Lyudmila Grigoryeva

发表

Stability of Hamiltonian relative equilibria in symmetric magnetically confined rigid bodies

J. Ortega, Lyudmila Grigoryeva, S. Zub, 2013, 1311.2256.

Asymptotic Forecasting Error Evaluation for Estimated Temporally Aggregated Linear Processes

J. Ortega, Lyudmila Grigoryeva, 2014 .

Finite sample forecasting with estimated temporally aggregated linear processes

J. Ortega, Lyudmila Grigoryeva, 2012, 1209.4188.

Stochastic Nonlinear Time Series Forecasting Using Time-Delay Reservoir Computers: Performance and Universality

L. Larger, J. Ortega, J. Henriques, 2013 .

Non-scalar GARCH models: Composite likelihood estimation and empirical comparisons

L. Bauwens, J. Ortega, Lyudmila Grigoryeva, 2015 .

Dimension reduction in recurrent networks by canonicalization pdf

Lyudmila Grigoryeva, Juan-Pablo Ortega, J. Ortega, 2020, Journal of Geometric Mechanics.

Estimation and Empirical Performance of Non-Scalar Dynamic Conditional Correlation Models

Juan-Pablo Ortega, Lyudmila Grigoryeva, Luc Bauwens, 2014, Comput. Stat. Data Anal..

Estimation and Empirical Performance of Non-Scalar Dynamic Conditional Correlation Models

L. Bauwens, J. Ortega, Lyudmila Grigoryeva, 2014 .

Volatility Forecasting Using Global Stochastic Financial Trends Extracted from Non-Synchronous Data

Juan-Pablo Ortega, Lyudmila Grigoryeva, Anatoly Peresetsky, 2015 .

Option pricing and hedging under non-affine autoregressive stochastic volatility models

R. Elliott, A. Badescu, J. Ortega, 2016 .

Hybrid Forecasting with Estimated Temporally Aggregated Linear Processes: Hybrid Forecasting with Estimated Temporally Aggregated Processes

J. Ortega, Lyudmila Grigoryeva, 2014 .

Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data

P. Dellaportas, J. Ortega, Lyudmila Grigoryeva, 2022, 2211.00363.

Reservoir kernels and Volterra series

J. Ortega, L. Gonon, Lyudmila Grigoryeva, 2022, ArXiv.

Risk bounds for reservoir computing pdf

Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega, 2019, ArXiv.

Echo state networks are universal

Juan-Pablo Ortega, Lyudmila Grigoryeva, J. Ortega, 2018, Neural Networks.

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

Juan-Pablo Ortega, Lyudmila Grigoryeva, J. Ortega, 2017, J. Mach. Learn. Res..

Approximation Bounds for Random Neural Networks and Reservoir Systems

J. Ortega, L. Gonon, Lyudmila Grigoryeva, 2020, The Annals of Applied Probability.

Hybrid Forecasting with Estimated Temporally Aggregated Linear Processes

Juan-Pablo Ortega, Lyudmila Grigoryeva, J. Ortega, 2012 .

Infinite-dimensional reservoir computing

J. Ortega, L. Gonon, Lyudmila Grigoryeva, 2023, Neural networks : the official journal of the International Neural Network Society.

Discrete-Time Signatures and Randomness in Reservoir Computing pdf

Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Dimension reduction in recurrent networks by canonicalization pdf

Juan-Pablo Ortega, Lyudmila Grigoryeva, J. Ortega, 2020, Journal of Geometric Mechanics.

Memory and forecasting capacities of nonlinear recurrent networks pdf

Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega, 2020, Physica D: Nonlinear Phenomena.

Differentiable reservoir computing

Juan-Pablo Ortega, Lyudmila Grigoryeva, J. Ortega, 2019, J. Mach. Learn. Res..

Stochastic Nonlinear Time Series Forecasting Using Time-Delay Reservoir Computers: Performance and Universality

Laurent Larger, Juan-Pablo Ortega, Lyudmila Grigoryeva, 2013, Neural Networks.

Learning strange attractors with reservoir systems pdf

Juan-Pablo Ortega, Lyudmila Grigoryeva, Allen Hart, 2021, ArXiv.

Memory of recurrent networks: Do we compute it right?

J. Ortega, Lyudmila Grigoryeva, Giovanni Ballarin, 2023, ArXiv.

Reservoir Computing: Information Processing of Stationary Signals

Juan-Pablo Ortega, Lyudmila Grigoryeva, Julie Henriques, 2016, 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES).

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

Juan-Pablo Ortega, Lyudmila Grigoryeva, Julie Henriques, 2015, ArXiv.

Quantitative evaluation of the performance of discrete-time reservoir computers in the forecasting, filtering, and reconstruction of stochastic stationary signals

Juan-Pablo Ortega, Lyudmila Grigoryeva, Julie Henriques, 2015 .

Optimal nonlinear information processing capacity in delay-based reservoir computers

Laurent Larger, Juan-Pablo Ortega, Lyudmila Grigoryeva, 2014, Scientific Reports.

Chaos on compact manifolds: Differentiable synchronizations beyond the Takens theorem.

Allen G. Hart, J. Ortega, Lyudmila Grigoryeva, 2020, Physical review. E.

Reservoir computing for macroeconomic forecasting with mixed-frequency data

P. Dellaportas, J. Ortega, Lyudmila Grigoryeva, 2022, International Journal of Forecasting.

Singular Ridge Regression with Homoscedastic Residuals: Generalization Error with Estimated Parameters

Juan-Pablo Ortega, Lyudmila Grigoryeva, J. Ortega, 2016, 1605.09026.

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

Laurent Larger, Juan-Pablo Ortega, Lyudmila Grigoryeva, 2015, Neural Computation.

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

Laurent Larger, Juan-Pablo Ortega, Lyudmila Grigoryeva, 2016, 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES).

Learning strange attractors with reservoir systems

Allen G. Hart, J. Ortega, Lyudmila Grigoryeva, 2021, Nonlinearity.

Chaos on compact manifolds: Differentiable synchronizations beyond Takens.

Allen G. Hart, J. Ortega, Lyudmila Grigoryeva, 2020 .

Stochastic Nonlinear Time Series Forecasting Using Time-Delay Reservoir Computers: Performance and Universality

L. Larger, J. Ortega, J. Henriques, 2013, Neural Networks.

Echo state networks are universal

J. Ortega, Lyudmila Grigoryeva, 2018, Neural Networks.

Substitute or complement? Defining the relative place of EEG and fMRI in the detection of voluntary brain reactions

L. Pazart, T. Moulin, E. Haffen, 2015, Neuroscience.