1 citations · 2 across the 15 of their papers we have counts for
8 papers · 1 filter
Stuart-Landau Oscillatory Graph Neural Network
Kaicheng Zhang, David N. Reynolds, Piero Deidda +1
Oscillatory Graph Neural Networks (OGNNs) are an emerging class of physics-inspired architectures designed to mitigate oversmoothing and vanishing gradient problems in deep GNNs. I…
Nonlinear Joint Spectral Radius
Piero Deidda, Nicola Guglielmi, Francesco Tudisco
We introduce a nonlinear extension of the joint spectral radius (JSR) for switched discrete-time dynamical systems governed by sub-homogeneous and order-preserving maps acting on c…
Graph-Convolutional-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation
Francesco Fabbri, Martino Andrea Scarpolini, Angelo Iollo +2
Synthetic data generation plays a crucial role in medical research by mitigating privacy concerns and enabling large-scale patient data analysis. This study presents a beta-Variati…
Nonlinear spectral graph theory
Piero Deidda, Francesco Tudisco, Dong Zhang
Nonlinear spectral graph theory is an extension of the traditional (linear) spectral graph theory and studies relationships between spectral properties of nonlinear operators defin…
Approximation properties of neural ODEs
Arturo De Marinis, Davide Murari, Elena Celledoni +3
We study the approximation properties of neural ordinary differential equations (neural ODEs) in the space of continuous functions. Since a neural ODE requires input and output dim…
Efficient Sparsification of Simplicial Complexes via Local Densities of States
Anton Savostianov, Michael T. Schaub, Nicola Guglielmi +1
Simplicial complexes (SCs) have become a popular abstraction for analyzing complex data using tools from topological data analysis or topological signal processing. However, the an…
Are We Measuring Oversmoothing in Graph Neural Networks Correctly?
Kaicheng Zhang, Piero Deidda, Desmond Higham +1
Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drop…
Improving the robustness of neural ODEs with minimal weight perturbation
Arturo De Marinis, Nicola Guglielmi, Stefano Sicilia +1
We propose a method to enhance the stability of a neural ordinary differential equation (neural ODE) by reducing the maximum error growth subsequent to a perturbation of the initia…