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].

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
en

The authors describe a dataset of proof steps in higher order logic derived from a set of proven theorems. The success of methods like AlphaGo suggests that for hard combinatorial style problems, having a curated set of expert data (in this case the sequence of subproofs) is a good launching point for possibly super-human performance. Super-human ATPs are clearly extremely valuable. Although relatively smaller than the original Go datasets, this dataset seems to be a great first step. Unfortunately, the ATP and HOL aspect of this work is not my area of expertise. I can't comment on the quality of this aspect.  It would be great to see future work scale up the baselines and integrate the networks into state of the art ATPs. The capacity of deep learning methods to scale and take advantage of larger datasets means there's a possibility of an iterative approach to improving ATPs: as the ATPs get stronger they may generate more data in the form of new theorems. This may be a long way off, but the possibility is exciting.
en

Aunque el paper está bien escrito y estructurado, y la experimentación parece extensa, no se percibe un grado de originalidad en la versión del algoritmo presentado en el paper respecto de las anteriores versiones del mismo existentes en la literatura y que han sido ya aplicadas a la misma problemática.

es