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 proposes a method for generating datasets of pictures from simple building blocks, as well as corresponding logical forms and language descriptions. The goal seems to be to have a method where the complexity of pictures and corresponding desciptions can be controlled and parametrized.    - The biggest downside seems to be that the maximally achievable complexity is very limited, and way below the complexity typically faced with image-captioning and other multimodal tasks.   - The relative simplicity is also a big difference to the referenced bAbI tasks (which cover the whole qualitative spectrum of easy-to-hard reasoning tasks), whereas in the proposed method a (qualitatively) easy image reconition task can only be quantitatively made harder, by increasing the number of objects, noise etc in unnatural ways.  - This is also reflected in the experimental section. Whenever the experimental performance results are not satisfying, these cases seem like basic over/underfitting issues that may easily be tackled by restricting/extending the capacity of the networks or using more data. It is hard for me to spot any other qualitative insight.  - In the introduction it is stated that the "goal is not too achieve optimal performance" but to find out whether "architectures are able to successfully demonstrate the desired understanding" - there is a fundamental contradiction here, in that the proposed task on the one side is meant to provide a measure as to whether architectures demontrate "understanding", on the other hand the score is not supposed to be taken as meaningful/seriously.  General comments: The general approach should be made more tangible earlier (i.e. in the introction rather than in section 3)
en

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.
en

The paper introduces a time dependent recommender system based on point processes parametrized by time dependent user and item latent representations. The later are modeled as coupled – autoregressive processes – i.e. the representation of a user/item changes when he interacts with an item/user, and is a function of both the user and the item representations before time t. This is called coevolution here and the autoregressive process is called recurrent NN. The model may also incorporate heterogeneous inputs. Experiments are performed on several datasets, and the model is compared with different baselines. There are several contributions in the paper: 1) modeling recommendation via parametrized point processes where the parameter dynamics are modeled by latent user/item representations, 2) an optimization algorithm for maximizing the likelihood of this process, with different technical tricks that seem to break its intrinsic complexity, 3) evaluation experiments for time dependent recommendation. The paper by the same authors (NIPS 2016) describes a similar model of continuous time coevolution, and a similar evaluation. The difference lies in the details of the model: the point process model is not the same and of the latent factor dynamic model is slightly different, but the modeling approach and the arguments are exactly the same. By the end, one does not know what makes this model perform better than the one proposed in NIPS, is it the choice for the process, the new parametrization? Both are quite similar. There is no justification on the choice of the specific form of the point process in the two papers. Did the authors tried other forms as well? The same remark applies for the form of the dynamical process: the non-linearity used for the modeling of the latent user/item vectors here is limited to a sigmoid function, which probably does not change much w.r.t. a linear model, but there is no evidence of the role of this non linearity in the paper. Note that there are some inconsistencies between the results in the two papers. Concerning the evaluation, the authors introduce two criteria. I did not get exactly how they evaluate the item recommendation: it is mentioned that at each time t, the model predicts the item the user will interact with. Do you mean, the next item the user will interact with after time t? For the time prediction, why is it a relevant metric for recommendation? A comparison of the complexity, or execution time of the different methods would be helpful. The complexity of your method is apparently proportional to #items*#users, what are the complexity limits of your methods. Overall, the paper is quite nice and looks technically sound, albeit many details are missing. On the other hand, I have a mixed feeling because of the similarity with NIPS paper. The authors should have make a better work at convincing us that this is not a marginal extension of previous work by the authors.  I was not convinced either by the evaluation criteria and there is no evidence that the model can be used for large datasets.

en