1 citations · 1 across the 3 of their papers we have counts for
5 papers
Grassmanian Interpolation of Low-Pass Graph Filters: Theory and Applications
Anton Savostianov, Michael T. Schaub, Benjamin Stamm
Low-pass graph filters are fundamental for signal processing on graphs and other non-Euclidean domains. However, the computation of such filters for parametric graph families can b…
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…
Convergence of gradient based training for linear Graph Neural Networks
Dhiraj Patel, Anton Savostianov, Michael T. Schaub
Graph Neural Networks (GNNs) are powerful tools for addressing learning problems on graph structures, with a wide range of applications in molecular biology and social networks. Ho…
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…