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Approximation theory for 1-Lipschitz ResNets

Approximation theory for 1-Lipschitz ResNets

17 May 2025
Davide Murari
Takashi Furuya
Carola-Bibiane Schönlieb
ArXiv (abs)PDFHTML

Papers citing "Approximation theory for 1-Lipschitz ResNets"

23 / 23 papers shown
Title
Approximation properties of neural ODEs
Approximation properties of neural ODEs
Arturo De Marinis
Davide Murari
E. Celledoni
Nicola Guglielmi
B. Owren
Francesco Tudisco
76
1
0
19 Mar 2025
1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness
1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness
Bernd Prach
Fabio Brau
Giorgio Buttazzo
Christoph H. Lampert
76
8
0
28 Nov 2023
Designing Stable Neural Networks using Convex Analysis and ODEs
Designing Stable Neural Networks using Convex Analysis and ODEs
Ferdia Sherry
E. Celledoni
Matthias Joachim Ehrhardt
Davide Murari
B. Owren
Carola-Bibiane Schönlieb
91
12
0
29 Jun 2023
Dynamical systems' based neural networks
Dynamical systems' based neural networks
E. Celledoni
Davide Murari
B. Owren
Carola-Bibiane Schönlieb
Ferdia Sherry
OOD
110
11
0
05 Oct 2022
Neural Conservation Laws: A Divergence-Free Perspective
Neural Conservation Laws: A Divergence-Free Perspective
Jack Richter-Powell
Y. Lipman
Ricky T. Q. Chen
105
56
0
04 Oct 2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks
Approximation of Lipschitz Functions using Deep Spline Neural Networks
Sebastian Neumayer
Alexis Goujon
Pakshal Bohra
M. Unser
62
17
0
13 Apr 2022
A Dynamical System Perspective for Lipschitz Neural Networks
A Dynamical System Perspective for Lipschitz Neural Networks
Laurent Meunier
Blaise Delattre
Alexandre Araujo
A. Allauzen
109
56
0
25 Oct 2021
Orthogonalizing Convolutional Layers with the Cayley Transform
Orthogonalizing Convolutional Layers with the Cayley Transform
Asher Trockman
J. Zico Kolter
80
115
0
14 Apr 2021
Lipschitz Normalization for Self-Attention Layers with Application to
  Graph Neural Networks
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks
George Dasoulas
Kevin Scaman
Aladin Virmaux
GNN
74
39
0
08 Mar 2021
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
J. Hertrich
Sebastian Neumayer
Gabriele Steidl
61
60
0
04 Nov 2020
The Lipschitz Constant of Self-Attention
The Lipschitz Constant of Self-Attention
Hyunjik Kim
George Papamakarios
A. Mnih
86
146
0
08 Jun 2020
Orthogonal Convolutional Neural Networks
Orthogonal Convolutional Neural Networks
Jiayun Wang
Yubei Chen
Rudrasis Chakraborty
Stella X. Yu
85
190
0
27 Nov 2019
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Ernest K. Ryu
Jialin Liu
Sicheng Wang
Xiaohan Chen
Zhangyang Wang
W. Yin
AI4CE
76
354
0
14 May 2019
Deep Theory of Functional Connections: A New Method for Estimating the
  Solutions of PDEs
Deep Theory of Functional Connections: A New Method for Estimating the Solutions of PDEs
Carl Leake
59
69
0
20 Dec 2018
Sorting out Lipschitz function approximation
Sorting out Lipschitz function approximation
Cem Anil
James Lucas
Roger C. Grosse
90
325
0
13 Nov 2018
Adversarial Regularizers in Inverse Problems
Adversarial Regularizers in Inverse Problems
Sebastian Lunz
Ozan Oktem
Carola-Bibiane Schönlieb
GANMedIm
107
221
0
29 May 2018
Spectral Normalization for Generative Adversarial Networks
Spectral Normalization for Generative Adversarial Networks
Takeru Miyato
Toshiki Kataoka
Masanori Koyama
Yuichi Yoshida
ODL
164
4,445
0
16 Feb 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation
  Invariance for Deep Neural Networks
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Yusuke Tsuzuku
Issei Sato
Masashi Sugiyama
AAML
105
309
0
12 Feb 2018
Optimal approximation of continuous functions by very deep ReLU networks
Optimal approximation of continuous functions by very deep ReLU networks
Dmitry Yarotsky
201
294
0
10 Feb 2018
Spectral Norm Regularization for Improving the Generalizability of Deep
  Learning
Spectral Norm Regularization for Improving the Generalizability of Deep Learning
Yuichi Yoshida
Takeru Miyato
83
335
0
31 May 2017
Error bounds for approximations with deep ReLU networks
Error bounds for approximations with deep ReLU networks
Dmitry Yarotsky
198
1,236
0
03 Oct 2016
Norm-preserving Orthogonal Permutation Linear Unit Activation Functions
  (OPLU)
Norm-preserving Orthogonal Permutation Linear Unit Activation Functions (OPLU)
Artem Chernodub
D. Nowicki
56
28
0
08 Apr 2016
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
289
14,968
1
21 Dec 2013
1