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We thank all the reviewers for their helpful and positive feedback. We are happy that all the reviewers acknowledge the contributions of this work, in particular, a novel loss function that addresses a relevant practical problem, an efficient optimization scheme that scales to large datasets, and an experimental evaluation that shows encouraging results. Reviewer 2: - motivate the choice of the loss function further. We provide two possible directions to extend the motivation. First, the top-k error is used as the performance measure in certain benchmarks, e.g. in the ImageNet challenge in computer vision.