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Memory and forecasting capacities of nonlinear recurrent networks
v1v2 (latest)

Memory and forecasting capacities of nonlinear recurrent networks

22 April 2020
Lukas Gonon
Lyudmila Grigoryeva
Juan-Pablo Ortega
    AI4TS
ArXiv (abs)PDFHTML

Papers citing "Memory and forecasting capacities of nonlinear recurrent networks"

8 / 8 papers shown
Memory Determines Learning Direction: A Theory of Gradient-Based Optimization in State Space Models
Memory Determines Learning Direction: A Theory of Gradient-Based Optimization in State Space Models
JingChuan Guan
T. Kubota
Yasuo Kuniyoshi
Kohei Nakajima
111
0
0
01 Oct 2025
Memory of recurrent networks: Do we compute it right?
Memory of recurrent networks: Do we compute it right?Journal of machine learning research (JMLR), 2023
Giovanni Ballarin
Lyudmila Grigoryeva
Juan-Pablo Ortega
345
6
0
02 May 2023
Empirical Analysis of Limits for Memory Distance in Recurrent Neural
  Networks
Empirical Analysis of Limits for Memory Distance in Recurrent Neural NetworksInternational Conference on Agents and Artificial Intelligence (ICAART), 2022
Steffen Illium
Thore Schillman
Robert Muller
Thomas Gabor
Claudia Linnhoff-Popien
295
2
0
20 Dec 2022
Interpretable Design of Reservoir Computing Networks using Realization
  Theory
Interpretable Design of Reservoir Computing Networks using Realization Theory
Wei Miao
Vignesh Narayanan
Jr-Shin Li
260
7
0
13 Dec 2021
Learning strange attractors with reservoir systems
Learning strange attractors with reservoir systems
Lyudmila Grigoryeva
Allen G. Hart
Juan-Pablo Ortega
284
35
0
11 Aug 2021
Learn to Synchronize, Synchronize to Learn
Learn to Synchronize, Synchronize to Learn
Pietro Verzelli
Cesare Alippi
L. Livi
406
31
0
06 Oct 2020
Discrete-time signatures and randomness in reservoir computing
Discrete-time signatures and randomness in reservoir computingIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2020
Christa Cuchiero
Lukas Gonon
Lyudmila Grigoryeva
Juan-Pablo Ortega
Josef Teichmann
213
55
0
17 Sep 2020
Dimension reduction in recurrent networks by canonicalization
Dimension reduction in recurrent networks by canonicalizationThe Journal of Geometric Mechanics (J. Geom. Mech.), 2020
Lyudmila Grigoryeva
Juan-Pablo Ortega
304
23
0
23 Jul 2020
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