3 papers
cs.LG2026
ParalESN: Enabling parallel information processing in Reservoir Computing
Matteo Pinna, Giacomo Lagomarsini, Andrea Ceni +1
Reservoir Computing (RC) has established itself as an efficient paradigm for temporal processing. However, its scalability remains severely constrained by the need to process tempo…
cs.LG2025
Deep Residual Echo State Networks: exploring residual orthogonal connections in untrained Recurrent Neural Networks
Matteo Pinna, Andrea Ceni, Claudio Gallicchio
Echo State Networks (ESNs) are a particular type of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) framework, popular for their fast and efficient l…
cs.LG2025
Residual Reservoir Memory Networks
Matteo Pinna, Andrea Ceni, Claudio Gallicchio
We introduce a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservoir Memory Networks (ResRMNs). ResRMN c…