Learn to synchronize, synchronize to learn-Reference-Cited by-同舟云学术

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Learn to synchronize, synchronize to learn

Published:2021-08 Issue:8 Volume:31 Page:083119
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Verzelli Pietro¹^ORCID,Alippi Cesare¹²^ORCID,Livi Lorenzo³⁴^ORCID

Affiliation:

1. Faculty of Informatics, Università della Svizzera Italiana, Lugano 69000, Switzerland

2. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy

3. Department of Computer Science and Mathematics, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada

4. Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, United Kingdom

Funder

Canada Research Chairs

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0056425

Reference71 articles.

1. G.H. Liu and E. A. Theodorou, “Deep learning theory review: An optimal control and dynamical systems perspective,” arXiv:1908.10920 (2019).

2. Investigating Echo-State Networks Dynamics by Means of Recurrence Analysis

3. Generating Coherent Patterns of Activity from Chaotic Neural Networks

4. Learning long-term dependencies with gradient descent is difficult

5. Kernel Methods for the Approximation of Nonlinear Systems

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3. Study of Delayed Recurrent Neural Networks for Learning of Chaotic Dynamical Systems;2024

4. Reconstructing computational system dynamics from neural data with recurrent neural networks;Nature Reviews Neuroscience;2023-10-04

5. Physics-informed hierarchical echo state network for predicting the dynamics of chaotic systems;Expert Systems with Applications;2023-10

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