instruction:
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].
question:
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)?
answer:
en


question:
Summary: In this paper, the authors study ResNets through a theoretical formulation of a spin glass model. The conclusions are that ResNets behave as an ensemble of shallow networks at the start of training (by examining the magnitude of the weights for paths of a specific length) but this changes through training, through which the scaling parameter C (from assumption A4) increases, causing it to behave as an ensemble of deeper and deeper networks.  Clarity: This paper was somewhat difficult to follow, being heavy in notation, with perhaps some notation overloading. A summary of some of the proofs in the main text might have been helpful.  Specific Comments: - In the proof of Lemma 2, I'm not sure where the sequence beta comes from (I don't see how it follows from 11?)  - The ResNet structure used in the paper is somewhat different from normal with multiple layers being skipped? (Can the same analysis be used if only one layer is skipped? It seems like the skipping mostly affects the number of paths there are of a certain length?)  - The new experiments supporting the scale increase in practice are interesting! I'm not sure about Theorems 3, 4 necessarily proving this link theoretically however, particularly given the simplifying assumption at the start of Section 4.2?
answer:
en


question:
El artículo describe un caso de innovación empresarial que apoya los procesos de aprendizaje organizacional. El principal mérito del caso es que se ha logrado un impacto muy significativo con una inversión modesta de recursos. Adicionalmente se enfatiza al conocimiento como un elemento estratégico para la organización, el cual requiere acciones concretas de gestión y asignación de recursos. Finalmente, se debe destacar una redacción impecable y amena del caso.
answer:

es


