Lyudmila Grigoryeva

CoRR, 2023

Infinite-dimensional reservoir computing.

[BibT_eX]

[DOI]

CoRR, 2023

2022

Discrete-Time Signatures and Randomness in Reservoir Computing.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2022

Reservoir kernels and Volterra series.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Learning strange attractors with reservoir systems.

[BibT_eX]

[DOI]

Allen Hart

CoRR, 2021

2020

Dimension reduction in recurrent networks by canonicalization.

[BibT_eX]

[DOI]

CoRR, 2020

Memory and forecasting capacities of nonlinear recurrent networks.

[BibT_eX]

[DOI]

CoRR, 2020

Approximation Bounds for Random Neural Networks and Reservoir Systems.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Differentiable reservoir computing.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2019

Risk bounds for reservoir computing.

[BibT_eX]

[DOI]

CoRR, 2019

2018

Echo state networks are universal.

[BibT_eX]

[DOI]

Neural Networks, 2018

Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2018

2016

Nonlinear Memory Capacity of Parallel Time-Delay Reservoir Computers in the Processing of Multidimensional Signals.

[BibT_eX]

[DOI]

Neural Comput., 2016

Estimation and empirical performance of non-scalar dynamic conditional correlation models.

[BibT_eX]

[DOI]

Luc Bauwens

Comput. Stat. Data Anal., 2016

Reservoir Computing: Information Processing of Stationary Signals.

[BibT_eX]

[DOI]

Julie Henriques

Proceedings of the 2016 IEEE Intl Conference on Computational Science and Engineering, 2016

Time-Delay Reservoir Computers and High-Speed Information Processing Capacity.

[BibT_eX]

[DOI]

Proceedings of the 2016 IEEE Intl Conference on Computational Science and Engineering, 2016

2015

Forecasting, filtering, and reconstruction of stochastic stationary signals using discrete-time reservoir computers.

[BibT_eX]

[DOI]

Julie Henriques