TASK DEFINITION: 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].
PROBLEM: The authors propose a method to investigate the predictiveness of intermediate layer activations. To do so, they propose training linear classifiers and evaluate the error on the test set.  The paper is well motivated and aims to shed some light onto the progress of model training and hopes to provide insights into deep learning architecture design.  The two main reasons for why the authors decided to use linear probes seem to be: - convexity - The last layer in the network is (usually) linear  In the second to last paragraph of page 4 the authors point out that it could happen that the intermediate features are useless for a linear classifier. This is correct and what I consider the main flaw of the paper. I am missing any motivation as to the usefulness of the suggested analysis to architecture design. In fact, the example with the skip connection (Figure 8) seems to suggest that skip connections shouldn't be used. Doesn't that contradict the recent successes of ResNet?  While the results are interesting, they aren't particularly surprising and I am failing to see direct applicability to understanding deep models as the authors suggest.

SOLUTION: en

PROBLEM: Strengths -- An interesting proposal for a smaller CNN architecture designed for embedded CNN applications.  -- Balanced exploration of CNN macroarchitecture and microarchitecture with fire modules. -- x50 less memory usage than AlexNet, keeping similar accuracy  -- strong experimental results  Weaknesses --Would be nice to test Sqeezenet on multiple tasks  --lack of insights and rigorous analysis into what factors are responsible for the success of SqueezeNet. For example, how are ResNet and GoogleNet connected to the current architecture? Another old paper (Analysis of correlation structure for a neural predictive model with application to speech recognition, Neural Networks, 1994) also showed that the “by-pass” architecture by mixing linear and nonlinear prediction terms improves long term dependency in NN based on rigorous perturbation analysis. Can the current work be placed more rigorously on theoretical analysis?

SOLUTION: en

PROBLEM: El artículo describe algunos aspectos generales de la aplicación de minería de datos a redes sociales.  La revisión de información no es completa y dista mucho de ser considerada como el estado del arte del tema.  Sugiero primero revisar artículos de revistas indexadas en el tema, identificar un problema de investigación concreto, donde se pueda realizar un aporte en el tema, y plantear una propuesta de investigación.

SOLUTION:
es