text: Summary The authors argue that ensemble prediction takes too much computation time and resource, especially in the case of deep neural networks.  They then address the problem by proposing an adaptive prediction approach.  The approach is based on the observation that it is most important for ensemble approaches to focus on the "uncertain" examples.  The proposed approach thus conducts early-stopping prediction when the confidence (certainty) of the prediction is high enough, where the confidence is based on the confidence intervals of (multi-class) labels based on the student-t distribution.  Experiments on vision datasets demonstrate that the proposed approach is effective in reducing computation resources while maintaining sufficient accuracy. Comments * The experiments are limited in the scope of (image) multi-class classification.  It is not clear whether the proposed approach is effective for other classification tasks, or even more sophisticated tasks like multi-label classification or sequence tagging. * The idea appears elegant but rather straightforward.  One important baseline that is easy but not discussed is to set a static threshold on pairwise comparison (p_max - p_secondmax).  Would this baseline be competitive with the proposed approach?  Such a comparison is able to demonstrate the benefits of using confidence interval. * The overall improvement in computation time seems to be within a constant scale,  which can be easily achieved by doing ensemble prediction in parallel  (note that the proposed approach would require predicting sequentially).  So are there real applications that can benefit from the improvement? * typo: p4, line19, neural "netowkrs" -> neural "networks"