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 paper explores a VAE architecture and training procedure that allows to generate new samples of a concept based on several exemplars that are shown to the model. The proposed architecture processes the set of exemplars with a recurrent neural network and aggregation procedure similar to the one used in Matching Networks. The resulting "summary" is used to condition a generative model (a VAE) that produces new samples of the same kind as the exemplars shown. The proposed aggregation and conditioning procedure are better suited to sets of exemplars that come from several classes than simple averaging. Perhaps surprisingly the model generalizes from generation conditioned on samples from 2 classes to generation conditioned on samples from 4 classes. The experiments are conducted on the OMNIGLOT dataset and are quite convincing. An explicit comparison to previous works is lacking, but this is explained in the appendices, and a comparison to architectures similar to previous work is presented.
answer:
en


question:
Paper Summary:  Authors investigate identity re-parametrization in the linear and the non linear case.   Detailed comments:  — Linear Residual Network:  The paper shows that for a linear residual network any critical point is a global optimum. This problem is non convex it is interesting that this simple re-parametrization leads to such a result.    — Non linear Residual Network:  Authors propose a construction that maps the points to their labels via a resnet , using an initial random projection, followed by a residual block that clusters the data based on their label, and a last layer that maps the clusters to the label.   1- In Eq 3.4  seems the dimensions are not matching q_j in R^k and e_j in R^r. please clarify   2- The construction seems fine, but what is special about the resnet here in this construction? One can do a similar construction if we did not have the identity? can you discuss this point? In the linear case it is clear from a spectral point of view how the identity is helping the optimization. Please provide some intuition.    3-   Existence of a network in the residual  class that overfits does it give us any intuition on why residual network outperform other architectures? What does an existence result of such a network tell us about its representation power ?  A simple linear model under the assumption that points can not be too close can overfit the data, and get fast convergence rate (see for instance tsybakov noise condition).  4- What does the construction tell us about the number of layers?   5- clustering the activation independently from the label, is an old way to pretrain the network. One could use those centroids as weights for the next layer (this is also related to Nystrom approximation see for instance
answer:
en


question:
El artículo está muy bien escrito y se lee con facilidad, teniendo básicamente características de un artículo de divulgación.  Se discuten aspectos centrados esencialmente en los problemas de recuperación de información ante el crecimiento exponencial de los datos. La propuesta realizada al final del artículo es interesante, aunque, desde mi punto de vista la inclusión del tema Big Data está dado con cierta liviandad y requiere un análisis más detallado de lo que se describe.
answer:

es