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].

Input: Consider Input: This paper makes three main methodological contributions:  - definition of Neural Feature (NF) as the pixel average of the top N images that highly activation a neuron  - ranking of neurons based on color selectivity  - ranking of neurons based on class selectivity  The main weaknesses of the paper are that none of the methodological contributions are very significant, and no singularly significant result arises from the application of the methods.  However, the main strengths of the paper are its assortment of moderately-sized interesting conclusions about the basic behavior of neural nets. For example, a few are:  - “Indexing on class selectivity neurons we found highly class selective neurons like digital-clock at conv2, cardoon at conv3 and ladybug at conv5, much before the fully connected layers.” As far as I know, this had not been previously reported.  - Color selective neurons are found even in higher layers. (25% color selectivity in conv5)  - “our main color axis emerge (black-white, blue-yellow, orange-cyan and cyan- magenta). Curiously, these two observations correlate with evidences in the human visual system (Shapley & Hawken (2011)).” Great observation!  Overall, I’d recommend the paper be accepted, because although it’s difficult to predict at this time, there’s a fair chance that one of the “smaller conclusions” would turn out to be important in hindsight a few years hence.   Other small comments:  - The cite for “Learning to generate chairs…” is wrong (first two authors combined resulting in a confusing cite)   - What exactly is the Color Selectivity Index computing? The Opponent Color Space isn’t well defined and it wasn’t previously familiar to me. Intuitively it seems to be selecting for units that respond to a constant color, but the highest color selectivity NF in Fig 5 i for a unit with two colors, not one. Finally, the very last unit (lowest color selectivity) is almost the same edge pattern, but with white -> black instead of blue -> orange. Why are these considered to be so drastically different? This should probably be more clearly described.   - For the sake of argument, imagine a mushroom sensitive neuron in conv5 that fires highly for mushrooms of *any* color but not for anything else. If the dataset contains only red-capped mushrooms, would the color selectivity index for this neuron be high or low? If it is high, it’s somewhat misleading because the unit itself actually isn’t color selective; the dataset just happens only to have red mushrooms in it. (It’s a subtle point but worth considering and probably discussing in the paper)

Output: en


Input: Consider Input: Presenta una búsqueda de respuesta a partir de una solicitud en un call center.  El trabajo está bien presentado, pero muy largo, y eso no permite ver bien lo que quieren aportar los autores.

Output: es


Input: Consider Input: The primary point made by this paper is that given certain architectural characteristics of multi-GPU systems, namely the use of bi-directional PCI-E for communication and the integration of two independent DMA engines on recent GPU devices (providing support for simultaneous independent communications), and given the characteristics of the communications patterns required by synchronous SGD trainers for deep neural networks, namely that the messages are large, dense, and have a fixed length, it makes sense to design communication collectives such as broadcast, reduce, and allreduce specifically for the use case of synchronous SGD training on a multi-GPU system.  The paper describes the implementation of these three collectives (broadcast, reduce, and allreduce) using a linear pipelining (LP) scheme on a (logical) ring topology.  The paper compares the LP collectives to two alternatives:  collectives based on a minimal spanning tree (MST) topology and collectives based on bidirectional exchange (BE).  First, a theoretical comparison is made using a standard cost model used in the high performance computing community.  When assumptions based on multi-GPU system architecture (very low latency for messages) and on the communication characteristics of synchronous SGD training (very large messages) are integrated into the model, the paper finds that the LP collectives should be less costly than BE collectives by a factor of 2 and less costly than MST collectives by a factor of log(p), where p is the number of GPUs being used.  Second, an empirical comparison is performed in which (1) the time required to perform each of the different collectives on a 4-device (k40m) system is measured as a function of message size and (2) the time required to perform each of the different collectives with a 200 MB message length is measured as a function of the number of devices in the system.  These measurements show that the LP-based collectives are consistently the fastest.  Third, DNN training experiments with AlexNet and GoogLeNet are performed on a 4-device system using three different synchronous SGD algorithms with the different implementations of the collectives (a total of 6 different algorithms in all).  Measurements of the communication and computation costs show that the LP collectives reduce communication costs without affecting computation costs (as expected).  Measurements of the convergence of the training loss as a function of time for the two DNN architectures show that use of the LP collectives leads to faster training.  While the theory says that the costs of LP collectives should be invariant to the number of devices in a multi-GPU system, the empirical work shows that in practice this does not hold going from 4 to 5 devices (in the tested configuration) because in a 5-device system messages must traverse the QPI.  Are there other practical considerations that the authors are aware of that affect the scaling of the LP collectives?  If so, these should be mentioned in the paper.  In the sentence "Worringen (2003) proposed a pipeline collective model in shared memory environment for CPU data, but communications of different MPI processes sharing the same CPU memory bus within the same CPU socket." I really can't figure out what the words after "but communications of different MPI processes" are trying to convey.  This sentence is not comprehensible.  "Please note the latency term is log pα, which is the smallest among algorithms in Table.1. Therefore, MST only suits for high frequent short messages."  The claim that MST collectives are only suitable for high-frequency, short messages does not follow from the statement that MST collectives have the smallest latency term.  You also need to consider the way the cost scales with message size (the bandwidth term).  If the MST collectives had a better bandwidth term than the other collectives, then they would also be superior for large messages.  "Let’s take an appropriate block size b to ensure n/b ≪ α."  This looks wrong, since n > b.  Should it be b/n ≪ α?  "However, the parameters are not consistent after several iterations due to the precision issues of float multiplications in Gradient Update."  Are you sure the inconsistency in weight estimates across devices is due to multiplication?  I would expect that it would be due to gradients being accumulated in different orders; that is, because floating point addition is not commutative.  I recommend replacing the term "sub-gradients" in this paper with "partial gradients."  In the optimization literature, the term "sub-gradient" has a very specific meaning that differs from this paper's use of the term (see


Output: en