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].
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Question: This paper develops a differentiable interpreter for the Forth programming language. This enables writing a program "sketch" (a program with parts left out), with a hole to be filled in based upon learning from input-output examples. The main technical development is to start with an abstract machine for the Forth language, and then to make all of the operations differentiable. The technique for making operations differentiable is analogous to what is done in models like Neural Turing Machine and Stack RNN. Special syntax is developed for specifying holes, which gives the pattern about what data should be read when filling in the hole, which data should be written, and what the rough structure of the model that fills the hole should be. Motivation for why one should want to do this is that it enables composing program sketches with other differentiable models like standard neural networks, but the experiments focus on sorting and addition tasks with relatively small degrees of freedom for how to fill in the holes.  Experimentally, result show that sorting and addition can be learned given strong sketches.  The aim of this paper is very ambitious: convert a full programming language to be differentiable, and I admire this ambition. The idea is provocative and I think will inspire people in the ICLR community.  The main weakness is that the experiments are somewhat trivial and there are no baselines. I believe that simply enumerating possible values to fill in the holes would work better, and if that is possible, then it's not clear to me what is practically gained from this formulation. (The authors argue that the point is to compose differentiable Forth sketches with neural networks sitting below, but if the holes can be filled by brute force, then could the underlying neural network not be separately trained to maximize the probability assigned to any filling of the hole that produces the correct input-output behavior?)  Related, one thing that is missing, in my opinion, is a more nuanced outlook of where the authors believe this work is going. Based on the small scale of the experiments and from reading other related papers in the area, I sense that it is hard to scale up differentiable forth to large real-world problems. It would be nice to have more discussion about this, and perhaps even an experiment that demonstrates a failure case. Is there a problem that is somewhat more complex than the ones that appear in the paper where the approach does not work? What has been tried to make it work? What are the failure modes? What are the challenges that the authors believe need to be overcome to make this work.  Overall, I think this paper deserves consideration for being provocative. However, I'm hesitant to strongly recommend acceptance because the experiments are weak.

Answer: en


Question: Building on earlier work on a model called NICE, this paper presents an approach to constructing deep feed-forward generative models. The model is evaluated on several datasets. While it does not achieve state-of-the-art performance, it advances an interesting class of models. The paper is mostly well written and clear.  Given that inference and generation are both efficient and exact, and given that this represents a main advantage over other models, it would be great if the authors could provide some motivating example applications where this is needed/would be useful.  The authors claim that “unlike both variational autoencoders and GANs, our technique is able to learn a semantically meaningful latent space which is as high dimensional as the input space.” Where is the evidence for this claim? I didn’t see any analysis of the semantic meaningfulness of the latent space learned by real NVP. Stronger evidence that the learned representations are actually useful for downstream tasks would be nice.  I still think the author’s intuitions around the “fixed reconstruction cost of L2” are very vague. The factorial Gaussian assumption itself does not limit the generative model, it merely smoothes an otherwise arbitrary distribution, and to a degree which can be arbitrarily small, p(x) = \int p(z) N(x | f(z), \sigma^2) dz. How a lose lower bound plays into this is not clear from the paper.

Answer: en


Question: This paper applies RFN for biclustering to overcome the drawbacks in FABIA. The proposed method performs best among 14 biclustering methods, However, my first concern is that from the methodological point of view, the novelty of the proposed method seems small. The authors replied to the same question which another reviewer gave, but the replies were not so convincing.   This paper was actually difficult for me to follow. For instance, in Figure 1, a bicluster matrix is constructed as an outer product of $h$ and $w$. $h$ is a hidden unit, but what is $w$? I could not find any definition. Furthermore, I could not know how $h$ is estimated in this method. Therefore, I do NOT understand how this method performs biclustering.   Totally, I am not sure that this paper is suitable for publication.   Prons: Empirical performance is good.  Cons: Novelty of the proposed method Some description in the paper is unclear.

Answer:
en