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We provide a series of results for unsupervised learning with autoencoders.
Training a 3-node neural network is np-complete
Avrim Blum and Ronald L Rivest · 1989
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Sparse coding with an overcomplete basis set: A strategy employed by v1?
Bruno A Olshausen and David J Field · 1997
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Learning mixtures of separated nonspherical gaussians
Sanjeev Arora and Ravi Kannan · 2005
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Settling the polynomial learnability of mixtures of gaussians
Ankur Moitra and Gregory Valiant · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
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Exact recovery of sparsely-used dictionaries
Daniel A Spielman, Huan Wang, and John Wright · 2012
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Provable ica with unknown gaussian noise, with implications for gaussian mixtures and autoencoders
Sanjeev Arora, Rong Ge, Ankur Moitra, and Sushant Sachdeva · 2012
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Exact recovery of sparsely used overcomplete dictionaries
Alekh Agarwal, Animashree Anandkumar, and Praneeth Netrapalli · 2013
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Learning sparsely used overcomplete dictionaries
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, and Rashish Tandon · 2014
Cited alongside, same era.
Fourier pca and robust tensor decomposition
Navin Goyal, Santosh Vempala, and Ying Xiao · 2014
Cited alongside, same era.
Sample complexity of dictionary learning and other matrix factorizations
Rémi Gribonval, Rodolphe Jenatton, Francis Bach, Martin Kleinsteuber, and Matthias Seibert · 2015
Cited alongside, same era.
Why regularized auto-encoders learn sparse representation?
Devansh Arpit, Yingbo Zhou, Hung Ngo, and Venu Govindaraju · 2015
Cited alongside, same era.
Zero-bias autoencoders and the benefits of co-adapting features
Kishore Konda, Roland Memisevic, and David Krueger · 2015
Cited alongside, same era.
Learning one-hidden-layer neural networks with landscape design
Rong Ge, Jason D Lee, and Tengyu Ma · 2017
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Globally optimal gradient descent for a convnet with gaussian inputs
Alon Brutzkus and Amir Globerson · 2017
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Recovery guarantees for one-hidden-layer neural networks
Kai Zhong, Zhao Song, Prateek Jain, Peter L Bartlett, and Inderjit S Dhillon · 2017
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Convergence analysis of two-layer neural networks with relu activation
Yuanzhi Li and Yang Yuan · 2017
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Identity matters in deep learning
Moritz Hardt and Tengyu Ma · 2017
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Deep learning without poor local minima
Kenji Kawaguchi · 2016
Cited alongside, same era.
Symmetry-breaking convergence analysis of certain two-layered neural networks with relu nonlinearity
Yuandong Tian · 2017
Cited alongside, same era.
Provable bounds for learning some deep representations
Sanjeev Arora, Aditya Bhaskara, Rong Ge, and Tengyu Ma
Cited in the paper.
Why are deep nets reversible: A simple theory, with implications for training
Sanjeev Arora, Yingyu Liang, and Tengyu Ma
Cited in the paper.
Simple, efficient, and neural algorithms for sparse coding
Sanjeev Arora, Rong Ge, Tengyu Ma, and Ankur Moitra
Cited in the paper.
New algorithms for learning incoherent and overcomplete dictionaries
Sanjeev Arora, Rong Ge, and Ankur Moitra
Cited in the paper.
Akshay Rangamani, Anirbit Mukherjee, Ashish Arora, Tejaswini Ganapathy, Amitabh Basu, Sang Chin, and Trac D Tran · 2017
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A provable approach for double-sparse coding
Thanh V Nguyen, Raymond K W Wong, and Chinmay Hegde · 2018
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