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].

Q: This paper combines variational RNN (VRNN) and domain adversarial networks (DANN) for domain adaptation in the sequence modelling domain.  The VRNN is used to learn representations for sequential data, which is the hidden states of the last time step.  The DANN is used to make the representations domain invariant, therefore achieving cross domain adaptation.  Experiments are done on a number of data sets, and the proposed method (VRADA) outperforms baselines including DANN, VFAE and R-DANN on almost all of them.  I don't have questions about the proposed model, the model is quite clear and seems to be a simple combination of VRNN and DANN.  But a few questions came up during the pre-review question phase:  - As the authors have mentioned, DANN in general outperforms MMD based methods, however, the VFAE method which is based on MMD regularization on the representations seems to outperform DANN across the board.  That seems to indicate VRNN + MMD should also be a good combination.  - One baseline the authors showed in the experiments is R-DANN, which is an RNN version of DANN.  There are two differences between R-DANN and VRADA: (1) R-DANN uses deterministic RNN for representation learning, while VRADA uses variational RNN; (2) on target domain R-DANN only optimizes adversarial loss, while VRADA optimizes both adversarial loss and reconstruction loss for feature learning.  It would be good to analyze further where the performance gain comes from.

A: en
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Q: The authors propose to sample from VAEs through a Markov chain [z_t ~ q(z|x=x_{t-1}), x_t ~ p(x|z=z_t)]. The paper uses confusing notation, oversells the novelty, ignoring some relevant previous results. The qualitative difference between regular sampling and this Gibbs chain is not very convincing, judging from the figures. It would be a great workshop paper (perhaps more), if the authors fix the notation, fix the discussion to related work, and produce more convincing (perhaps simply upscaled?) figures.  Comments:   - Rezende et al's (2014) original VAE paper already discusses the Markov chain, which is ignored in this paper  - Notation is nonstandard / confusing. At page 1, it’s unclear what the authors mean with “p(x|z) which is approximated as q(x|z)”. - It’s also not clear what’s meant with q(z). At page 2, q(z) is called the learned distribution, while p(z) can in general also be a learned distribution. - It’s not true that it’s impossible to draw samples from q(z): one can sample x ~ q(x) from the dataset, then draw z ~ q(z|x). - It's not explained whether the analysis only applies to continuous observed spaces, or also discrete observed spaces - Figures 3 and 4 are not very convincing.

A: en
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Q: 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

A:
en
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