16 citations · 26 across the 8 of their papers we have counts for
16 papers · 1 filter
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…
Continual Learning with Echo State Networks
Andrea Cossu, Davide Bacciu, Antonio Carta +2
Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge. The study of CL for sequential pat…
Phase Transition Adaptation
Claudio Gallicchio, Alessio Micheli, Luca Silvestri
Artificial Recurrent Neural Networks are a powerful information processing abstraction, and Reservoir Computing provides an efficient strategy to build robust implementations by pr…
Pyramidal Reservoir Graph Neural Network
Filippo Maria Bianchi, Claudio Gallicchio, Alessio Micheli
We propose a deep Graph Neural Network (GNN) model that alternates two types of layers. The first type is inspired by Reservoir Computing (RC) and generates new vertex features by…
Sparsity in Reservoir Computing Neural Networks
Claudio Gallicchio
Reservoir Computing (RC) is a well-known strategy for designing Recurrent Neural Networks featured by striking efficiency of training. The crucial aspect of RC is to properly insta…