Juan-Pablo Ortega

Neural Networks, 2024

Learnability of Linear Port-Hamiltonian Systems.

[BibT_eX]

[DOI]

Daiying Yin

J. Mach. Learn. Res., 2024

Memory of recurrent networks: Do we compute it right?

[BibT_eX]

[DOI]

Giovanni Ballarin

J. Mach. Learn. Res., 2024

Fading memory and the convolution theorem.

[BibT_eX]

[DOI]

Florian Rossmannek

CoRR, 2024

State-Space Systems as Dynamic Generative Models.

[BibT_eX]

[DOI]

Florian Rossmannek

CoRR, 2024

A Structure-Preserving Kernel Method for Learning Hamiltonian Systems.

[BibT_eX]

[DOI]

Jianyu Hu

Daiying Yin

CoRR, 2024

2023

The Design and Development of Instrumented Toys for the Assessment of Infant Cognitive Flexibility.

[BibT_eX]

[DOI]

Vishal Ramanathan

Mohammad Zaidi Ariffin

Guo Dong Goh

Guo Liang Goh

Mohammad Adhimas Rikat

Sensors, March, 2023

Invariant kernels on Riemannian symmetric spaces: a harmonic-analytic approach.

[BibT_eX]

[DOI]

CoRR, 2023

Geometric Learning with Positively Decomposable Kernels.

[BibT_eX]

[DOI]

CoRR, 2023

Learning multi-modal generative models with permutation-invariant encoders and tighter variational bounds.

[BibT_eX]

[DOI]

CoRR, 2023

The Gaussian Kernel on the Circle and Spaces that Admit Isometric Embeddings of the Circle.

[BibT_eX]

[DOI]

Nathaël Da Costa

Cyrus Mostajeran

Proceedings of the Geometric Science of Information - 6th International Conference, 2023

2022

Guest Editorial Special Issue on New Frontiers in Extremely Efficient Reservoir Computing.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 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

Transport in reservoir computing.

[BibT_eX]

[DOI]

G. Manjunath

CoRR, 2022

2021

Fading memory echo state networks are universal.

[BibT_eX]

[DOI]

Neural Networks, 2021

Learning strange attractors with reservoir systems.

[BibT_eX]

[DOI]

Allen Hart

CoRR, 2021

Expressiveness and Structure Preservation in Learning Port-Hamiltonian Systems.

[BibT_eX]

[DOI]

Daiying Yin

Proceedings of the Geometric Science of Information - 6th International Conference, 2021

2020

Reservoir Computing Universality With Stochastic Inputs.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 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

Closed-form variance swap prices under general affine GARCH models and their continuous-time limits.

[BibT_eX]

[DOI]

Alexandru Badescu

Zhenyu Cui

Ann. Oper. Res., 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

Non-Gaussian GARCH option pricing models and their diffusion limits.

[BibT_eX]

[DOI]

Alexandru Badescu

Robert J. Elliott

Eur. J. Oper. Res., 2015

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

[BibT_eX]

[DOI]

Julie Henriques

CoRR, 2015

2014

Stochastic nonlinear time series forecasting using time-delay reservoir computers: Performance and universality.

[BibT_eX]

[DOI]

Neural Networks, 2014

Multivariate GARCH estimation via a Bregman-proximal trust-region method.

[BibT_eX]

[DOI]

Stéphane Chrétien