68 citations · 188 across the 25 of their papers we have counts for
33 papers
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…
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
Álvaro Arroyo, Alessio Gravina, Benjamin Gutteridge +5
Graph Neural Networks (GNNs) are models that leverage the graph structure to transmit information between nodes, typically through the message-passing operation. While widely succe…
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…
Long Range Propagation on Continuous-Time Dynamic Graphs
Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio +2
Learning Continuous-Time Dynamic Graphs (C-TDGs) requires accurately modeling spatio-temporal information on streams of irregularly sampled events. While many methods have been pro…