1 citations · 2 across the 15 of their papers we have counts for
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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…
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…
Contractivity of neural ODEs: an eigenvalue optimization problem
Nicola Guglielmi, Arturo De Marinis, Anton Savostianov +1
We propose a novel methodology to solve a key eigenvalue optimization problem which arises in the contractivity analysis of neural ODEs. When looking at contractivity properties of…
Cholesky-like Preconditioner for Hodge Laplacians via Heavy Collapsible Subcomplex
Anton Savostianov, Francesco Tudisco, Nicola Guglielmi
Techniques based on -th order Hodge Laplacian operators are widely used to describe the topology as well as the governing dynamics of high-order systems modeled as simplic…
Optimizing network robustness via Krylov subspaces
Stefano Massei, Francesco Tudisco
We consider the problem of attaining either the maximal increase or reduction of the robustness of a complex network by means of a bounded modification of a subset of the edge weig…
Nodal domain count for the generalized graph -Laplacian
Piero Deidda, Mario Putti, Francesco Tudisco
Inspired by the linear Schrödinger operator, we consider a generalized -Laplacian operator on discrete graphs and present new results that characterize several spectral properti…