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].

Input: Consider Input: Taking into account the loss in the binarization step through a proximal Newton algorithm is a nice idea. This is at least one approach to bringing in the missing loss in the binarization step, which has recently gone from a two step process of train and binarize to a single step simultaneous train/compress. Performance on a few small tasks show the benefit. It would be nice to see some results on substantial networks and tasks which really need compression on embedded systems (a point made in the introduction). Is it necessary to discuss exploding/vanishing gradients when the RNN experiments are carried out by an LSTM, and handled by the cell error carousel? We see the desire to tie into proposition 2, but not clear that the degradation we see in the binary connect is related. Adam is used in the LSTM optimization, was gradient clipping really needed, or is the degradation of binary connect simply related to capacity? For proposition 3.1, theorem 3.1 and proposition 3.2 put the pointers to proofs in appendix.

Output: en


Input: Consider Input: 1) Summary  This paper investigates the usefulness of decoupling appearance and motion information for the problem of future frame prediction in natural videos. The method introduces a novel two-stream encoder-decoder architecture, MCNet, consisting of two separate encoders -- a convnet on single frames and a convnet+LSTM on sequences of temporal differences -- followed by combination layers (stacking + convolutions) and a deconvolutional network decoder leveraging also residual connections from the two encoders. The architecture is trained end-to-end using the objective and adversarial training strategy of Mathieu et al.  2) Contributions  + The architecture seems novel and is well motivated. It is also somewhat related to the two-stream networks of Simonyan & Zisserman, which are very effective for real-world action recognition. + The qualitative results are numerous, insightful, and very convincing (including quantitatively) on KTH & Weizmann, showing the benefits of decoupling content and motion for simple scenes with periodic motions, as well as the need for residual connections.  3) Suggestions for improvement  Static dataset bias: In response to the pre-review concerns about the observed static nature of the qualitative results, the authors added a simple baseline consisting in copying the pixels of the last observed frame. On the one hand, the updated experiments on KTH confirm the good results of the method in these conditions. On the other hand, the fact that this baseline is better than all other methods (not just the authors's) on UCF101 casts some doubts on whether reporting average statistics on UCF101 is insightful enough. Although the authors provide some qualitative analysis pertaining to the quantity of motion, further quantitative analysis seems necessary to validate the performance of this and other methods on future frame prediction. At least, the results on UCF101 should be disambiguated with respect to the type of scene, for instance by measuring the overall quantity of motion (e.g., l2 norm of time differences) and reporting PSNR and SSIM per quartile / decile. Ideally, other realistic datasets than UCF101 should be considered in complement. For instance, the Hollywood 2 dataset of Marszalek et al would be a good candidate, as it focuses on movies and often contains complex actor, camera, and background motions that would make the "pixel-copying" baseline very poor. Experiments on video datasets beyond actions, like the KITTI tracking benchmark, would also greatly improve the paper.  Additional recognition experiments: As mentioned in pre-review questions, further UCF-101 experiments on action recognition tasks by fine-tuning would also greatly improve the paper. Classifying videos indeed requires learning both appearance and motion features, and the two-stream encoder + combination layers of the MCNet+Res architecture seem particularly adapted, if they indeed allowed for unsupervised pre-trainining of content and motion representations, as postulated by the authors. These experiments would also contribute to dispelling the aforementioned concerns about the static nature of the learned representations.  4) Conclusion  Overall, this paper proposes an interesting architecture for an important problem, but requires additional experiments to substantiate the claims made by the authors. If the authors make the aforementioned additional experiments and the results are convincing, then this paper would be clearly relevant for ICLR.  5) Post-rebuttal final decision  The authors did a significant amount of additional work, following the suggestions made by the reviewers, and providing additional compelling experimental evidence. This makes this one of the most experimentally thorough ones for this problem. I, therefore, increase my rating, and suggest to accept this paper. Good job!

Output: en


Input: Consider Input: This paper proposes a framework for evaluation of word embeddings based on data efficiency and simple supervised tasks. The main motivation is that word embeddings are generally used in a transfer learning setting, where evaluation is done based on how faster is to train a target model. The approach uses a set of simple tasks evaluated in a supervised fashion, including common benchmarks such as word similarity and word analogy. Experiments on a broad set of embeddings show that ranks tend to be task-specific and change according to the amount of training data used.  Strengths  - The transfer learning / data efficiency motivation is an interesting one, as it directly relates to the idea of using embeddings as a simple "semi-supervised" approach.  Weaknesses  - A good evaluation approach would be one that propagates to end tasks. Specifically, if the approach gives some rank R for a set of embeddings, I would like it to follow the same rank for an end task like text classification, parsing or machine translation. However, the approach is not assessed in this way so it is difficult to trust the technique is actually more useful than what is traditionally done. - The discussion about injective embeddings seems completely out-of-topic and does not seem to add to the paper's understanding. - The experimental section is very confusing. Section 3.7 points out that the analysis results in answers to questions as "is it worth fitting syntax specific embeddings even when supervised datset is large?" but I fail to understand where in the evaluation the conclusion was made. - Still in Section 3.7, the manuscript says "This hints, that purely unsupervised large scale pretraining might not be suitable for NLP applications". This is a very bold assumption and I again fail to understand how this can be concluded from the proposed evaluation approach. - All embeddings were obtained as off-the-shelf pretrained ones so there is no control over which corpora they were trained on. This limits the validity of the evaluation shown in the paper. - The manuscript needs proofreading, especially in terms of citing figures in the right places (why Figure 1, which is on page 3, is only cited in page 6?).  General Discussion  I think the paper starts with a very interesting motivation but it does not properly evaluate if their approach is good or not. As mentioned above, for any intrinsic evaluation approach I expect to see some study if the conclusions propagate to end tasks and this is not done in the paper. The lack of clarity and proofreading in the manuscript also hinders the understanding. In the future, I think the paper would vastly benefit from some extrinsic studies and a more controlled experimental setting (using the same corpora to train all embeddings, for instance). But in the current state I do not think it is a good addition to the conference.


Output: en