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Deep learning, in the form of artificial neural networks, has achieved remarkable practical success in recent years, for a variety of difficult machine learning applications.
An implicit function theorem: Comment
Sadatoshi Kumagai · 1980
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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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Training a 3-node neural network is np-complete
Avrim L Blum and Ronald L Rivest · 1992
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Exponentially many local minima for single neurons
Peter Auer, Mark Herbster, and Manfred K Warmuth · 1996
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Convex neural networks
Yoshua Bengio, Nicolas L Roux, Pascal Vincent, Olivier Delalleau, and Patrice Marcotte · 2005
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Distributing points on the sphere: partitions, separation, quadrature and energy
Paul Leopardi · 2007
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Concise formulas for the area and volume of a hyperspherical cap
Shengqiao Li · 2011
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Learning polynomials with neural networks
Alexandr Andoni, Rina Panigrahy, Gregory Valiant, and Li Zhang · 2014
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Provable bounds for learning some deep representations
Sanjeev Arora, Aditya Bhaskara, Rong Ge, and Tengyu Ma · 2014
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Breaking the curse of dimensionality with convex neural networks
Francis Bach · 2014
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The loss surface of multilayer networks
Anna Choromanska, Mikael Henaff, Michael Mathieu, Gérard Ben Arous, and Yann LeCun · 2014
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Qualitatively characterizing neural network optimization problems
Ian J Goodfellow and Oriol Vinyals · 2014
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On the computational efficiency of training neural networks
Roi Livni, Shai Shalev-Shwartz, and Ohad Shamir · 2014
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Global optimality in tensor factorization, deep learning, and beyond
Benjamin D Haeffele and René Vidal · 2015
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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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Learning halfspaces and neural networks with random initialization
Yuchen Zhang, Jason D Lee, Martin J Wainwright, and Michael I Jordan · 2015
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Y. Dauphin, R. Pascanu, C. Gulcehre, K. Cho, S. Ganguli, and Y. Bengio · 2014
Cited alongside, same era.
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