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 proposes to initialize the weights of a deep neural network layer-wise with a marginal Fisher analysis model, making use of potentially the similarity metric. Pros: There are a lot of experiments, albeit small datasets, that the authors tested their proposed method on. Cons: lacking baseline such as discriminatively trained convolutional network on standard dataset such as CIFAR-10. It is also unclear how costly in computation to compute the association matrix A in equation 4. This is an OK paper, where a new idea is proposed, and combined with other existing ideas such as greedy-layerwise stacking, dropout, and denoising auto-encoders. However, there have been many papers with similar ideas perhaps 3-5 years ago, e.g. SPCANet. Therefore, the main novelty is the use of marginal Fisher Analysis as a new layer. This would be ok, but the baselines to demonstrate that this approach works better is missing. In particular, I'd like to see a conv net or fully connected net trained from scratch with good initialization would do at these problems. To improve the paper, the authors should try to demonstrate without doubt that initializing layers with MFA is better than just random weight matrices.
[A]: en

[Q]: This paper presents a variational inference based method for learning nonlinear dynamical systems. Unlike the deep Kalman filter, the proposed method learns a state space model, which forces the latent state to maintain all of the information relevant to predictions, rather than leaving it implicit in the observations. Experiments show the proposed method is better able to learn meaningful representations of sequence data. The proposed DVBF is well motivated, and for the most part the presentation is clear. The experiments show interesting results on illustrative toy examples. I think the contribution is interesting and potentially useful, so I'd recommend acceptance. The SVAE method of Johnson et al. (2016) deserves more discussion than the two sentences devoted to it, since the method seems pretty closely related. Like the DVBF, the SVAE imposes a Markovianity assumption, and it is able to handle similar kinds of problems. From what I understand, the most important algorithmic difference is that the SVAE q network predicts potentials, whereas the DVBF q network predicts innovations. What are the tradeoffs between the two? Section 2.2 says they do the latter in the interest of solving control-related tasks, but I'm not clear why this follows. Is there a reason SVAEs don't meet all the desiderata mentioned at the end of the Introduction? Since the SVAE code is publicly available, one could probably compare against it in the experiments. I'm a bit confused about the role of uncertainty about v. In principle, one could estimate the transition parameters by maximum likelihood (i.e. fitting a point estimate of v), but this isn't what's done. Instead, v is integrated out as part of the marginal likelihood, which I interpret as giving the flexibility to model different dynamics for different sequences. But if this is the case, then shouldn't the q distribution for v depend on the data, rather than being data-independent as in Eqn. (9)?
[A]: en

[Q]: The paper proposes a recurrent neural architecture that can skip irrelevant input units. This is achieved by specifying R (# of words to read at each "skim"), K (max jump size), and N (max # of jumps allowed). An LSTM processes R words, predicts the jump size k in {0, 1...K} (0 signals stop), skips the next k-1 words and continues until either the number of jumps reaches N or the model reaches the last word. While the model is not differentiable, it can be trained by standard policy gradient. The work seems to have been heavily influenced by Shen et al. (2016) who apply a similar reinforcement learning approach (including the same variance stabilization) to multi-pass machine reading. - Strengths: The work simulates an intuitive "skimming" behavior of a reader, mirroring Shen et al. who simulate (self-terminated) repeated reading. A major attribute of this work is its simplicity. Despite the simplicity, the approach yields favorable results. In particular, the authors show through a well-designed synthetic experiment that the model is indeed able to learn to skip when given oracle jump signals. In text classification using real-world datasets, the model is able to perform competitively with the non-skimming model while being clearly faster. The proposed model can potentially have meaningful practical implications: for tasks in which skimming suffices (e.g., sentiment classification), it suggests that we can obtain equivalent results without consuming all data in a completely automated fashion. To my knowledge this is a novel finding. - Weaknesses: It's a bit mysterious on what basis the model determines its jumping behavior so effectively (other than the synthetic dataset). I'm thinking of a case where the last part of the given sentence is a crucial evidence, for instance: "The movie was so so and boring to the last minute but then its ending blew me away." In this example, the model may decide to skip the rest of the sentence after reading "so so and boring". But by doing so it'll miss the turning point "ending blew me away" and mislabel the instance as negative. For such cases a solution can be running the skimming model in both directions as the authors suggest as future work. But in general the model may require more sophisticated architecture for controlling skimming. It seems one can achieve improved skimming by combining it with multi-pass reading (presumably in reverse directions). That's how humans read to understand text that can't be digested in one skim; indeed, that's how I read this draft. Overall, the work raises an interesting problem and provides an effective but intuitive solution.
[A]:
en