instruction:
In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
question:
The present submission discusses a "causal regularizer", which promotes the use of causal dependencies (X -> Y, where X is a feature of the learning problem, and Y is the target variable) in predictive models. Similarly, such causal regularizer penalizes the use of non-causal dependencies, which can arise due to reverse causation (Y -> X) or confounding (X <- Z -> Y, where Z is a hidden confounder).  + Overall, this submission tackles one of the most important problems in machine learning, which is to build causal models. The paper discusses and addresses this issue effectively when applied to a dataset in heart disease. In their experiments, the authors correctly identify some of the common causes of heart disease by virtue of their causal regularizer.  - The authors do not discuss the robustness of their approach with respect to choice of hyper-parameters (both describing the neural network architecture and the generative model that synthesizes artificial causal data). This seems like a crucial issue, in particular when dealing with medical data.  - The conclusions of the experimental evaluation should be discussed in greater length. On the one hand, Figure 4.a shows that there are no differences between L1 and causal regularization in terms of predictive performance, but it is difficult to conclude if this result is statistically significant without access to error-bars. On the other hand, Table 3 describes the qualitative differences between L1 and causal regularization. However, this table is hard to read: How were the 30 rows selected? What does the red highlighting mean? Are these red rows some true causal features that were missed? If so, this is related to precision. What about recall? Did the causal regularization pick up many non-causal features as causal?  - Regarding causal classifiers, this paper should do a much better job at reviewing previous work. For instance, the paper "Towards a Learning Theory of Cause-Effect Inference" from Lopez-Paz et al. is missing from the references. However, this prior work studies many of the aspects that are hinted as novel in this submission. In particular, the prior work of Lopez-Paz 1) introduces the concept of Mother distribution (referred as Nature hyper-prior in this submission) which explicitly factorizes the distribution over causes and mechanisms, 2) circumvented intractable likelihoods by synthesizing and training on causal data, 3) tackled the confounding case (compare Figure 1 of this submission and Appendix C of Lopez-Paz), and 4) dealt with discrete data seamlessly (such as the ChaLearn data from Section 5.3 in Lopez-Paz).  On a positive note, this is a well-written paper that addresses the important, under-appreciated problem of incorporating causal reasoning into machine learning. On a negative note, the novelty of the technical contributions is modest and the qualitative evaluation of the results could be greatly extended. In short, I am leaning slightly towards acceptance.
answer:
en


question:
Combining storage and processing capabilities is an interesting research topic because data transfer is a major issue for many machine learning tasks. The paper itself is well-written, but unfortunately addresses a lot of things only to medium depth (probably due length constraints). My opinion is that a journal with an in-depth discussion of the technical details would be a better target for this paper.  Even though the researchers took an interesting approach to evaluate the performance of the system, it's difficult for me to grasp the expected practical improvements of this approach. With such a big focus on GPU (and more specialized hardware such as TPUs), the one question that comes to mind: By how much does this - or do you expect it to - beat the latest and greatest GPU on a real task?  I don't consider myself an expert on this topic even though I have some experience with SystemC.
answer:
en


question:
Se trata de un artículo bastante particular que propone una extensión a los modelos de datos existentes considerando la idea de "sistema" (de objetos). Para esto se propone un lenguaje (LATIN) y un modelo de datos ¿universal?, MED, basado en un "volumen esférico" con dos polos "mayor" y "diario" basados en ideas contables. Este es, a mi juicio, el aspecto más crítico de la propuesta, ya que surge la duda, de si todo sistema debería calzar en este esquema, o estamos hablando de un conjunto particular de casos. Las ideas posteriores son interesante y, al menos, generan un quiebre con respecto a las ideas clásicas de la orientación a objetos. Cito la idea de sistema propuesta: "un sistema es un conjunto de clases enlazadas e inter-actuantes según un mismo método, el cual no es particular a cada clase sino emergente de todas ellas". Otras ideas asocian transacción con conjunto referencial, macros, tipos, instancias, ... lo que en términos globales constituyen un nuevo cuerpo de ideas. No habiendo en las referencias alguna cita a algún artículo anterior del autor que trate estas ideas, me parece que puede llegar a ser interesante escuchar esta propuesta y debatir al respecto. En términos de formalidad, el artículo no cumple cabalmente con el formato del congreso, y el número de páginas excede las ocho solicitadas.
answer:

es