22 citations · 61 across the 30 of their papers we have counts for
9 papers · 1 filter
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…
Algorithmic Collective Action with Multiple Collectives
Claudio Battiloro, Pietro Greiner, Bret Nestor +2
As learning systems increasingly influence everyday decisions, user-side steering via Algorithmic Collective Action (ACA)-coordinated changes to shared data-offers a complement to…
Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications
Simone Fiorellino, Claudio Battiloro, Emilio Calvanese Strinati +1
In future AI-native wireless networks, the presence of mismatches between the latent spaces of independently designed and trained deep neural network (DNN) encoders may impede mutu…
Topological Adaptive Least Mean Squares Algorithms over Simplicial Complexes
Lorenzo Marinucci, Claudio Battiloro, Paolo Di Lorenzo
This paper introduces a novel adaptive framework for processing dynamic flow signals over simplicial complexes, extending classical least-mean-squares (LMS) methods to high-order t…
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…
Topological Dictionary Learning
Enrico Grimaldi, Claudio Battiloro, Paolo Di Lorenzo
The aim of this paper is to introduce a novel dictionary learning algorithm for sparse representation of signals defined over combinatorial topological spaces, specifically, regula…
The Relativity of Causal Knowledge
Gabriele D'Acunto, Claudio Battiloro
Recent advances in artificial intelligence reveal the limits of purely predictive systems and call for a shift toward causal and collaborative reasoning. Drawing inspiration from t…
Topological Neural Networks over the Air
Simone Fiorellino, Claudio Battiloro, Paolo Di Lorenzo
Topological neural networks (TNNs) are information processing architectures that model representations from data lying over topological spaces (e.g., simplicial or cell complexes)…
Quantum Simplicial Neural Networks
Simone Piperno, Claudio Battiloro, Andrea Ceschini +3
Graph Neural Networks (GNNs) excel at learning from graph-structured data but are limited to modeling pairwise interactions, insufficient for capturing higher-order relationships p…