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The driving force behind convolutional networks - the most successful deep learning architecture to date, is their expressive power.
Principles of neurodynamics. perceptrons and the theory of brain mechanisms
Frank Rosenblatt · 1961
Earlier work this paper cites.
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Rectified linear units improve restricted boltzmann machines
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Tensor Spaces and Numerical Tensor Calculus , volume 42 of
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Shawn X Cui, Michael H Freedman, Or Sattath, Richard Stong, and Greg Minton · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 2016
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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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