TASK DEFINITION: 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].
PROBLEM: Overview: This work seems very promising, but I believe it should be compared with more baselines, and more precisely described and explained, from a signal processing point of view.  Pros: New descriptor Fast implementation  Cons: a) Lack of rigor b) Too long accordingly to the content c) The computational gain of the algorithm is not clear d) The work is not compared with its most obvious baseline: a scattering transform  I will detail each cons.  a) Section 1: The author  motivates the use of scattering transform because it defines a contraction of the space that relies on geometric features. " The nonlinearity used in the scattering network is the complex modulus which is piecewise linear." A real modulus is piecewise linear. A complex modulus has a shape of bell when interpreting C as R^2. Could you clarify? \Omega is not introduced.  Could you give a precise reference (page+paper) of this claim: “Higher order nonlinearity refers to |x|^2 instead of |x| as it is usually done in the scattering network.” ?  Section 2: The motivation of the non-linearity is not clear. First, this non-linearity might potentially increase a lot the variance of your architecture since it depends on higher moments(up to 4). I think a fair analysis would be to compute numerically the normalized variance (e.g. divided by the averaged l^2 norm), as a sanity check. Besides, one should prove that the energy is decreasing. It is not possible to argue that this architecture is similar to a scattering transform which has precise mathematical foundations and those results are required, since the setting is different.  Permutation is not a relevant variability.  The notion of sparsity during the whole paper sometimes refers to the number of 0 value, either the l^1 norm. Mathematically, a small value, even 10^-1000 is still a non 0 value.  Did you compute the graph of the figure 4 on the bird dataset? You might use a ratio instead for clarity.   The wavelet that is defined is not a morlet wavelet (

SOLUTION: en

PROBLEM: This work proposes to use visualization of gradients to further understand the importance of features (i.e. pixels) for visual classification. Overall, this presented visualizations are interesting, however, the approach is very ad hoc. The authors do not explain why visualizing regular gradients isn't correlated with the importance of features relevant to the given visual category and proceed to the interior gradient approach.   One particular question with regular gradients at features that form the spatial support of the visual class. Is it the case that the gradients of the features that are confident of the prediction remain low, while those with high uncertainty will have strong gradients?  With regards to the interior gradients, it is unclear how the scaling parameter \alpha affects the feature importance and how it is related to attention.  Finally, does this model use batch normalization?

SOLUTION: en

PROBLEM: This paper considers the problem of model-based policy search. The authors  consider the use of Bayesian Neural Networks to learn a model of the environment and advocate for the $\alpha$-divergence minimization rather than the more usual  variational Bayes.   The ability of alpha-divergence to capture bi-modality however  comes at a price and most of the paper is devoted to finding tractable approximations.  The authors therefore use the approach of Hernandez-Lobato et al. (2016) as proxy to the alpha-divergence .   The environment/system dynamics is clearly defined as a well as the policy parametrization  (section 3) and would constitute a useful reference point for other researchers.  Simulated roll-outs, using the learned model, then provide samples of the expected  return. Since a model of the environment is available, stochastic gradient descent  can be performed in the usual way, without policy gradient estimators, via automatic  differentiation tools.   The experiments demonstrate that alpha-divergence is capable of capturing multi-model  structure which competing methods (variational Bayes and GP) would otherwise struggle with. The proposed approach also compares favorably in a real-world batch setting.  The paper is well-written, technically rich and combines many recent tools  into a coherent algorithm. However, the repeated use of approximations to original  quantities seems to somehow defeat the benefits of the original problem formulation.  The scalability and computational effectiveness of this approach is also questionable  and I am uncertain if many problem would warrant such complexity in their solution.  As with other Bayesian methods, the proposed approach would probably shine in low-samples  regime and in this case might be preferable to other methods in the same class (VB, GP).

SOLUTION:
en