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We establish a margin based data dependent generalization error bound for a general family of deep neural networks in terms of the depth and width, as well as the Jacobian of the networks.
Approximation by superpositions of a sigmoidal function
G. Cybenko · 1989
Earlier work this paper cites.
On the approximate realization of continuous mappings by neural networks
K.-I. Funahashi · 1989
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
K. Hornik, M. Stinchcombe, and H. White · 1989
Earlier work this paper cites.
Universal approximation bounds for superpositions of a sigmoidal function
A. R. Barron · 1993
Earlier work this paper cites.
Approximation and estimation bounds for artificial neural networks
A. R. Barron · 1994
Earlier work this paper cites.
An Introduction to Computational Learning Theory
M. J. Kearns and U. V. Vazirani · 1994
Earlier work this paper cites.
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
P. L. Bartlett · 1998
Earlier work this paper cites.
Neural Network Learning: Theoretical Foundations
M. Anthony and P. L. Bartlett · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
Natural language processing (almost) from scratch
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa · 2011
Earlier work this paper cites.
Circulant Matrices
P. J. Davis · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
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M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2012
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Cited alongside, same era.
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Norm-based capacity control in neural networks
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Approximating continuous functions by relu nets of minimal width
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Later among the works it cites.
L. Huang, X. Liu, B. Lang, A. W. Yu, Y. Wang, and B. Li · 2017
Later among the works it cites.
On the ability of neural nets to express distributions
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Later among the works it cites.
Sphereface: Deep hypersphere embedding for face recognition
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song · 2017
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A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
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Sharp minima can generalize for deep nets
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Optimal approximation of piecewise smooth functions using deep relu neural networks
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On the complexity of learning neural networks
L. Song, S. Vempala, J. Wilmes, and B. Xie · 2017
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