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:
Overview: This work seems very promising, but I believe it should be compared with more baselines, and more precisely described and explained, from a signal processing point of view.  Pros: New descriptor Fast implementation  Cons: a) Lack of rigor b) Too long accordingly to the content c) The computational gain of the algorithm is not clear d) The work is not compared with its most obvious baseline: a scattering transform  I will detail each cons.  a) Section 1: The author  motivates the use of scattering transform because it defines a contraction of the space that relies on geometric features. " The nonlinearity used in the scattering network is the complex modulus which is piecewise linear." A real modulus is piecewise linear. A complex modulus has a shape of bell when interpreting C as R^2. Could you clarify? \Omega is not introduced.  Could you give a precise reference (page+paper) of this claim: “Higher order nonlinearity refers to |x|^2 instead of |x| as it is usually done in the scattering network.” ?  Section 2: The motivation of the non-linearity is not clear. First, this non-linearity might potentially increase a lot the variance of your architecture since it depends on higher moments(up to 4). I think a fair analysis would be to compute numerically the normalized variance (e.g. divided by the averaged l^2 norm), as a sanity check. Besides, one should prove that the energy is decreasing. It is not possible to argue that this architecture is similar to a scattering transform which has precise mathematical foundations and those results are required, since the setting is different.  Permutation is not a relevant variability.  The notion of sparsity during the whole paper sometimes refers to the number of 0 value, either the l^1 norm. Mathematically, a small value, even 10^-1000 is still a non 0 value.  Did you compute the graph of the figure 4 on the bird dataset? You might use a ratio instead for clarity.   The wavelet that is defined is not a morlet wavelet (
answer:
en


question:
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
answer:
en


question:
This paper extends preceding works to create a mapping between the word embedding space of two languages. The word embeddings had been independently trained on monolingual data only, and various forms of bilingual information is used to learn the mapping. This mapping is then used to measure the precision of translations.  In this paper, the authors propose two changes: "CCA" and "inverted softmax".  Looking at Table 1, CCA is only better than Dina et al in 1 out of 6 cases (It/En @1).  Most of the improvements are in fact obtained by the introduction of the inverted softmax normalization.  Overall, I wonder which aspect of this paper is really new. You mention:  - Faruqui & Dyer 2014 already used CCA and dimensionality reduction  - Xing et al 2015 argued already that Mikolov's linear matrix should be orthogonal  Could you make clear in what aspect your work is different from Faruqui & Dyer 2014 (other the fact that you applied the method to measure translation precision) ?  Using cognates instead of a bilingual directory is a nice trick. Please explain how you obtained this list of cognates ? Obviously, this only works for languages with the same alphabet (for instance Greek and Russian are excluded)  Also, it seems to me that in linguistics the term "cognate" refers to words which have a common etymological origin - they don't necessarily have the same written form (e.g. night, nuit, noche, Nacht). Maybe, you should use a different term ? Those words are probably proper names in news texts.
answer:

en