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: Summary of the paper  The paper studies the invertiblity of convolutional neural network in the random model. A reconstruction algorithm similar to IHT is proposed for layer-wise inversion of the network.    Clarity:  - The paper is confusing wrt to standard notations in deep learning.  Comments:  The paper makes two simplifications in the analysis of a CNN, that makes it map to a model based compressive sensing framework:  1-  The non linearity (RELU) is dropped. This is a big simplification, for random gaussian weights for instance we know by JL that we can preserve L_2 distance, when RELU is applied the metric changes (see for instance the kernel for n=1 in

SOLUTION: en

PROBLEM: El artículo presenta el desarrollo de una aplicación móvil para realizar denuncias, avisar de algún acto criminal y crear un mapa del delito propio. El artículo requiere de una mejor redacción y mejorar su estructura técnica como artículo de investigación. Por ejemplo, no presenta el resumen en español.  Al ser una aplicación software, su contribución a la investigación es limitada. Sugiero postular a track o workshop de aplicaciones empresariales.

SOLUTION: es

PROBLEM: This paper proposes to use RNN and reinforcement learning for solving combinatorial optimization problems. The use of pointer network is interesting as it enables generalization to arbitrary input size. The proposed method also "fintunes" on test examples with active search to achieve better performance.  The proposed method is theoretically interesting as it shows that RNN and RL can be combined to solve combinatorial optimization problems and achieve comparable performance to traditional heuristic based algorithms.  However, the lack of complexity comparison against baselines make it impossible to tell whether the proposed method has any practical value. The matter is further complicated by the fact that the proposed method runs on GPU while baselines run on CPU: it is hard to even come up with a meaningful unit of complexity. Money spent on hardware and electricity per instance may be a viable option.  Further more, the performance comparisons should be taken with a grain of salt as traditional heuristic based algorithms can often give better performance if allowed more computation, which is not controlled across algorithms.

SOLUTION:
en