4 citations · 5 across the 11 of their papers we have counts for
9 papers · 1 filter
Free-Probability Kernels for Zero-Rollout Hyperparameter Selection in Reservoir Computing
Sara Malacarne, Andrea Ceni, Claudio Gallicchio
Reservoir computing (RC) couples a fixed recurrent dynamical system with a trained lightweight readout, but this efficiency is partly lost during hyperparameter selection: the recu…
Frequency Domain Reservoir Computing
Klaus Schertler, Xiomara Runge, Andrea Ceni +2
While the quadratic sequence-length bottleneck of transformers has fueled a resurgence in recurrent models, effectively capturing complex dynamics requires architectures that balan…
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…
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…
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…
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
Andrea Ceni, Alessio Gravina, Claudio Gallicchio +3
The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, exi…
GRAMA: Adaptive Graph Autoregressive Moving Average Models
Moshe Eliasof, Alessio Gravina, Andrea Ceni +3
Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural Networks (GNNs) in modeling long-range interactions. Despite their success, existing methods e…
Edge of stability echo state networks
Andrea Ceni, Claudio Gallicchio
Echo State Networks (ESNs) are time-series processing models working under the Echo State Property (ESP) principle. The ESP is a notion of stability that imposes an asymptotic fadi…
Random orthogonal additive filters: a solution to the vanishing/exploding gradient of deep neural networks
Andrea Ceni
Since the recognition in the early nineties of the vanishing/exploding (V/E) gradient issue plaguing the training of neural networks (NNs), significant efforts have been exerted to…