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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates\054 Inc\056)
/Language (en\055US)
/Created (2014)
/EventType (Poster)
/Description-Abstract (Currently\054 deep neural networks are the state of the art on problems such as speech recognition and computer vision\056 In this paper we empirically demonstrate that shallow feed\055forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models\056 Moreover\054 in some cases the shallow nets can learn these deep functions using the same number of parameters as the original deep models\056 On the TIMIT phoneme recognition and CIFAR\05510 image recognition tasks\054 shallow nets can be trained that perform similarly to complex\054 well\055engineered\054 deeper convolutional models\056)
/Producer (PyPDF2)
/Title (Do Deep Nets Really Need to be Deep\077)
/Date (2014)
/ModDate (D\07220141202174403\05508\04700\047)
/Published (2014)
/Type (Conference Proceedings)
/firstpage (2654)
/Book (Advances in Neural Information Processing Systems 27)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (Z\056 Ghahramani and M\056 Welling and C\056 Cortes and N\056D\056 Lawrence and K\056Q\056 Weinberger)
/Author (Jimmy Ba\054 Rich Caruana)
/lastpage (2662)
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