73 citations · 139 across the 34 of their papers we have counts for
6 papers · 1 filter
Inverse Cross-spectral Neural Networks for Multivariate Time Series
Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto +2
CoVariance Neural Networks and their extensions have emerged as effective tools for processing multivariate data, deriving graph shift operators directly from second-order statisti…
Sheaf-Based Federated Representation Learning
Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino +4
Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing modalities, model architectures,…
A Sheaf-Theoretic Framework for Distributed Multi-Site Channel Charting
Enrico Grimaldi, Leonardo Di Nino, Mario Edoardo Pandolfo +3
Channel charting (CC) enables data-driven user localization in wireless networks by embedding channel state information (CSI) into low-dimensional representations. In multi-cell sc…
SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations
Mario Edoardo Pandolfo, Enrico Grimaldi, Lorenzo Marinucci +4
Latent representations learned by neural networks often exhibit semantic structure, where concept similarity is reflected by geometric proximity in embedding space. However, compar…
Federated Latent Space Alignment for Multi-user Semantic Communications
Giuseppe Di Poce, Mario Edoardo Pandolfo, Emilio Calvanese Strinati +1
Semantic communication aims to convey meaning for effective task execution, but differing latent representations in AI-native devices can cause semantic mismatches that hinder mutu…
Learning Consistent Causal Abstraction Networks
Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa
Causal artificial intelligence aims to enhance explainability, trustworthiness, and robustness in AI by leveraging structural causal models (SCMs). In this pursuit, recent advances…