instruction:
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].
question:
The authors of this work propose a learnable approach to reducing the dimensionality of learned filters in deep neural networks. This is an interesting approach, but the presented work looks a bit raw.  1. There are many typos in this manuscript.  2. The experimental results are rather weak and don't show much improvement in accuracy. Instead the authors could position this work as a compression mechanism and would have to compare to low rank approximation of filters for DNNs. Yet this is not done.  3. Aside from compression, OMG can be viewed as a form of regularization to reduce the unnecessary capacity of the network to improve generalization. Again, this is not addressed in enough detail. 4. If the authors care to compare their approach to other 1-shot learning methods, then they would have to evaluate their approach with siamese and triplet learning networks. This isn't done.
answer:
en


question:
The topic of the paper, model-based RL with a learned model, is important and timely. The paper is well written. I feel that the presented results are too incremental. Augmenting the frame prediction network with another head that predicts the reward is a very sensible thing to do. However neither the methodology not the results are novel / surprising, given that the original method of [Oh et al. 2015] already learns to successfully increment score counters in predicted frames in many games.  I’m very much looking forward to seeing the results of applying the learned joint model of frames and rewards to model-based RL as proposed by the authors.
answer:
en


question:
SUMMARY  This paper discusses how data from a special type of low dimensional structure (monotonic chain) can be efficiently represented in terms of neural networks with two hidden layers.   PROS  Interesting, easy to follow view on some of the capabilities of neural networks, highlighting the dimensionality reduction aspect, and pointing at possible directions for further investigation.   CONS  The paper presents a construction illustrating certain structures that can be captured by a network, but it does not address the learning problem (although it presents experiments where such structures do emerge, more or less).   COMMENTS  It would be interesting to study the ramifications of the presented observations for the case of deep(er) networks.  Also, to study to what extent the proposed picture describes the totality of functions that are representable by the networks.   MINOR COMMENTS  - Figure 1 could be referenced first in the text.   - ``Color coded'' where the color codes what?  - Thank you for thinking about revising the points from my first questions. Note: Isometry on the manifold.  - On page 5, mention how the orthogonal projection on S_k is realized in the network.  - On page 6 ``divided into segments'' here `segments' is maybe not the best word.  - On page 6 ``The mean relative error is 0.98'' what is the baseline here, or what does this number mean?
answer:

en