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"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","1380" "Title:","Do Deep Nets Really Need to be Deep?" First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors show empirical results on TIMIT and CIFAR-10 that shallow nets trained to mimic the outputs of DNNs and CNNs achieve comparable accuracy on these tasks. The paper is clearly written and makes compelling arguments. The contribution is significant because it suggests that SNNs are capable of learning complex functions that were thought to be learnable only with DNNs or CNNs. This means that better training algorithms have yet to be devised for SNNs.