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].

Q: Este trabajo describe de manera práctica, una comunicación del como introducirse a la utilización de un robot en particular, bajo un ambiente de prueba libre, que trabaja con este tipo de aplicación. Si bien está bien escrito, solo aporta como tutorial, en vez de resultado de una investigación. Se nota que se está trabajando muy bien, pero es un trabajo preliminar.  Se debe revisar la escrita, porque el resumen es muy introductorio, no indicando lo relevante de lo que presenta el trabajo, también se utilizan nemónicos de los cuales no se describen antes. En el resumen y palabras claves se usan nemónicos que no se describen en el texto anteriormente. Tiene  la particularidad de que es un buen trabajo, pero es preliminar.  No presenta la arquitectura de lo realizado, imágenes que den cuenta de las simulaciones, resultados logrados.

A: es
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Q: Es un buen tema para el congreso. Presenta una buena propuesta de trabajo de vinculación  Está redactado como un abstract extendido, debe ser re-escrito para ser aceptado definitivamente para el congreso. No hay referencias y las que coloca al final son pobres. Por la presentación debería ser castigado

A: es
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Q: This paper presents a variational inference based method for learning nonlinear dynamical systems. Unlike the deep Kalman filter, the proposed method learns a state space model, which forces the latent state to maintain all of the information relevant to predictions, rather than leaving it implicit in the observations. Experiments show the proposed method is better able to learn meaningful representations of sequence data.  The proposed DVBF is well motivated, and for the most part the presentation is clear. The experiments show interesting results on illustrative toy examples. I think the contribution is interesting and potentially useful, so I’d recommend acceptance.  The SVAE method of Johnson et al. (2016) deserves more discussion than the two sentences devoted to it, since the method seems pretty closely related. Like the DVBF, the SVAE imposes a Markovianity assumption, and it is able to handle similar kinds of problems. From what I understand, the most important algorithmic difference is that the SVAE q network predicts potentials, whereas the DVBF q network predicts innovations. What are the tradeoffs between the two?  Section 2.2 says they do the latter in the interest of solving control-related tasks, but I’m not clear why this follows.   Is there a reason SVAEs don’t meet all the desiderata mentioned at the end of the Introduction?  Since the SVAE code is publicly available, one could probably compare against it in the experiments.   I’m a bit confused about the role of uncertainty about v. In principle, one could estimate the transition parameters by maximum likelihood (i.e. fitting a point estimate of v), but this isn’t what’s done. Instead, v is integrated out as part of the marginal likelihood, which I interpret as giving the flexibility to model different dynamics for different sequences. But if this is the case, then shouldn’t the q distribution for v depend on the data, rather than being data-independent as in Eqn. (9)?

A:
en
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