268 citations · 1.1k across the 58 of their papers we have counts for
6 papers · 2 filters
Sparse Covariance Neural Networks
Andrea Cavallo, Zhan Gao, Elvin Isufi
Covariance Neural Networks (VNNs) perform graph convolutions on the covariance matrix of input data to leverage correlation information as pairwise connections. They have achieved…
Spatiotemporal Covariance Neural Networks
Andrea Cavallo, Mohammad Sabbaqi, Elvin Isufi
Modeling spatiotemporal interactions in multivariate time series is key to their effective processing, but challenging because of their irregular and often unknown structure. Stati…
Online Learning Of Expanding Graphs
Samuel Rey, Bishwadeep Das, Elvin Isufi
This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are…
Online Graph Filtering Over Expanding Graphs
Bishwadeep Das, Elvin Isufi
Graph filters are a staple tool for processing signals over graphs in a multitude of downstream tasks. However, they are commonly designed for graphs with a fixed number of nodes,…
Fair CoVariance Neural Networks
Andrea Cavallo, Madeline Navarro, Santiago Segarra +1
Covariance-based data processing is widespread across signal processing and machine learning applications due to its ability to model data interconnectivities and dependencies. How…
Higher-Order Topological Directionality and Directed Simplicial Neural Networks
Manuel Lecha, Andrea Cavallo, Francesca Dominici +2
Topological Deep Learning (TDL) has emerged as a paradigm to process and learn from signals defined on higher-order combinatorial topological spaces, such as simplicial or cell com…