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cs.LG2026
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,…
cs.LG2026
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…
cs.LG2025
Learning the Structure of Connection Graphs
Leonardo Di Nino, Gabriele D'Acunto, Sergio Barbarossa +1
Connection graphs (CGs) extend traditional graph models by coupling network topology with orthogonal transformations, enabling the representation of global geometric consistency. T…