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We prove bounds on the generalization error of convolutional networks.
Data-dependent sample complexity of deep neural networks via lipschitz augmentation
Colin Wei and Tengyu Ma · 1905
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ε \varepsilon -entropy and ε \varepsilon -capacity of sets in function spaces
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On the uniform convergence of relative frequencies of events to their probabilities
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The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
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Empirical Processes in M-estimation , volume 6
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On consistency of kernel density estimators for randomly censored data: rates holding uniformly over adaptive intervals
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Efficient backprop
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2012
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Dudley’s entropy integral, 2013
P. L. Bartlett · 2013
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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On tighter generalization bound for deep neural networks: Cnns, resnets, and beyond
Xingguo Li, Junwei Lu, Zhaoran Wang, Jarvis Haupt, and Tuo Zhao · 2018
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Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval and matrix completion
Cong Ma, Kaizheng Wang, Yuejie Chi, and Yuxin Chen · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro · 2018
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The singular values of convolutional layers
Hanie Sedghi, Vineet Gupta, and Philip M Long · 2018
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Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
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Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
Gintare Karolina Dziugaite and Daniel M Roy · 2017
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Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2017
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Implicit regularization in matrix factorization
Suriya Gunasekar, Blake E Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nati Srebro · 2017
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
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Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
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Understanding generalization and optimization performance of deep cnns
Pan Zhou and Jiashi Feng · 2018
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Stochastic gradient descent optimizes over-parameterized deep relu networks
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 2018
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A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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Generalization bounds of stochastic gradient descent for wide and deep neural networks
Yuan Cao and Quanquan Gu · 2019
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Generalization bounds for neural networks via approximate description length
Amit Daniely and Elad Granot · 2019
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Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2019
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Norm-based generalisation bounds for multi-class convolutional neural networks
Antoine Ledent, Yunwen Lei, and Marius Kloft · 2019
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Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
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Generalization in deep networks: The role of distance from initialization
Vaishnavh Nagarajan and J Zico Kolter · 2019
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Towards understanding the role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan Srebro · 2019
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