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].

[EX 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.
[EX A]: en

[EX Q]: The paper propose to find an optimal decoder for binary data using a min-max decoder on the binary hypercube given a linear constraint on the correlation between the encoder and the data. The paper gives finally that the optimal decoder as logistic of the lagragian W multiplying the encoding e. Given the weights of the 'min-max'decoder W the paper finds the best encoding for the data distribution considered, by minimizing that error as a function of the encoding. The paper then alternates that optimization between the encoding and the min-max decoding, starting from random weights W. clarity: -The paper would be easier to follow if the real data (x in section 3 ) is differentiated from the worst case data played by the model (x in section 2). significance Overall I like the paper, however I have some doubts on what the alternating optimization optimum ends up being. The paper ends up implementing a single layer network. The correlation constraints while convenient in the derivation, is a bit intriguing. Since linear relation between the encoding and the data seems to be weak modeling constraint and might be not different from what PCA would implement. - what is the performance of PCA on those tasks? one could you use a simple sign function to decode. This is related to one bit compressive sensing. - what happens if you initialize W in algorithm one with PCA weights? or weighted pca weights? - Have you tried on more complex datasets such as cifar?
[EX A]: en

[EX Q]: 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
[EX A]:
en