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].

This paper introduces a mechanism for active learning with convolutional neural networks (CNNs). I would not go as far as the authors in calling these "deep", seeing that they seem to have only 2 hidden layers with only 20 filters each. The active learning criterion is a greedy selection scheme based on variational free energy and a series of approximations.  The paper is sometimes hard to read, due to (a) many grammatical errors and (b) sloppy notation in some places (e.g., on page 5, line 1, f is used but never introduced before). Overall, I give an accepting score, but a weak one because of the grammatical errors. If the paper is accepted, these should be fixed for the final version, optimally by a native speaker.   The paper's topic is interesting, and the paper appears to succeed in its goal of showing a proof of concept for active learning in CNNs (if only on toy datasets). I'm surprised by the new results on uncertainty sampling and curriculum learning the authors added: why do these methods both break for USPS? In particular, uncertainty sampling did very well (in fact, better than the authors' new method) on MNIST, but apparently horribly on USPS; some explanation for this would be useful.  I have one more question: why is it necessary to first sample a larger subset D \subset U, from which we select using active learning? Is this merely done for reasons of computational efficiency, or can it actually somehow improve results? (If so, it would be instrumental to see the worse results when this is not done.)
en

Fondo: - Se menciona de manera muy superficial el lenguaje a utilizar en forma posterior a Scratch, quizás no es necesario o se debe ahondar más en el tema. - El número de alumnos sobre el cual se aplicó el estudio es muy pequeño para obtener resultados concluyentes. - Sería deseable una comparación o estudio entre la utilización de Scratch y Alice (mencionado en el trabajo). - Concluir a partir del experimento que Scratch es una herramienta adecuada para la educación en Chile, sin incluir resultados con otros lenguajes es un poco apresurado. - Sería deseable mencionar que instituciones o trabajos relacionados con lenguajes para fomentar el pensamiento lógico se han realizado en Chile. No se menciona ninguno como referencia o en la bibliografía. - El ejercicio utilizado en el experimento es uno sólo, se deberían utilizar más si se desea objetivamente evaluar su utilización como lenguaje para mejorar el razonamiento lógico. - La primera cita proviene de un portal de noticias Colombiano y del 2006. Sería deseable una referencia más sólida y local asociada quizás a los lineamientos y planes de estudio del ministerio de educación.   Forma: - Se debe re-escribir el abstract: posee muchas palabras separadas por comas y la redacción incluye ideas desconectadas entre sí. - Se cita 8 veces la cita 7/8. Con la mitad de ellas queda clara la referencia. - ".. pero pocos aprender a programar .." debería ser: ".. pero pocos aprendeN a programar ..". - La cita 2 dice .. "Fundamentos de POgramacion".. debería ser: ".. Fundamentos de Programación".
es

This paper proposes to initialize the weights of a deep neural network layer-wise with a marginal Fisher analysis model, making use of potentially the similarity metric.   Pros:  There are a lot of experiments, albeit small datasets, that the authors tested their proposed method on.  Cons: lacking baseline such as discriminatively trained convolutional network on standard dataset such as CIFAR-10. It is also unclear how costly in computation to compute the association matrix A in equation 4.  This is an OK paper, where a new idea is proposed, and combined with other existing ideas such as greedy-layerwise stacking, dropout, and denoising auto-encoders. However, there have been many papers with similar ideas perhaps 3-5 years ago, e.g. SPCANet.   Therefore, the main novelty is the use of marginal Fisher Analysis as a new layer. This would be ok, but the baselines to demonstrate that this approach works better is missing. In particular, I'd like to see a conv net or fully connected net trained from scratch with good initialization would do at these problems.  To improve the paper, the authors should try to demonstrate without doubt that initializing layers with MFA is better than just random weight matrices.

en