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].
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Question: Los autores hacen un análisis comparativo   entre diversas tecnologias para automación industrial. El análisis me pareció razonablemente profundo y amplio, pero lo califique como "weak accept" por no tener contribuciones científicas fuertes.  El texto necesita ser revisado, hay frases largas (por ejemplo la segunda del segundo parágrafo de la introducción tiene 11 líneas) y puntuaciones faltando o colocadas incorrectamente. La primera frase del segundo parágrafo de la introducción.  Incluir también el significado de las siglas PLC y PC.

Answer: es


Question: Overall, this is a nice paper. Developing a unifying framework for these newer neural models is a worthwhile endeavor.  However, it's unclear if the DRAGNN framework (in its current form) is a significant standalone contribution. The main idea is straightforward: use a transition system to unroll a computation graph. When you implement models in this way you can reuse code because modules can be mixed and matched. This is nice, but (in my opinion) is just good software engineering, not machine  learning research.  Moreover, there appears to be little incentive to use DRAGNN, as there are no 'free things' (benefits) that you get by using the framework. For example:  - If you write your neuralnet in an automatic differentiation library (e.g.,   tensorflow or dynet) you get gradients for 'free'.  - In the VW framework, there are efficiency tricks that 'the credit assignment   compiler' provides for you, which would be tedious to implement on your   own. There is also a variety of algorithms for training the model in a   principled way (i.e., without exposure bias).  I don't feel that my question about the limitations of the framework has been satisfactorily addressed. Let me ask it in a different way: Can you give me examples of a few models that I can't (nicely) express in the DRAGNN framework? What if I wanted to implement

Answer: en


Question: This paper presents an intriguing study of how one can pose architecture search as a meta learning problem. By collecting features from networks trained on various datasets and training a “ranking classifier” (the actual details of the classifier do not seem to be described in detail) one can potentially infer what a good architecture for a new problem could be by simply running the ranker on the extracted features for a new problem setup.  One notable comment from the paper is that the authors fix some important hyper-parameters for all the networks. I am of the opinion that optimizing the learning rate (and its decay schedule) is actually quite important. I hypothesize that a lot of the conclusions of this paper may change quite a bit if the authors did an actual search over the rates instead. I suspect that instead of training 11k nets, one can train 2k nets with 5 learning rates each and get a much better result that is actually compelling.  I am not convinced that the protocol for generating the various architectures is doing a good job at creating a diversity of architecture (simply because of the max depth of 8 layers and 14 components overall). I suspect that most of these generated architectures are actually almost identical performance-wise and that it’s a waste to train so many of them on so many tasks. Unless the authors are already doing this, they should define a pruning mechanism that filters out nets that are too similar to already existing ones.  The batch normalization experiments in Table 2 seem odd and under-explained. It is also well-known that the optimal learning rates when using batch norm vs. not using batch norm can differ by an order of magnitude so given the fixed learning rate throughout all experiments, I take these results with some grain of salt.  I am not sure we got many insights into the kinds of architectures that ended up being at the top. Either visualizations, or trends (or both), would be great.  This work seems to conflate the study of parallel vs. serial architectures with the study of meta learning, which are somewhat distinct issues. I take issue with the table that compares parallel vs. serial performance (table 2) simply because the right way would be to filter the architectures by the same number of parameters / capacity.  Ultimately the conclusion seems to be that when applying deep nets in a new domain, it is difficult to come up with a good architecture in advance. In that sense, it is hard to see the paper as a constructive result, because it’s conclusions are that while the ranker may do a good job often-times, it’s not that reliable. Thus I am not convinced that this particular result will be of practical use to folks who are intending to use deep nets for a new domain.

Answer:
en