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]: The starting point of this work is the understanding that by having decorrelated neurons (e.g. neurons that only fire on background, or only on foreground regions) one provides independent pieces of information to the subsequent decisions. As such one gives "complementary viewpoints" of the input to the subsequent layers, which can be thought of as performing ensembling/expert combination within the model, rather than using an ensemble of networks.   For this, the authors propose a sensible method to decorrelate the activations of intermediate neurons, with the aim of delivering complementary inputs to the final classification layers: they split intermediate neurons to a "foreground" and a "background" subset, and append side-losses that force them to be zero on background and foreground pixels respectively.   They demonstrate that this can improve classification on a mid-scale classification example (a fraction of imagenet, and a ResNet with 18, rather than 150 layers), when compared to a "vanilla" baseline that does not use these losses.  I enjoyed reading the paper because the idea is simple, smart, and seems to be effective.  But there are a few concerns; -firstly, the way of doing this seems very particular to vision. In vision one knows that masking the features (during both training and testing) helps, e.g.
[EX A]: en

[EX Q]: This paper proposes a new kind of expert model where a sparse subset of most reliable experts is chosen instead of the usual logarithmic opinion pool of a PoE. I find the paper very unclear. I tried to find a proper definition of the joint model p(x,z) but could not extract this from the text. The proposed “EM-like” algorithm should then also follow directly from this definition. At this point I do not see if such as definition even exists. In other words, is there is an objective function on which the iterates of the proposed algorithm are guaranteed to improve on the train data? We also note that the “product of unifac models” from Hinton tries to do something very similar where only a subset of the experts will get activated to generate the input:
[EX A]: en

[EX Q]: *** Paper Summary ***  This paper formalizes the properties required for addressing (indexing) memory augmented neural networks as well as how to pair the addressing with read/write operation. It then proposes a framework in which any Lie group as the addressing space. Experiments on algorithmic tasks are reported.  *** Review Summary ***  This paper brings unity and formalism in the requirement for memory addressing while maintaining differentiable memories. Its proposal provide a generic scheme to build addressing mechanisms. When comparing the proposed approach with key-value networks, the unbounded number of memory cells and the lack of incentive to reuse indexes might reveal impractical.   *** Detailed Review ***  The paper reads well, has appropriate relevance to related work. The unified presentation of memory augmented networks is clear and brings unity to the field. The proposed approach is introduced clearly, is powerful and gives a tool that can be reused after reading the article. I do not appreciate that the growing memory is not mentioned as a drawback. It should be stressed and a discussion on the impact it has on efficiency/scalability is needed.
[EX A]:
en