928 citations · 3.7k across the 56 of their papers we have counts for
58 papers
SPARQ: An Optimization Framework for the Distribution of AI-Intensive Applications under Non-Linear Delay Constraints
Pietro Spadaccino, Paolo Di Lorenzo, Sergio Barbarossa +2
Next-generation real-time compute-intensive applications, such as extended reality, multi-user gaming, and autonomous transportation, are increasingly composed of heterogeneous AI-…
Networks of Causal Abstractions: A Sheaf-theoretic Framework
Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa
A core challenge in causal artificial intelligence is the principled coordination of multiple, imperfect, and subjective causal perspectives arising from distributed agents with li…
Physics-Informed Topological Signal Processing for Water Distribution Network Monitoring
Tiziana Cattai, Stefania Sardellitti, Stefania Colonnese +2
Water management is one of the most critical aspects of our society, together with population increase and climate change. Water scarcity requires a better characterization and mon…
Learning Sheaf Laplacian Optimizing Restriction Maps
Leonardo Di Nino, Sergio Barbarossa, Paolo Di Lorenzo
The aim of this paper is to propose a novel framework to infer the sheaf Laplacian, including the topology of a graph and the restriction maps, from a set of data observed over the…
Topological Signal Processing and Learning: Recent Advances and Future Challenges
Elvin Isufi, Geert Leus, Baltasar Beferull-Lozano +2
Developing methods to process irregularly structured data is crucial in applications like gene-regulatory, brain, power, and socioeconomic networks. Graphs have been the go-to alge…
Opportunistic Information-Bottleneck for Goal-oriented Feature Extraction and Communication
Francesco Binucci, Paolo Banelli, Paolo Di Lorenzo +1
The Information Bottleneck (IB) method is an information theoretical framework to design a parsimonious and tunable feature-extraction mechanism, such that the extracted features a…