TASK DEFINITION: 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].
PROBLEM: This paper proposes a design principle for computation blocks in convolutional networks based on repeated application of expand and join operations resulting in a fractal-like structure.   This paper is primarily about experimental evaluation, since the objective is to show that a residual formulation is not necessary to obtain good performance, at least on some tasks.  However, in my opinion the evaluations in the paper are not convincing. The primary issue is lack of a proper baseline, against which the improvements can be clearly demonstrated by making isolated changes. I understand that for this paper such a baseline is hard to construct, since it is about a novel architecture principle. This is why more effort should be put into this, so that core insights from this paper can be useful even after better performing architectures are discovered. The number of parameters and amount of computation should be used to indicate how fair the comparisons are between architectures. Some detailed comments:  - In Table 1 comparisons to Resnets, the resnets from He et al. 2016b and Wide Resnets should be compared to FractalNet (in lieu of a proper baseline). The first outperforms FractalNet on CIFAR-100 while the second outperforms it on both. The authors compare to other results without augmentation, but did not perform additional experiments without augmentation for these architectures.  - The 40 layer Fractal Net should not be compared to other models unless the parameter reduction tricks are utilized for the other models as well.  - A proper comparison to Inception networks should also be performed for these networks. My guess is that the reason behind a seemingly 'ad-hoc' design of Inception modules is to reduce the computational footprint of the model (which is not a central motivation of fractal nets). Since this model is directly related to the Inception module due to use of shorter and longer paths without shortcuts, one can easily simplify the Inception design to build a strong baseline e.g. by converting the concatenation operation to a mean operation among equally sized convolution outputs. As an aside, note that Inception networks have already shown that residual networks are not necessary to obtain the best performance [1].  - It should be noted that Residual/Highway architectures do have a type of anytime property, as shown by lesioning experiments in Srivastava et al and Viet et al.  - The architecture specific drop-path regularization is interesting, but is used along with other regularizers such as dropout, batch norm and weight decay and its benefit on its own is not clear.  Overall, it's not clear to me that the experiments clearly demonstrate the utility of the proposed architecture.   [1] Szegedy, Christian, Sergey Ioffe, and Vincent Vanhoucke. "Inception-v4, inception-resnet and the impact of residual connections on learning." arXiv preprint arXiv:1602.07261 (2016).

SOLUTION: en

PROBLEM: This paper describes a new deterministic dependency parsing algorithm and analyses its behaviour across a range of languages. The core of the algorithm is a set of rules defining permitted dependencies based on POS tags. The algorithm starts by ranking words using a slightly biased PageRank over a graph with edges defined by the permitted dependencies. Stepping through the ranking, each word is linked to the closest word that will maintain a tree and is permitted by the head rules and a directionality constraint.  Overall, the paper is interesting and clearly presented, though seems to differ only slightly from Sogaard (2012), "Unsupervised Dependency Parsing without Training". I have a few questions and suggestions:  Head Rules (Table 1) - It would be good to have some analysis of these rules in relation to the corpus. For example, in section 3.1 the fact that they do not always lead to a connected graph is mentioned, but not how frequently it occurs, or how large the components typically are.  I was surprised that head direction was chosen using the test data rather than training or development data. Given how fast the decision converges (10-15 sentences), this is not a major issue, but a surprising choice.  How does tie-breaking for words with the same PageRank score work? Does it impact performance significantly, or are ties rare enough that it doesn't have an impact?  The various types of constraints (head rules, directionality, distance) will lead to upper bounds on possible performance of the system. It would be informative to include oracle results for each constraint, to show how much they hurt the maximum possible score. That would be particularly helpful for guiding future work in terms of where to try to modify this system.  Minor:  - 4.1, "we obtain [the] rank"  - Table 5 and Table 7 have columns in different orders. I found the Table 7 arrangement clearer.  - 6.1, "isolate the [contribution] of both"

SOLUTION: en

PROBLEM: This paper takes a model based on that of Graves and retrofits it with a representation derived from the work of Plamondon.  part of the goal of deep learning has been to avoid the use of hand-crafted features and have the network learn from raw feature representations, so this paper is somewhat against the grain.   The paper relies on some qualitative examples as demonstration of the system, and doesn't seem to provide a strong motivation for there being any progress here.  The paper does not provide true text-conditional handwriting synthesis as shown in Graves' original work.   Be more consistent about your bibliography (e.g. variants of Plamondon's own name, use of "et al." in the bibliography etc.)

SOLUTION:
en