1 citations · 1 across the 3 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2025
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…
cs.LG2025
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…