooling to get the final representation. However, why would average pooling make sense here? would it make more sense to combine the predictions at the decoder, not the encoder?  Learning from only 5 executions seems very difficult to me. For programs so small it might be ok, but going to more difficult and longer programs this setting does not seem reasonable.   In summary an interesting paper. This paper tackles a problem that is outside my area of expertise so I might have miss something important.
Output: en
SUMMARY  This paper discusses how data from a special type of low dimensional structure (monotonic chain) can be efficiently represented in terms of neural networks with two hidden layers.   PROS  Interesting, easy to follow view on some of the capabilities of neural networks, highlighting the dimensionality reduction aspect, and pointing at possible directions for further investigation.   CONS  The paper presents a construction illustrating certain structures that can be captured by a network, but it does not address the learning problem (although it presents experiments where such structures do emerge, more or less).   COMMENTS  It would be interesting to study the ramifications of the presented observations for the case of deep(er) networks.  Also, to study to what extent the proposed picture describes the totality of functions that are representable by the networks.   MINOR COMMENTS  - Figure 1 could be referenced first in the text.   - ``Color coded'' where the color codes what?  - Thank you for thinking about revising the points from my first questions. Note: Isometry on the manifold.  - On page 5, mention how the orthogonal projection on S_k is realized in the network.  - On page 6 ``divided into segments'' here `segments' is maybe not the best word.  - On page 6 ``The mean relative error is 0.98'' what is the baseline here, or what does this number mean?
Output: en
Este artículo presenta una interesante aplicación de algoritmos inmunes para  la programación de tareas.  Los resultados son de interés práctico.  Observaciones:  1.	Los autores hacen uso frecuente de términos en inglés, sin  la necesaria definición de los mismos o substitución por un término en español.  2.	Hay inúmeros errores de redacción que deben ser corregidos. A modo de ejemplo citamos la frase “Mientras la tecnología computacional avanza a pasos agigantados es importante en estos días optimizar la programación de una mejor manera con el fin de obtener mejores resultados, es por esto que se ha dado el uso de algoritmos inspirados en sistemas biológicos, dado que presentan una mayor optimización que el uso de heurísticas convencionales”;  3.	Un gran número de referencias está incompleta. Por ejemplo: 2, 3, 6 ......  Estas correcciones son necesarias  para la aceptación del trabajo
Output: es
This paper considers the problem of model-based policy search. The authors  consider the use of Bayesian Neural Networks to learn a model of the environment and advocate for the $\alpha$-divergence minimization rather than the more usual  variational Bayes.   The ability of alpha-divergence to capture bi-modality however  comes at a price and most of the paper is devoted to finding tractable approximations.  The authors therefore use the approach of Hernandez-Lobato et al. (2016) as proxy to the alpha-divergence .   The environment/system dynamics is clearly defined as a well as the policy parametrization  (section 3) and would constitute a useful reference point for other researchers.  Simulated roll-outs, using the learned model, then provide samples of the expected  return. Since a model of the environment is available, stochastic gradient descent  can be performed in the usual way, without policy gradient estimators, via automatic  differentiation tools.   The experiments demonstrate that alpha-divergence is capable of capturing multi-model  structure which competing methods (variational Bayes and GP) would otherwise struggle with. The proposed approach also compares favorably in a real-world batch setting.  The paper is well-written, technically rich and combines many recent tools  into a coherent algorithm. However, the repeated use of approximations to original  quantities seems to somehow defeat the benefits of the original problem formulation.  The scalability and computational effectiveness of this approach is also questionable  and I am uncertain if many problem would warrant such complexity in their solution.  As with other Bayesian methods, the proposed approach would probably shine in low-samples  regime and in this case might be preferable to other methods in the same class (VB, GP).
Output: en
El artículo, según los revisores, no es una contribución científica, pero el tema resulta particularmente interesante de discutir en nuestro congreso. Es por ello que se recomienda aceptar el artículo para su presentación en el Workshop de aplicaciones empresariales en lugar de paper regular.
