Do Deep Nets Really Need To Be Deep
Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. We show that by using a method called model compression that shallow feedforward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow neural nets can learn these deep functions using the same number of parameters as the original deep models. On the TIMIT phoneme recognition
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