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pro vyhledávání: '"Hart, Allen G."'
Generalised Synchronisations, Embeddings, and Approximations for Continuous Time Reservoir Computers
Autor:
Hart, Allen G
We establish conditions under which a continuous time reservoir computer, such as a leaky integrator echo state network, admits a generalised synchronisation $f$ between between the source dynamics and reservoir dynamics. We show that multiple genera
Externí odkaz:
http://arxiv.org/abs/2211.09515
Autor:
Hart, Allen G
A reservoir computer is a special type of neural network, where most of the weights are randomly fixed and only a subset are trained. In this thesis we prove results about reservoir computers trained on deterministic dynamical systems, and stochastic
Externí odkaz:
http://arxiv.org/abs/2111.14226
Publikováno v:
July 2018 Acta Crystallographica Section A: Foundations and Advances 74(4):357-372
We review the Markov theoretic description of 1D aperiodic crystals, describing the stacking-faulted crystal polytype as a special case of an aperiodic crystal. Under this description we generalise the centrosymmetric unit cell underlying a topologic
Externí odkaz:
http://arxiv.org/abs/2102.08418
An Echo State Network (ESN) is a type of single-layer recurrent neural network with randomly-chosen internal weights and a trainable output layer. We prove under mild conditions that a sufficiently large Echo State Network can approximate the value f
Externí odkaz:
http://arxiv.org/abs/2102.06258
Generalised synchronisations, embeddings, and approximations for continuous time reservoir computers
Autor:
Hart, Allen G.
Publikováno v:
In Physica D: Nonlinear Phenomena February 2024 458
Echo State Networks (ESNs) are a class of single-layer recurrent neural networks with randomly generated internal weights, and a single layer of tuneable outer weights, which are usually trained by regularised linear least squares regression. Remarka
Externí odkaz:
http://arxiv.org/abs/2005.06967
Echo State Networks (ESNs) are a class of single layer recurrent neural networks that have enjoyed recent attention. In this paper we prove that a suitable ESN, trained on a series of measurements of an invertible dynamical system, induces a C1 map f
Externí odkaz:
http://arxiv.org/abs/1908.05202
Publikováno v:
In Physica D: Nonlinear Phenomena July 2021 421
Publikováno v:
Acta Crystallographica. Section A, Foundations & Advances. May2019, Vol. 75 Issue 3, p501-516. 16p.