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].

This paper presents a system approach to combine multiple modalities to perform classification in a practical scenario (e-commerce).  In general, I find the proposed approach in the paper sound and solid, but do not see novelty in the paper: feature fusion and decision time fusion are both standard practices in multi-modal analysis, and the rest of the paper offers no surprise in implementing such approaches. This seems to be a better fit for venues that focus more on production systems, and seems to be a bad fit for ICLR where the focus is more on research of novel algorithms and theories.
en

This submission introduces a formulation of Generative Adversarial Networks (GANs) under the lens of density ratio estimation, when using Bregman divergences. Even thought GANs already perform density estimation, the motivation of using Bregman divergences is to obtain an objective function with stronger gradients. I have three concerns with this submission.  First, the exposition of the paper must be significantly improved. The current version of the manuscript is at some points unreadable, and does a poor job at motivating, describing, and justifying the contributions.  Second, the authors scatter a variety of alternatives and heuristics throughout the description of the proposed b-GAN. This introduces a great amount of complexity when it comes to understanding, implementing, and using b-GAN. Further work is necessary to rule out (in a principled manner!) many of the proposed variants of the algorithm.  Third, it is next to impossible to interpret the experimental results, in particular Figures 2, 3, 4. The authors claim that these figures show that "learning does not stop", but such behavior can also be attributed to the typical chaotic dynamics of GANs. Even after reading Appendix A, I am left unconvinced on whether the proposed approach provides with any practical advantage (even no comparison is offered to other GAN approaches with similar architectures).  Overall, I believe this submission calls for significant improvements before being considered for publication.
en

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

