instruction:
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].
question:
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)
answer:
en

question:
I'd like to thank the authors for their detailed response and clarifications. This work proposes new training scheme for online sparse dictionary learning. The model assumes a non-stationary flow of the incoming data. The goal (and the challenge) is to learn a model in an online manner in a way that is capable of adjusting to the new incoming data without forgetting how to represent previously seen data. The proposed approach deals with this problem by incorporating a mechanism for adding or deleting atoms in the dictionary. This procedure is inspired by the adult neurogenesis phenomenon in the dentate gyrus of the hippocampus. The paper has two main innovations over the baseline approach (Mairal et al): (i) "neuronal birth" which represents an adaptive way of increasing the number of atoms in the dictionary (ii) "neuronal death", which corresponds to removing "useless" dictionary atoms. Neural death is implemented by including an group-sparsity regularization to the dictionary atoms themselves (the group corresponds to a column of the dictionary). This promotes to shrink to zero atoms that are not very useful, keeping controlled the increase of the dictionary size. I believe that the strong side of the paper is its connections with the adult neurogenesis phenomenon, which is, in my opinion a very nice feature. The paper is very well written and easy to follow. On the other hand, the overall technique is not very novel. Although not exactly equivalent, similar ideas have been explored. While the neural death is implemente elegantly with a sparsity-promoting regularization term, the neural birth is performed by relying on heuristics that measure how well the dictionary can represent new incoming data. Which depending on the "level" of non-stationarity in the incoming data (or presence of outliers) could be difficult to set. Still, having adaptive dictionary size is very interesting. The authors could also cite some references in model selection literature. In particular, some ideas such as MDL have been used for automatically selecting the dictionary size (I believe this work does not address the online setting, but still its a relevant reference to have). For instance, Ramirez, Ignacio, and Guillermo Sapiro. "An MDL framework for sparse coding and dictionary learning." IEEE Transactions on Signal Processing 60.6 (2012): 2913-2927.
answer:
en

question:
SUMMARY This paper discusses how data from a special type of low dimensional structure (monotonic chain) can be efficiently represented in terms of neural networks with two hidden layers. PROS Interesting, easy to follow view on some of the capabilities of neural networks, highlighting the dimensionality reduction aspect, and pointing at possible directions for further investigation. CONS The paper presents a construction illustrating certain structures that can be captured by a network, but it does not address the learning problem (although it presents experiments where such structures do emerge, more or less). COMMENTS It would be interesting to study the ramifications of the presented observations for the case of deep(er) networks. Also, to study to what extent the proposed picture describes the totality of functions that are representable by the networks. MINOR COMMENTS - Figure 1 could be referenced first in the text. - ''Color coded'' where the color codes what? - Thank you for thinking about revising the points from my first questions. Note: Isometry on the manifold. - On page 5, mention how the orthogonal projection on S_k is realized in the network. - On page 6 ''divided into segments'' here 'segments' is maybe not the best word. - On page 6 ''The mean relative error is 0.98'' what is the baseline here, or what does this number mean?
answer:
en