3 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity
Hugh Blayney, Álvaro Arroyo, Xiaowen Dong +1
Graph Neural Networks (GNNs) leverage the graph structure to transmit information between nodes, typically through the message-passing mechanism. While these models have found a wi…
Attention Sinks and Compression Valleys in LLMs are Two Sides of the Same Coin
Enrique Queipo-de-Llano, Álvaro Arroyo, Federico Barbero +4
Attention sinks and compression valleys have attracted significant attention as two puzzling phenomena in large language models, but have been studied in isolation. In this work, w…
Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks
Ali Hariri, Álvaro Arroyo, Alessio Gravina +6
ChebNet, one of the earliest spectral GNNs, has largely been overshadowed by Message Passing Neural Networks (MPNNs), which gained popularity for their simplicity and effectiveness…
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…