Output: es
Se aprecia un trabajo extenso que se valora, sin embargo se observan varias inconsistencias a lo largo del trabajo.  Se observa en la página 4 figura 1 la indicación de un modelo conceptual siendo este del tipo copo de nieve, sin embargo lo correcto sería indicar que es un esquema de diseño lógico y no de diseño conceptual de DW. Además un esquema conceptual correcto no debe indicar el tipo de dato. También en este aspecto se hace referencia a una bibliografía de esquema estrella. Faltó investigación sobre modelos conceptuales para DW para evitar lo anterior.  Mucha redundancia en problemas técnicos que no deberían ser de importancia para el artículo.  Se habla de inteligencia de negocios pero en un sentido estricto si se aplica inteligencia de negocios se necesita un  analista “experto” que sea capaz de tomar decisiones con las tendencias encontradas en el estudio, lo cual no se presenta en el artículo.  Faltan referencias de trabajos relacionados, pues solo se referencian  libros. Una revisión a trabajos relacionados habría sido muy útil en el sentido de estructurar mejor el artículo.  Existen metodologías para esquematizar el proceso ETL que habrían permitido mejorar este aspecto descrito en el artículo.
Output: es
This one is a tough call, because I do think that there are some important, salvageable technial results in here (notably the parsing algorithm), but the paper as a whole has very little cohesion.        It is united around an overarching view of formal languages in which a language being "probabilistic" or not is treated as a formal property of the same  variety as being closed under intersection or not.  In my opinion, what it  means for a formal language to be probabilistic in this view has not been  considered with sufficient rigor for this viewpoint to be compelling.  I should note, by the way, that the value of the formal results provided mostly does not depend on the flimsiness of the overarching story.  So what we have here is not bad research, but a badly written paper.  This needs  more work.  I find it particulary puzzling that the organization of the paper leaves so little space for elucidating the parsing result that soundness and completeness are relegated to a continuation of the paper in the form of supplementary notes.  I also find the mention of probabilistic languages in the title of the paper to be very disingenuous --- there is in fact no probabilistic reasoning in this submission.  The sigificance of the intersection-closure result of section 3 is also being somewhat overstated, I think.  Unless there is something I'm not understanding about the restrictions on the right-hand sides of rules (in which case, please elaborate), this is merely a matter of folding a finite intersection into the set of non-terminal labels.
Output: en
El  trabajo está bien presentado, es consistente y trata de un problema  relevante. Los resultados presentados  aún son incipientes,  debido  que el estudio de caso es simple y no incorpora complejidades mayores. Aún así, considero que este trabajo presenta elementos suficientes  para su aceptación.
Output: es
This paper proposes a new Bayesian neural network architecture for predicting the values of learning curves during the training of machine learning models. This is an exploratory paper, in that the ultimate goal is to use this method in a Bayesian optimization system, but for now the experiments are limited to assessing the quality of the predictions. This builds on previous work in Domhan, 2015, however in this work the model incorporates information from all tested hyperparameter settings rather than just extrapolating from a single learning curve. This paper also explores two MCMC methods for inference: SGLD and SGHMC, but I couldn’t tell if either of these were tested in Domhan, 2015 as well.  The performance seems overall positive, particularly in the initial phase of each curve where there is very little information. In this case, as expected, sharing knowledge across curves helps. One regime which did not seem to be tested, but might be very informative, is when some curves in the training set have been mostly, or fully observed. This might be a case where sharing information really helps.  Something that concerns me about this approach is the timing. The authors stated that to train the network takes about 20-60 seconds. In the worst case, with 100 epochs, this results in a little over 1.5 hours spent training the Bayesian network. This is a non-trivial fraction of the several hours it takes to train the model being tuned.  The Bayesian network makes many separate predictions, as shown in Figure 2. It would be interesting to see how accurate some of these individual pieces are. For example, did you bound the asymptotic value of the learning curve, since you mostly predicted accuracy? If not, did the value tend to lie in [0,1]?  Below are some minor questions/comments.  Figure 1 axes should read “validation accuracy” Figure 6 can you describe LastSeenValue (although it seems self-explanatory, it’s good to be explicit) in the bottom left figure, and why isn’t it used anywhere else as a baseline? Figure 7 and Table 1 are you predicting just the final value of the curves? Or every value along each curve, conditioned on the previous values? Why do you only use 5 basis functions? Does this sufficiently capture all of the flexibility of these learning curves? Would more basis functions help or hurt?
