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].

Q: Contributions The paper presents an adaptation of batch normalization for RNNs in the case of LSTMs, along the horizontal depth. Contrary to previous work from (Laurent 2015; Amodei 2016), the work demonstrates that batch-normalizing the hidden states of RNNs can improve optimization, and argues with quantitative experiments that the key factor to making this work is proper initialization of parameters, in particular gamma. Experiments show some gain in performance over vanilla LSTMs on Sequential MNIST, PTB, Text8 and CNN Question-Answering.  Novelty+Significance Batch normalization has been key for training deeper and deeper networks (e.g. ResNets) and it seems natural that we would want to extend it to RNNs.  The paper shows that it is possible to do so with proper initialization of parameters, contrary to previous work from (Laurent 2015; Amodei 2016). Novelty comes from where to batch norm (i.e. not in the cell update) and in the per-time step statistics.   Adding batch normalization to LSTMs incurs additional computational cost and bookkeeping; for training speed comparisons (e.g. Figure 2) the paper only compares LSTM and BN-LSTM by iteration count; given the additional complexity of the BN-LSTM I would have also liked to see a wall-clock comparison.  As RNNs are used across many tasks, this work is of interest to many.  However, the results gains are generally minor and require several tricks to work in practice. Also, this work doesn’t address a question about batch normalization that it seems natural that it helps with faster training, but why would it also improve generalization?   Clarity The paper is overall very clear and well-motivated. The model is well described and easy to understand, and the plots illustrate the points clearly.  Summary Interesting though relatively incremental adaptation, but shows batch normalization to work for RNNs where previous works have not succeeded. Comprehensive set of experiments though it is questionable if the empirical gains are significant enough to justify the increased model complexity as well as computational overhead.  Pros - Shows batch normalization to work for RNNs where previous works have not succeeded - Good empirical analysis of hyper-parameter choices and of the activations - Experiments on multiple tasks - Clarity  Cons - Relatively incremental - Several ‘hacks’ for the method (per-time step statistics, adding noise for exploding variance, sequence-wise normalization) - No mention of computational overhead - Only character or pixel-level tasks, what about word-level?

A: en
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Q: This paper is a well written paper. This paper can be divided into 2 parts: 1.Adversary training on ImageNet  2.Empirical study of label leak, single/multiple step attack, transferability and importance of model capacity  For part [1], I don’t think training without clean example will not make reasonable ImageNet level model. Ian’s experiment in “Explaining and Harnessing Adversarial Examples” didn't use BatchNorm, which may be important for training large scale model. This part looks like an extension to Ian’s work with Inception-V3 model. I suggest to add an experiment of training without clean samples.  For part [2], The experiments cover most variables in adversary training, yet lack technical depth.  The depth, model capacity experiments can be explained by regularizer effect of adv training;  Label leaking is novel; In transferability experiment with FGSM, if we do careful observe on some special MNIST FGSM example, we can find augmentation effect on numbers, which makes grey part on image to make the number look more like the other numbers. Although this effect is hard to be observed with complex data such as CIFAR-10 or ImageNet, they may be related to the authors' observation "FGSM examples are most transferable".    In this part the authors raise many interesting problems or guess, but lack theoretical explanations.   Overall I think these empirical observations are useful for future work.

A: en
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Q: Este es un trabajo interesante, donde el autor quiere mostrar a las Honeynets como medida de seguridad alternativa, para aprendizaje del  modo de operar de intrusos y así crear nuevas formas de protección.  Observaciones:  - El nombre del único autor debe estar centralizado en el artículo. - Palabras y figuras en inglés. El artículo está en español. - Las referencias están inadecuadas: hay  un único artículo citado y con referencia incompleta. Mejorar la revisión bibliográfica, citando artículos y libros  actuales.

A:
es
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