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].

Strengths  - interesting to explore the connection between ReLU DNN and simplified SFNN - small task (MNIST)  is used to demonstrate the usefulness of the proposed training methods experimentally - the proposed, multi-stage training methods are simple to implement (despite lacking theoretical rigor)   Weaknesses  -no results are reported on real tasks with large training set  -not clear exploration on the scalability of the learning methods when training data becomes larger  -when the hidden layers become stochastic, the model shares uncertainty representation with deep Bayes networks or deep generative models (Deep Discriminative and Generative Models for Pattern Recognition , book chapter in “Pattern Recognition and Computer Vision”, November 2015, Download PDF). Such connections should be discussed, especially wrt the use of uncertainty representation to benefit pattern recognition (i.e. supervised learning via Bayes rule) and to benefit the use of domain knowledge such as “explaining away”.  -would like to see connections with variational autoencoder models and training, which is also stochastic with hidden layers
en

- Strengths:  This paper tackles an interesting problem and provides a (to my knowledge) novel and reasonable way of learning and combining cognitive features with textual features for sentiment analysis and irony detection. The paper is  clearly written and organized, and the authors provided a lot of useful detail and informative example and plots. Most of the results are convincing, and the authors did a good job comparing their approach and results with previous work.  - Weaknesses:  1. Just from the reading abstract, I expected that the authors' approach would significantly outperform previous methods, and that using both the eye-gaze and textual features consistently yields the best results. Upon reading the actual results section, however, it seems like the findings were more mixed. I think it would be helpful to update the abstract and introduction to reflect this.  2. When evaluating the model on dataset 1 for sentiment analysis, were the sarcastic utterances included? Did the model do better on classifying the non-sarcastic utterances than the sarcastic ones? 3. I understand why the eye-movement data would be useful for sarcasm detection, but it wasn't as obvious to me why it would be helpful for (non-sarcastic) sentiment classification beyond the textual features.   - General Discussion:  This paper contains a lot of interesting content, and the approach seems solid and novel to me. The results were a little weaker than I had anticipated from the abstract, but I believe would still be interesting to the larger community and merits publication.
en

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.

en