4 citations · 5 across the 11 of their papers we have counts for
4 papers · 1 filter
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…