ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2402.00761
  4. Cited By
Control-Theoretic Techniques for Online Adaptation of Deep Neural
  Networks in Dynamical Systems

Control-Theoretic Techniques for Online Adaptation of Deep Neural Networks in Dynamical Systems

1 February 2024
Jacob G. Elkins
F. Fahimi
    AI4CE
ArXivPDFHTML

Papers citing "Control-Theoretic Techniques for Online Adaptation of Deep Neural Networks in Dynamical Systems"

3 / 3 papers shown
Title
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Karol Arndt
Murtaza Hazara
Ali Ghadirzadeh
Ville Kyrki
104
104
0
16 Sep 2019
Transferring End-to-End Visuomotor Control from Simulation to Real World
  for a Multi-Stage Task
Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task
Stephen James
Andrew J. Davison
Edward Johns
162
275
0
07 Jul 2017
Domain Adaptation: Learning Bounds and Algorithms
Domain Adaptation: Learning Bounds and Algorithms
Yishay Mansour
M. Mohri
Afshin Rostamizadeh
179
786
0
19 Feb 2009
1