Lyudmila Grigoryeva
Title
Prof. Ph.D.
Last Name
Grigoryeva
First name
Lyudmila
Email
lyudmila.grigoryeva@unisg.ch
ORCID
Phone
+41 71 224 31 54
12 results
Now showing 1 - 10 of 12
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Item type:Publication, Reservoir Kernels and Volterra Series(2025); ; Type:journal articleJournal:IEEE Transactions on Neural Networks and Learning SystemsScopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Memory of recurrent networks: Do we compute it right?Numerical evaluations of the memory capacity (MC) of recurrent neural networks reported in the literature often contradict well-established theoretical bounds. In this paper, we study the case of linear echo state networks, for which the total memory capacity has been proven to be equal to the rank of the corresponding Kalman controllability matrix. We shed light on various reasons for the inaccurate numerical estimations of the memory, and we show that these issues, often overlooked in the recent literature, are of an exclusively numerical nature. More explicitly, we prove that when the Krylov structure of the linear MC is ignored, a gap between the theoretical MC and its empirical counterpart is introduced. As a solution, we develop robust numerical approaches by exploiting a result of MC neutrality with respect to the input mask matrix. Simulations show that the memory curves that are recovered using the proposed methods fully agree with the theory.Type:journal articleJournal:Journal of Machine Learning Research - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reservoir computing for macroeconomic forecasting with mixed-frequency data(2024-07); ; Scopus© Citations 21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scopus© Citations 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Memory of recurrent networks: Do we compute it right?(2023-05-02); ; Numerical evaluations of the memory capacity (MC) of recurrent neural networks reported in the literature often contradict well-established theoretical bounds. In this paper, we study the case of linear echo state networks, for which the total memory capacity has been proven to be equal to the rank of the corresponding Kalman controllability matrix. We shed light on various reasons for the inaccurate numerical estimations of the memory, and we show that these issues, often overlooked in the recent literature, are of an exclusively numerical nature. More explicitly, we prove that when the Krylov structure of the linear MC is ignored, a gap between the theoretical MC and its empirical counterpart is introduced. As a solution, we develop robust numerical approaches by exploiting a result of MC neutrality with respect to the input mask matrix. Simulations show that the memory curves that are recovered using the proposed methods fully agree with the theory.Type:journal article - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Memory and forecasting capacities of nonlinear recurrent networksType:journal articleJournal:Physica D: Nonlinear PhenomenaScopus© Citations 25 - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Memory Capacity of Nonlinear Recurrent Networks: Is It Informative?(2025-10-20); ; Type:conference paperJournal:Lecture Notes in Computer ScienceVolume:16035Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication,