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:
Este trabajo ofrece un análisis comparativo del uso de tecnología cableada y tecnología programada.  El ámbito de "trabajo relacionado" presentado en el documento es inexistente. En este contexto, no es posible determinar si los criterios de comparación sugeridos en el trabajo son, efectivamente, los pertinentes para realizar una evaluación.  Los criterios de comparación de tecnologías son en algunos casos objetivos y en otros casos subjetivos. En este sentido, el trabajo no sugiere cómo enfrentar la valoración de cada criterio para finalmente efectuar una comparación (por ejemplo, en el ítem "seguridad" o en el ítem "programación").  Existen problemas gramaticales (por ejemplo, "alto tiempo). Adicionalmente, el trabajo está pobremente ordenado, dificultando la comprensión por el bajo esfuerzo en localizar el contenido de las tablas de una manera legible.  No se visualiza un aporte real de este trabajo. En numerosas oportunidades se cita a "Rexroth Bosch Group", sin agregar mucho más en cada punto. El impacto y relevancia  de este trabajo, en cuanto a la novedad, no es observable.
answer:
es


question:
It is an interesting idea to go after saddle points in the optimization with an SR1 update and a good start in experiments, but missing important comparisons to recent 2nd order optimizations such as Adam, other Hessian free methods (Martens 2012), Pearlmutter fast exact multiplication by the Hessian. From the mnist/cifar curves it is not really showing an advantage to AdaDelta/Nag (although this is stated), and much more experimentation is needed to make a claim about mini-batch insensitivity to performance, can you show error rates on a larger scale task?
answer:
en


question:
As you noted for Figure 5 Left, sometimes it seems sufficient to tune learning rates. I see your argument for Figure 6 Right,  but  1) not for all good learning rates make Adam fail, I guess you selected the one where it did (note that Adam was several times faster than Eve in the beginning) 2) I don't buy "Eve always converges" because you show it only for 0.1 and since Eve is not Adam, 0.1 of Adam is not 0.1 of Eve because of d_t.   To my understanding, you define d_t over time with 3 hyperparameters. Similarly, one can define d_t directly. The behaviour of d_t that you show is not extraordinary and can be parameterized. If Eve is better than Adam, then looking at d_t we can directly see whether we underestimated or overestimated learning rates. You could argue that Eve does it automatically but you do tune learning rates for each problem individually anyway.
answer:

en