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In this paper we propose a generalization of deep neural networks called deep function machines (DFMs).
A logical calculus of the ideas immanent in nervous activity
Warren S McCulloch and Walter Pitts · 1943
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Approximation by superpositions of a sigmoidal function
G. Cybenko · 1989
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Bayesian learning for neural networks , volume 118
Radford M Neal · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Computation with infinite neural networks
Christopher KI Williams · 1998
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Neural network approximation of continuous functionals and continuous functions on compactifications
Maxwell B Stinchcombe · 1999
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A survey of machine learning
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Deep learning in neural networks: An overview
Jurgen Schmidhuber · 2015
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Amit Daniely, Roy Frostig, and Yoram Singer · 2016
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Learning infinite-layer networks: beyond the kernel trick
Amir Globerson and Roi Livni · 2016
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Exponential expressivity in deep neural networks through transient chaos
Ben Poole, Subhaneil Lahiri, Maithreyi Raghu, Jascha Sohl-Dickstein, and Surya Ganguli · 2016
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On the expressive power of deep neural networks
Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein · 2016
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