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].

[EX Q]: The paper presents a method to learn graph embeddings in a unsupervised way using random walks. It is well written and the execution appears quite accurate. The area of learning whole graph representations does not seem to be very well explored in general, and the proposed approach enjoys having very few competitors.  In a nutshell, the idea is to linearize the graph using random walks and to compute the embedding of the central segment of each walk using the skip-thought criterion. Being not an expert in biology, I can not comment whether or not this makes sense, but the gains reported in Table 2 are quite significant.   An anonymous public comment compared this work to a number of others in which the problem of learning representations of nodes is considered. While this is arguably a different goal, one natural baseline would be to pool these representations using mean- or max- pooling. It would very interesting to do such a comparison, especially given that the considered approach heavily relies on pooling (see Figure 3(c))  To sum up, I think it is a nice paper, and with more baselines I would be ready to further increase the numerical score.
[EX A]: en

[EX Q]: This paper shows:    1. Easy, constructive proofs to derive e-error upper-bounds on neural networks with O(log 1/e) layers and O(log 1/e) ReLU units.   2. Extensions of the previous results to more general function classes, such as smooth or vector-valued functions.   3. Lower bounds on the neural network size, as a function of its number of layers. The lower bound reveals the need of exponentially many more units to approximate functions using shallow architectures.  The paper is well written and easy to follow. The technical content, including the proofs in the Appendix, look correct. Although the proof techniques are simple (and are sometimes modifications of arguments by Gil, Telgarsky, or Dasgupta), they are brought together in a coherent manner to produce sharp results. Therefore, I am leaning toward acceptance.
[EX A]: en

[EX Q]: The paper presents an approach for tackling the instability problem that is present in generative adversarial networks. The general idea is to allow the generator to "peek ahead" at how the discriminator will evolve its decision boundary over-time with the premise that this information should prevent the generator from collapsing to produce only samples from a single mode of the data distribution.  This is a very well written paper that clearly motivates its attack on an important open issue. The experiments are well carried out and strongly support the presented idea. The pursued approach is substantially more elegant than current existing "hacks" that are commonly used to make GANs work in practice. I however have three main issues that let me partly doubt the success of the method. If these can be resolved this paper is a clear candidate for acceptance.  1) I am not entirely convinced that the same effect cannot be obtained by the following procedure: simply train the discriminator for an extended number of K steps when updating the generator (say a number equivalent to the unrolling steps used in the current experiments) then, after the generator was updated undo the K updates to the discriminator and do 1 new update step instead. I only briefly glanced at your response to Reviewer2 which seems to imply you now tried something similar to this setup by stopping gradient flow at an appropriate point (although I think this is not exactly equivalent). 2) I tried to reproduce the simple MNIST example but using a fully connected network instead of an RNN generator without much success. Even when unrolling the discriminator for 30-40 steps the generator still engages in mode seeking behavior or does not train at all. This could either be because of a bug in my implementation or because of some peculiarities of the RNN generator or because I did not use batch normalization anywhere. If it is one of the latter two this would entail a dependence of the proposed approach on specific forms of the discriminator and generator and should be discussed. My code can be found here
[EX A]:
en

