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1906.09477
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The phase diagram of approximation rates for deep neural networks
Neural Information Processing Systems (NeurIPS), 2019
22 June 2019
Dmitry Yarotsky
Anton Zhevnerchuk
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Papers citing
"The phase diagram of approximation rates for deep neural networks"
50 / 87 papers shown
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Jonathan W. Siegel
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Thomas Y. Hou
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Mixture of Experts Softens the Curse of Dimensionality in Operator Learning
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Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
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Marc T. Law
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Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
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Deep ReLU networks and high-order finite element methods II: Chebyshev emulation
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Christoph Schwab
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Deep Ridgelet Transform: Voice with Koopman Operator Proves Universality of Formal Deep Networks
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Yuka Hashimoto
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Distribution learning via neural differential equations: a nonparametric statistical perspective
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Zhi Ren
Sven Wang
Jakob Zech
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Optimal Approximation and Learning Rates for Deep Convolutional Neural Networks
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Deep Operator Network Approximation Rates for Lipschitz Operators
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Ch. Schwab
A. Stein
Jakob Zech
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19 Jul 2023
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Jianfeng Lu
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Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces
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Instance-Dependent Generalization Bounds via Optimal Transport
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Songyan Hou
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Approximation results for Gradient Descent trained Shallow Neural Networks in
1
d
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Consistency of Neural Networks with Regularization
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Finite Expression Method for Solving High-Dimensional Partial Differential Equations
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497
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Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations
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Denis Belomestny
A. Naumov
Nikita Puchkin
S. Samsonov
198
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20 Jun 2022
A general approximation lower bound in
L
p
L^p
L
p
norm, with applications to feed-forward neural networks
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Armand Foucault
Sébastien Gerchinovitz
Franccois Malgouyres
327
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Neural Network Architecture Beyond Width and Depth
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