268 citations · 1.1k across the 58 of their papers we have counts for
10 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…
Tracking Dynamic Simplicial Complexes via Constrained State-Space Estimation
Varun Sarathchandran, Bishwadeep Das, Elvin Isufi +1
Simplicial complexes (SCs) extend graphs to represent higher-order interactions, but couple their simplex levels through the inclusion property. While static SC inference is an eme…
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 the Topology of a Simplicial Complex Using Noisy Simplicial Signals
Andrei Buciulea, Elvin Isufi, Geert Leus +1
Graphs are a fundamental tool for modeling the irregular (non-Euclidean) structure of complex data. However, they are inherently limited to representing pairwise relationships, mak…
Exact Bayesian Tracking of Dynamic Network Topologies
Victor M. Tenorio, Elvin Isufi, Geert Leus +1
Tracking the temporal evolution of network topologies is a fundamental challenge in social networks, epidemiology, and sensor systems, among others. This paper develops an exact Ba…