5 citations · 9 across the 10 of their papers we have counts for
4 papers · 1 filter
Joint Precision Neural Networks: Task-Aware Dependency and Predictive Learning
Andrea Cavallo, Samuel Rey, Antonio G. Marques +1
Exploiting meaningful latent structures from data to solve downstream tasks is a fundamental challenge in signal processing and machine learning. While Principal Component Analysis…
Spatiotemporal Kronecker Covariance Neural Networks
Andrea Cavallo, Athanasios Georgoutsos, Elvin Isufi
Multivariate time series contain complex patterns that span across both space and time. While covariance-based statistical tools like spatiotemporal Principal Component Analysis (S…
Topological Signal Processing With Unoriented Operators
Andrea Cavallo, Varun Sarathchandran, Geert Leus +1
Topological signal processing (TSP) processes signals on simplicial complexes with oriented boundary operators, which is the natural choice for flow signals or when the topological…
Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs
Saurabh Sihag, Andrea Cavallo, Elvin Isufi +2
This feature article provides an overview of the theoretical foundations for coVariance neural networks (VNNs), i.e., graph neural networks (GNNs) operating on covariance matrices…