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: The paper describes a method to evaluate generative models such as VAE, GAN and GMMN. This is very much needed in our community where we still eyeball generated images to judge the quality of a model. However, the technical increment over the NIPS 16 paper: “Measuring the reliability of MCMC inference with bidirectional Monte Carlo” is very small, or nonexistent (but please correct me if I am wrong!).  (Grosse et al). The relative contribution of this paper is the application of this method to generative models.  In section 2.3 the authors seem to make a mistake. They write E[p’(x)] <= p(x) but I think they mean: E[log p’(x)] <= log E[p’(x)] = log p(x). Also,  for what value of x? If p(x) is normalized it can’t be true for all values of x. Anyways, I think there are typos here and there and the equations could be more precise. On page 5 top of the page it is said that the AIS procedure can be initialized with q(z|x) instead of p(z). However, it is unclear what value of x is then picked? Is it perhaps Ep(x)[q(z|x)] ? I am confused with the use of the term overfitting (p8 bottom). Does a model A overfit relative to a another model B if the test accuracy of A is higher than that of B even though the gap between train and test accuracy is also higher for B than for A. I think not. Perhaps the last sentence on page 8 should say that VAE-50 underfits less than GMMN-50? The experimental results are interesting in that it exposes the fact that GANs and GMMNs seem to have much lover test accuracy than VAE despite the fact that their samples look great.

SOLUTION: en

PROBLEM: This work proposes a convolutional architecture for any graph-like input data (where the structure is example-dependent), or more generally, any data where the input dimensions that are related by a similarity matrix. If instead each input example is associated with a transition matrix, then a random walk algorithm is used generate a similarity matrix.  Developing convolutional or recurrent architectures for graph-like data is an important problem because we would like to develop neural networks that can handle inputs such as molecule structures or social networks. However, I don't think this work contributes anything significant to the work that has already been done in this area.   The two main proposals I see in this paper are: 1) For data associated with a transition matrix, this paper proposes that the transition matrix be converted to a similarity matrix. This seems obvious. 2) For data associated with a similarity matrix, the k nearest neighbors of each node are computed and supply the context information for that node. This also seems obvious.  Perhaps I have misunderstood the contribution, but the presentation also lacks clarity, and I cannot recommend this paper for publication.   Specific Comments: 1) On page 4: "An interesting attribute of this convolution, as compared to other convolutions on graphs is that, it preserves locality while still being applicable over different graphs with different structures."  This is false; the other proposed architectures can be applied to inputs with different structures (e.g. Duvenaud et. al., Lusci et. al. for NN architectures on molecules specifically).

SOLUTION: en

PROBLEM: - General Discussion:  This paper extends Zhou and Xu's ACL 2015 approach to semantic role labeling based on deep BiLSTMs. In addition to applying recent best practice techniques, leading to further quantitative improvements, the authors provide an insightful qualitative analysis of their results. The paper is well written and has a clear structure. The authors provide a comprehensive overview of related work and compare results to a representative set of other SRL models that hace been applied on the same data sets.  I found the paper to be interesting and convincing. It is a welcome research contribution that not only shows that NNs work well, but also analyzes merits and shortcomings of an end-to-end learning approach.  - Strengths:  Strong model, insightful discussion/error analysis.  - Weaknesses:  Little to no insights regarding the SRL task itself.

SOLUTION:
en