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Deep neural nets have caused a revolution in many classification tasks.
“On a space of completely additive functions”
Leonid Kantorovich and G Rubinstein · 1958
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“Approximation by superpositions of a sigmoidal function”
George Cybenko · 1989
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“On the approximate realization of continuous mappings by neural networks”
Ken-Ichi Funahashi · 1989
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“Multilayer feedforward networks are universal approximators”
Kurt Hornik, Maxwell Stinchcombe and Halbert White · 1989
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“Universal approximation bounds for superpositions of a sigmoidal function”
Andrew. Barron · 1993
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“Approximation and estimation bounds for artificial neural networks”
Andrew. Barron · 1994
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Michael Kearns et al · 1994
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“Detecting change in data streams”
Daniel Kifer, Shai Ben-David and Johannes Gehrke · 2004
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Diederik Kingma and Max Welling · 2013
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“Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods”
Majid Janzamin, Hanie Sedghi and Anima Anandkumar · 2015
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“Super-linear gate and super-quadratic wire lower bounds for depth-two and depth-three threshold circuits”
Daniel Kane and Ryan Williams · 2016
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“Benefits of depth in neural networks”
Matus Telgarsky · 2016
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“Towards principled methods for training generative adversarial networks”
Martin Arjovsky and Léon Bottou · 2017
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J. Schmidhuber · 2014
Cited alongside, same era.
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Nadav Cohen, Or Sharir and Amnon Shashua · 2015
Cited alongside, same era.
“Training generative neural networks via maximum mean discrepancy optimization”
Gintare Dziugaite, Daniel Roy and Zoubin Ghahramani · 2015
Cited alongside, same era.
Martin Arjovsky, Soumith Chintala and Léon Bottou · 2017
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“Depth Separation for Neural Networks”
Amit Daniely · 2017
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