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 following statement best summarizes the contribution: "This paper shows that model free RL methods can learn how to gather information about physical properties of objects, even when this information is not available to a passive observer, and use this information to make decisions." So this is not a paper about new theory or algorithms, but rather about solving the problem of acquiring knowledge about the physics of the world around us, which is important for many problems and helps explain human performance in many tasks. There are still some concerns about the depth-of-analysis of the paper, but on balance, it is seen as an unconventional but interesting paper. As per AnonReviewer6, the final version could still aim to better address "What should other researchers focus on if they are trying to build agents that can understand physics intuitively (building off this work)?" -- Area chair
[EX A]: en

[EX Q]: In this paper, the authors explicitly design geometrical structure into a CNN by combining it with a Scattering network. This aids stability and limited-data performance. The paper is well written, the contribution of combining Scattering and CNNs is novel and the results seem promising. I feel that such work was a missing piece in the Scattering literature to make it useful for practical applications. I wish the authors would have investigated the effect of the stable bottom layers with respect to adversarial examples. This can be done in a relatively straightforward way with software like cleverhans [1] or deep fool [2]. It would be very interesting if the first layer's stability in the hybrid architectures increases robustness significantly, as this would tell us that these fooling images are related to low-level geometry. Finding that this is not the case, would be very interesting as well. Further, the proposed architecture is not evaluated on real limited data problems. This would further strengthen the improved generalization claim. However, I admit that the Cifar-100 / Cifar-10 difference already seems like a promising indicator in this regard. If one of the two points above will be addressed in an additional experiment, I would be happy to raise my score from 6 to 7. Summary: + An interesting approach is presented that might be useful for real-world limited data scenarios. + Limited data results look promising. - Adversarial examples are not investigated in the experimental section. - No realistic small-data problem is addressed. Minor: - The authors should add a SOTA ResNet to Table 3, as NiN is indeed out of fashion these days. - Some typos: tacke, developping, learni. [1]
[EX A]: en

[EX Q]: The authors propose to combine a CCA objective with a downstream loss. This is a really nice and natural idea. However, both the execution and presentation leave a lot to be desired in the current version of the paper. It is not clear what the overall objective is. This was asked in a pre-review question but the answer did not fully clarify it for me. Is it the sum of the CCA objective and the final (top-layer) objective, including the CCA constraints? Is there some interpolation of the two objectives? By saying that the top-layer objective is "cosine distance" or "squared cosine distance", do you really mean you are just minimizing this distance between the matched pairs in the two views? If so, then of course that does not work out of the box without the intervening CCA layer: You could minimize it by setting all of the projections to a single point. A better comparison would be against a contrastive loss like the Hermann & Blunsom one mentioned in the reviewer question, which aims to both minimize the distance for matched pairs and separate mismatched ones (where "mismatched" ones can be uniformly drawn, or picked in some cleverer way). But other discriminative top-layer objectives that are tailored to a downstream task could make sense. There is some loose terminology in the paper. The authors refer to the "correlation" and "cross-correlation" between two vectors. "Correlation" normally applies to scalars, so you need to define what you mean here. "Cross-correlation" typically refers to time series. In eq. (2) you are taking the max of a matrix. Finally I am not too sure in what way this approach is "fully differentiable" while regular CCA is not -- perhaps it is worth revisiting this term as well. Also just a small note about the relationship between cosine distance and correlation: they are related when we view the dimensions of each of the two vectors as samples of a single random variable. In that case the cosine distance of the (mean-normalized) vectors is the same as the correlation between the two corresponding random variables. In CCA we are viewing each dimension of the vectors as its own random variable. So I fear the claim about cosine distance and correlation is a bit of a red herring here. A couple of typos: "prosed" --> "proposed" "allong" --> "along"
[EX A]:
en