9 papers
Directed Semi-Simplicial Learning with Applications to Brain Activity Decoding
Manuel Lecha, Andrea Cavallo, Francesca Dominici +5
Graph Neural Networks (GNNs) excel at learning from pairwise interactions but often overlook multi-way and hierarchical relationships. Topological Deep Learning (TDL) addresses thi…
Precision Neural Networks: Joint Graph And Relational Learning
Andrea Cavallo, Samuel Rey, Antonio G. Marques +1
CoVariance Neural Networks (VNNs) perform convolutions on the graph determined by the covariance matrix of the data, which enables expressive and stable covariance-based learning.…
Covariance Scattering Transforms
Andrea Cavallo, Ayushman Raghuvanshi, Sundeep Prabhakar Chepuri +1
Machine learning and data processing techniques relying on covariance information are widespread as they identify meaningful patterns in unsupervised and unlabeled settings. As a p…
Graph-Aware Diffusion for Signal Generation
Sergio Rozada, Vimal K. B., Andrea Cavallo +3
We study the problem of generating graph signals from unknown distributions defined over given graphs, relevant to domains such as recommender systems or sensor networks. Our appro…
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…
CoVariance Filters and Neural Networks over Hilbert Spaces
Claudio Battiloro, Andrea Cavallo, Elvin Isufi
CoVariance Neural Networks (VNNs) perform graph convolutions on the empirical covariance matrix of signals defined over finite-dimensional Hilbert spaces, motivated by robustness a…