Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
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…
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…
cs.LG2025
VitaGraph: Building a Knowledge Graph for Biologically Relevant Learning Tasks
Francesco Madeddu, Lucia Testa, Gianluca De Carlo +5
The intrinsic complexity of human biology presents ongoing challenges to scientific understanding. Researchers collaborate across disciplines to expand our knowledge of the biologi…