Output: en
This paper proposes a new kind of generative model based on an annealing process, where the transition probabilities are learned directly to maximize a variational lower bound on the log-likelihood. Overall, the idea is clever and appealing, but I think the paper needs more quantitative validation and better discussion of the relationship with prior work.  In terms of prior work, AIS and RAISE are both closely related algorithms, and share much of the mathematical structure with the proposed method. For this reason, it’s not sufficient to mention them in passing in the related work section; those methods and their relationship to variational walkback need to be discussed in detail. If I understand correctly, the proposed method is essentially an extension of RAISE where the transition probabilities are learned rather than fixed based on an existing MRF. I think this is an interesting and worthwhile extension, but the relationship to existing work needs to be clarified.  The analysis of Appendix D seems incorrect. It derives a formula for the ratios of prior and posterior probabilities, but this formula only holds under the assumption of constant temperature (in which case the ratio is very large). When the temperature is varied, the analysis of Neal (2001) applies, and the answer is different.   One of the main selling points of the method is that it optimizes a variational lower bound on the log-likelihood; even more accurate estimates can be obtained using importance sampling. It ought to be easy to report log-likelihood estimates for this method, so I wonder why such estimates aren’t reported. There are lots of prior results to compare against on MNIST. (In addition, a natural baseline would be RAISE, so that one can check if the ability to learn the transitions actually helps.)  I think the basic idea here is a sound one, so I would be willing to raise my score if the above issues are addressed in a revised version.   Minor comments:  “A recognized obstacle to training undirected graphical models… is that ML training requires sampling from MCMC chains in the inner loop of training, for each example.” This seems like an unfair characterization, since the standard algorithm is PCD, which usually takes only a single step per mini-batch.  Some of the methods discussed in the related work are missing citations.  The method is justified in terms of “carving the energy function in the right direction at each point”, but I’m not sure this is actually what’s happening. Isn’t the point of the method that it can optimize a lower bound on the log-likelihood, and therefore learn a globally correct allocation of probability mass?
Output: en
El artículo presenta un sistema de control de flota que integra diversas tecnologías.  Se presenta una solución compleja que integra distintos sistemas de hardware y software.  La presentación describe el sistema a nivel de módulos principales (gestión y control de trenes, SMS, autorizaciones de vía, detectores de desrielo, y cambios radiales) pero no profundiza en ninguno.  Se presenta la solución como una alternativa de bajo costo comparable a referentes mundiales, sin embargo no se aportan datos al respecto. Así mismo, no se proporciona respaldo para las afirmaciones hechas respecto de escalablidad y otros beneficios de la solución (ver Conclusiones y las dos secciones precedentes).
Output: es
This paper proposes an idea of looking n-steps backward when modelling sequences with RNNs. The proposed RNN does not only use the previous hidden state (t-1) but also looks further back ( (t - k) steps, where k=1,2,3,4 ). The paper also proposes a few different ways to aggregate multiple hidden states from the past.   The reviewer can see few issues with this paper.  Firstly, the writing of this paper requires improvement. The introduction and abstract are wasting too much space just to explain unrelated facts or to describe already well-known things in the literature. Some of the statements written in the paper are misleading. For instance, it explains, “Among various neural network models, recurrent neural networks (RNNs) are appealing for modeling sequential data because they can capture long term dependency in sequential data using a simple mechanism of recurrent feedback” and then it says RNNs cannot actually capture long-term dependencies that well. RNNs are appealing in the first place because they can handle variable length sequences and can model temporal relationships between each symbol in a sequence. The criticism against LSTMs is hard to accept when it says: LSTMs are slow and because of the slowness, they are hard to scale at larger tasks. But we all know that some companies are already using gigantic seq2seq models for their production (LSTMs are used as building blocks in their systems). This indicates that the LSTMs can be practically used in a very large-scale setting.   Secondly, the idea proposed in the paper is incremental and not new to the field. There are other previous works that propose to use direct connections to the previous hidden states [1]. However, the previous works do not use aggregation of multiple number of previous hidden states. Most importantly, the paper fails to deliver a proper analysis on whether its main contribution is actually helpful to improve the problem posed in the paper. The new architecture is said that it handles the long-term dependencies better, however, there is no rigorous proof or intuitive design in the architecture that help us to understand why it should work better. By the design of the architecture, and speaking in very high-level, it seems like the model maybe helpful to mitigate the vanishing gradients issue by a linear factor. It is always a good practice to have at least one page to analyze the empirical findings in the paper.   