1 code implementation • 8 Apr 2024 • Matteo Farina, Massimiliano Mancini, Elia Cunegatti, Gaowen Liu, Giovanni Iacca, Elisa Ricci
In this challenging setting, the transferable representations already encoded in the pretrained model are a key aspect to preserve.
1 code implementation • 27 Mar 2024 • Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca
The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most.
1 code implementation • 16 Feb 2024 • Andrea Ferigo, Elia Cunegatti, Giovanni Iacca
To overcome this limitation, we propose a novel plasticity model, called Neuron-centric Hebbian Learning (NcHL), where optimization focuses on neuron- rather than synaptic-specific Hebbian parameters.
1 code implementation • 26 May 2023 • Elia Cunegatti, Matteo Farina, Doina Bucur, Giovanni Iacca
With these novelties, we show the following: (a) The proposed MGE allows to extract topological metrics that are much better predictors of the accuracy drop than metrics computed from current input-agnostic BGEs; (b) Which metrics are important at different sparsity levels and for different architectures; (c) A mixture of our topological metrics can rank PaI algorithms more effectively than Ramanujan-based metrics.
1 code implementation • 13 Apr 2022 • Elia Cunegatti, Giovanni Iacca, Doina Bucur
Finding the most influential nodes in a network is a computationally hard problem with several possible applications in various kinds of network-based problems.