ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2007.06007
  4. Cited By
Universal Approximation Power of Deep Residual Neural Networks via
  Nonlinear Control Theory
v1v2v3v4 (latest)

Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory

International Conference on Learning Representations (ICLR), 2020
12 July 2020
Paulo Tabuada
Bahman Gharesifard
ArXiv (abs)PDFHTML

Papers citing "Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory"

10 / 10 papers shown
The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communications
The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communications
Alex Acero
Daniel M. Jimenez-Gutierrez
Dario Pighin
Enrique Zuazua
Joaquin Del Rio
Xabi Uribe-Etxebarria
FedML
176
1
0
19 Oct 2025
Universal approximation property of ODENet and ResNet with a single
  activation function
Universal approximation property of ODENet and ResNet with a single activation functionJournal of Computational Mathematics and Data Science (JCMDS), 2024
M. Kimura
Kazunori Matsui
Yosuke Mizuno
205
0
0
22 Oct 2024
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of
  Experts
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
Anastasis Kratsios
Haitz Sáez de Ocáriz Borde
Takashi Furuya
Marc T. Law
MoE
566
2
0
05 Feb 2024
Vocabulary for Universal Approximation: A Linguistic Perspective of
  Mapping Compositions
Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping CompositionsInternational Conference on Machine Learning (ICML), 2023
Yongqiang Cai
CoGe
241
11
0
20 May 2023
Learning on Manifolds: Universal Approximations Properties using
  Geometric Controllability Conditions for Neural ODEs
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEsConference on Learning for Dynamics & Control (L4DC), 2023
Karthik Elamvazhuthi
Xuechen Zhang
Samet Oymak
Fabio Pasqualetti
AI4CE
187
7
0
15 May 2023
Universal Approximation Property of Hamiltonian Deep Neural Networks
Universal Approximation Property of Hamiltonian Deep Neural NetworksIEEE Control Systems Letters (L-CSS), 2023
M. Zakwan
M. d’Angelo
Giancarlo Ferrari-Trecate
311
8
0
21 Mar 2023
Learning Robust State Observers using Neural ODEs (longer version)
Learning Robust State Observers using Neural ODEs (longer version)Conference on Learning for Dynamics & Control (L4DC), 2022
Keyan Miao
Konstantinos Gatsis
OOD
250
21
0
01 Dec 2022
Achieve the Minimum Width of Neural Networks for Universal Approximation
Achieve the Minimum Width of Neural Networks for Universal ApproximationInternational Conference on Learning Representations (ICLR), 2022
Yongqiang Cai
240
27
0
23 Sep 2022
Neural ODE Control for Trajectory Approximation of Continuity Equation
Neural ODE Control for Trajectory Approximation of Continuity EquationIEEE Control Systems Letters (L-CSS), 2022
Karthik Elamvazhuthi
Bahman Gharesifard
Andrea L. Bertozzi
Stanley Osher
173
15
0
18 May 2022
Large-time asymptotics in deep learning
Large-time asymptotics in deep learning
Carlos Esteve
Borjan Geshkovski
Dario Pighin
Enrique Zuazua
672
39
0
06 Aug 2020
1
Page 1 of 1