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: 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.

SOLUTION: en

PROBLEM: Se trata de un articulo bastante particular que propone una extension a los modelos de datos existentes considerando la idea de "sistema" (de objetos). Para esto se propone un lenguaje (LATIN) y un modelo de datos universal?, MED, basado en un "volumen esferico" con dos polos "mayor" y "diario" basados en ideas contables. Este es, a mi juicio, el aspecto mas critico de la propuesta, ya que surge la duda, de si todo sistema deberia calzar en este esquema, o estamos hablando de un conjunto particular de casos. Las ideas posteriores son interesante y, al menos, generan un quiebre con respecto a las ideas clasicas de la orientacion a objetos. Cito la idea de sistema propuesta: "un sistema es un conjunto de clases enlazadas e inter-actuantes segun un mismo metodo, el cual no es particular a cada clase sino emergente de todas ellas". Otras ideas asocian transaccion con conjunto referencial, macros, tipos, instancias, ... lo que en terminos globales constituyen un nuevo cuerpo de ideas. No habiendo en las referencias alguna cita a algun articulo anterior del autor que trate estas ideas, me parece que puede llegar a ser interesante escuchar esta propuesta y debatir al respecto. En terminos de formalidad, el articulo no cumple cabalmente con el formato del congreso, y el numero de paginas excede las ocho solicitadas.

SOLUTION: es

PROBLEM: Paper Strengths: -- Elegant use of MoE for expanding model capacity and enabling training large models necessary for exploiting very large datasets in a computationally feasible manner -- The effective batch size for training the MoE drastically increased also -- Interesting experimental results on the effects of increasing the number of MoEs, which is expected. Paper Weaknesses: --- there are many different ways of increasing model capacity to enable the exploitation of very large datasets; it would be very nice to discuss the use of MoE and other alternatives in terms of computational efficiency and other factors.

SOLUTION:
en