Fading memory echo state networks are universal

22 October 2020

Papers citing "Fading memory echo state networks are universal"

13 / 13 papers shown

Title
State-space systems as dynamic generative models Juan-Pablo Ortega Florian Rossmannek 68 1 0 13 Mar 2025
Fading memory and the convolution theorem Juan-Pablo Ortega Florian Rossmannek 41 0 0 14 Aug 2024
On the choice of the non-trainable internal weights in random feature maps Pinak Mandal Georg Gottwald Nicholas Cranch TPM 40 1 0 07 Aug 2024
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 41 1 0 05 Feb 2024
Change Point Detection with Conceptors Noah D. Gade J. Rodu 15 0 0 11 Aug 2023
A Brief Survey on the Approximation Theory for Sequence Modelling Hao Jiang Qianxiao Li Zhong Li Shida Wang AI4TS 30 12 0 27 Feb 2023
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis Luca Galimberti Anastasis Kratsios Giulia Livieri OOD 28 14 0 24 Oct 2022
Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer Beatrice Acciaio Anastasis Kratsios G. Pammer OOD 49 20 0 31 Jan 2022
Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics A. Racca Luca Magri OOD AAML 13 60 0 09 Feb 2021
Discrete-time signatures and randomness in reservoir computing Christa Cuchiero Lukas Gonon Lyudmila Grigoryeva Juan-Pablo Ortega Josef Teichmann 19 45 0 17 Sep 2020
Dimension reduction in recurrent networks by canonicalization Lyudmila Grigoryeva Juan-Pablo Ortega 18 19 0 23 Jul 2020
Approximation Bounds for Random Neural Networks and Reservoir Systems Lukas Gonon Lyudmila Grigoryeva Juan-Pablo Ortega 25 64 0 14 Feb 2020
Risk bounds for reservoir computing Lukas Gonon Lyudmila Grigoryeva Juan-Pablo Ortega 27 40 0 30 Oct 2019