5 citations · 9 across the 10 of their papers we have counts for
15 papers
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…
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…
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…
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.…
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…
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…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
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…
TopoBench: A Framework for Benchmarking Topological Deep Learning
Lev Telyatnikov, Guillermo Bernardez, Marco Montagna +34
This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL int…