Thirdly, the baseline models used in this paper are very weak. Their are plenty of other models that are trained and tested on word-level language modelling task using Penn Treebank corpus, but the paper only contains a few of outdated models. I cannot fully agree on the statement “To the best of our knowledge, this is the best performance on PTB under the same training condition”, these days, RNN-based methods usually score below 80 in terms of the test perplexity, which are far lower than 100 achieved in this paper.   [1] Zhang et al., “Architectural Complexity Measures of Recurrent Neural Networks”, NIPS’16
Output: en
This paper introduces a hierarchical clustering method using learned CNN features to build 'the tree of life'. The assumption is that the feature similarity indicates the distance in the tree. The authors tried three different ways to construct the tree: 1) approximation central point 2) minimum spanning tree and 3) multidimensional scaling based method. Out of them, MDS works the best. It is a nice application of using deep features. However, I lean toward rejecting the paper because the following reasons:  1) All experiments are conducted in very small scale. The experiments include 6 fish species, 11 canine species, 8 vehicle classes. There are no quantitative results, only by visualizing the generated tree versus the wordNet tree. Moreover, the assumption of using wordNet is not quite valid. WordNet is not designed for biology purpose and it might not reflect the true evolutionary relationship between species.  2) Limited technical novelty. Most parts of the pipeline are standard, e.g. use pretrained model for feature extraction, use previous methods to construct hierarchical clustering. I think the technical contribution of this paper is very limited.
Output: en
Strengths  - interesting to explore the connection between ReLU DNN and simplified SFNN - small task (MNIST)  is used to demonstrate the usefulness of the proposed training methods experimentally - the proposed, multi-stage training methods are simple to implement (despite lacking theoretical rigor)   Weaknesses  -no results are reported on real tasks with large training set  -not clear exploration on the scalability of the learning methods when training data becomes larger  -when the hidden layers become stochastic, the model shares uncertainty representation with deep Bayes networks or deep generative models (Deep Discriminative and Generative Models for Pattern Recognition , book chapter in “Pattern Recognition and Computer Vision”, November 2015, Download PDF). Such connections should be discussed, especially wrt the use of uncertainty representation to benefit pattern recognition (i.e. supervised learning via Bayes rule) and to benefit the use of domain knowledge such as “explaining away”.  -would like to see connections with variational autoencoder models and training, which is also stochastic with hidden layers
Output: en
El trabajo consiste en un artículo tipo survey o estudio de la temática sobre redes y vehículos sub-acuáticos, revisión bibliográfica y de tecnología relacionadas muy interesante y completa a mi juicio. Sugiero que pudiera entregar una contribución tomando como base de este estudio, por ejemplo proporcionando una taxonomía de las características de las tecnologías requeridas para este tipo de par problemas-soluciones, y agregando una propuesta mediante una metodología de implementación y prueba. Lo anterior lo señala como entrega en trabajo futuro, quizás al menos debió entregar una tabla comparativa de las tecnologías asociadas y descritas.
Output: es
El trabajo muestra el trabajo desarrollado para la construcción de una aplicación Web orientada a una interfaz móvil (tablet). La descripción del trabajo desarrollado no es detallada y deja muchas dudas sobre su implementación.   Problemas de fondo encontrados:  - Se describe la metodología RUP y BPM, sin embargo, no se detalle la razón de ello y el aporte asociado al trabajo más allá de las 2 figuras mostradas. - Se menciona la utilización de GPS para registro/tracking de mascotas, pero no se entregan detalles técnicos de la solución propuesta. Esto hace dudar de la propuesta realizada. - No se realiza el análisis de la calidad de interface humano-computador, por lo menos debería incluir una encuesta de usabilidad. - Se menciona la utilización del lenguaje PHP y MySQL como base de datos, sin embargo, no se dan detalles de la implementación utilizada (Patrones, SQL, ORM, etc). - No muestra evidencias de la implementación de la aplicación: diagrama de clases, diagrama de base de datos, contratos, patrones utilizados (DAO, Active Record, etc.).   Problemas de forma identificados en el trabajo:  - Las capturas de pantalla no son claros y pierden mucho espacio al utilizar como marco la interface de un tablet. - La figura 7 se encuentra sobrepuesta sobre texto, el cual es ilegible. - La figura 11 hace referencia a la aplicación Google Maps y no se ve clara la asociación con la aplicación.
Output: es