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We give algorithms with provable guarantees that learn a class of deep nets in the generative model view popularized by Hinton and others.
The Organization of Behavior: A Neuropsychological Theory
Donald O. Hebb · 1949
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Graph reconstructiona survey
J Adrian Bondy and Robert L Hemminger · 1977
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Learnability beyond a c 0 ac^{0}
Jeffrey C Jackson, Adam R Klivans, and Rocco A Servedio · 2002
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Compressed sensing
David L Donoho · 2006
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Combining geometry and combinatorics: a unified approach to sparse signal recovery
R. Berinde, A.C. Gilbert, P. Indyk, H. Karloff, and M.J. Strauss · 2008
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Learning deep architectures for AI
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton · 2012
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New algorithms for learning incoherent and overcomplete dictionaries
Sanjeev Arora, Rong Ge, and Ankur Moitra · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron C. Courville, and Pascal Vincent · 2013
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Learning mixtures of spherical gaussians: moment methods and spectral decompositions
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A provably efficient algorithm for training deep networks
Roi Livni, Shai Shalev-Shwartz, and Ohad Shamir · 2013
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