Fetching the paper…
Reading the bibliography…
This paper studies the approximation capacity of ReLU neural networks with norm constraint on the weights.
ϵ \epsilon -entropy and ϵ \epsilon -capacity of sets in functional spaces
Andrey N. Kolmogorov and Vladimir M. Tikhomirov · 1961
Earlier work this paper cites.
On the uniform convergence of relative frequencies of events to their probabilities
Vladimir N. Vapnik and Alexey Ya. Chervonenkis · 1971
Earlier work this paper cites.
The best constants in the Khintchine inequality
Uffe Haagerup · 1981
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
Earlier work this paper cites.
Probability in Banach spaces: isoperimetry and processes
Michel Ledoux and Michel Talagrand · 1991
Earlier work this paper cites.
Universal approximation bounds for superpositions of a sigmoidal function
Andrew R. Barron · 1993
Earlier work this paper cites.
Integral probability metrics and their generating classes of functions
Alfred Müller · 1997
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
Peter L. Bartlett · 1998
Earlier work this paper cites.
On the degree of approximation by manifolds of finite pseudo-dimension
Vitaly Maiorov and Joel Ratsaby · 1999
Earlier work this paper cites.
Approximation theory of the MLP model in neural networks
Allan Pinkus · 1999
Earlier work this paper cites.
Rademacher and Gaussian complexities: Risk bounds and structural results
Peter L. Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Cédric Villani · 2008
Earlier work this paper cites.
Neural network learning: Theoretical foundations
Martin Anthony and Peter L. Bartlett · 2009
Earlier work this paper cites.
Rectified linear units improve restricted Boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
Earlier work this paper cites.
Concentration inequalities: A nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Earlier work this paper cites.
Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
Cited alongside, same era.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Spectrally-normalized margin bounds for neural networks
Peter L. Bartlett, Dylan J. Foster, and Matus Telgarsky · 2017
Cited alongside, same era.
Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
Cited alongside, same era.
Improved training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, and Aaron C. Courville · 2017
Cited alongside, same era.
Error bounds for approximations with deep ReLU networks
Dmitry Yarotsky · 2017
Cited alongside, same era.
Optimal approximation with sparsely connected deep neural networks
Helmut Bölcskei, Philipp Grohs, Gitta Kutyniok, and Philipp Petersen · 2019
Later among the works it cites.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Later among the works it cites.
Gradient descent finds global minima of deep neural networks
Simon Du, Jason Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2019
Later among the works it cites.
Limitations of the lipschitz constant as a defense against adversarial examples
Todd Huster, Cho-Yu Jason Chiang, and Ritu Chadha · 2019
Later among the works it cites.
Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2020
Later among the works it cites.
Adaptive approximation and generalization of deep neural network with intrinsic dimensionality
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Cited alongside, same era.
Sobolev GAN
Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, and Yu Cheng · 2018
Cited alongside, same era.
A PAC-Bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro · 2018
Cited alongside, same era.
Optimal approximation of piecewise smooth functions using deep ReLU neural networks
Philipp Petersen and Felix Voigtlaender · 2018
Cited alongside, same era.
On the regularization of Wasserstein GANs
Henning Petzka, Asja Fischer, and Denis Lukovnikov · 2018
Cited alongside, same era.
Ryumei Nakada and Masaaki Imaizumi · 2020
Later among the works it cites.
Constructive universal high-dimensional distribution generation through deep ReLU networks
Dmytro Perekrestenko, Stephan Müller, and Helmut Bölcskei · 2020
Later among the works it cites.
Nonparametric regression using deep neural networks with ReLU activation function
Johannes Schmidt-Hieber · 2020
Later among the works it cites.
Deep network approximation characterized by number of neurons
Zuowei Shen, Haizhao Yang, and Shijun Zhang · 2020
Later among the works it cites.
The phase diagram of approximation rates for deep neural networks
Dmitry Yarotsky and Anton Zhevnerchuk · 2020
Later among the works it cites.
How well generative adversarial networks learn distributions
Tengyuan Liang · 2021
Later among the works it cites.
Deep network approximation for smooth functions
Jianfeng Lu, Zuowei Shen, Haizhao Yang, and Shijun Zhang · 2021
Later among the works it cites.
Deep ReLU networks overcome the curse of dimensionality for bandlimited functions
Hadrien Montanelli, Haizhao Yang, and Qiang Du · 2021
Later among the works it cites.
On the proof of global convergence of gradient descent for deep ReLU networks with linear widths
Quynh Nguyen · 2021
Later among the works it cites.
High-dimensional distribution generation through deep neural networks
Dmytro Perekrestenko, Léandre Eberhard, and Helmut Bölcskei · 2021
Later among the works it cites.
The Kolmogorov-Arnold representation theorem revisited
Johannes Schmidt-Hieber · 2021
Later among the works it cites.
An error analysis of generative adversarial networks for learning distributions
Jian Huang, Yuling Jiao, Zhen Li, Shiao Liu, Yang Wang, and Yunfei Yang · 2022
Closest in time.
Loss landscapes and optimization in over-parameterized non-linear systems and neural networks
Chaoyue Liu, Libin Zhu, and Mikhail Belkin · 2022
Closest in time.