73 citations · 139 across the 34 of their papers we have counts for
15 papers · 1 filter
Colored Markov Random Fields for Probabilistic Topological Modeling
Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto +3
Probabilistic Graphical Models (PGMs) encode conditional dependencies among random variables using a graph -nodes for variables, links for dependencies- and factorize the joint dis…
Learning Network Sheaves for AI-native Semantic Communication
Enrico Grimaldi, Mario Edoardo Pandolfo, Gabriele D'Acunto +2
Recent advances in AI call for a paradigm shift from bit-centric communication to goal- and semantics-oriented architectures, paving the way for AI-native 6G networks. In this cont…
Simplicial Gaussian Models: Representation and Inference
Lorenzo Marinucci, Gabriele D'Acunto, Paolo Di Lorenzo +1
Probabilistic graphical models (PGMs) are powerful tools for representing statistical dependencies through graphs in high-dimensional systems. However, they are limited to pairwise…
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…
Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding
Lorenzo Pannacci, Simone Fiorellino, Mario Edoardo Pandolfo +2
Deep joint source-channel coding (DeepJSCC) has emerged as a powerful paradigm for end-to-end semantic communications, jointly learning to compress and protect task-relevant featur…
Communication Efficient Split Learning of ViTs with Attention-based Double Compression
Federico Alvetreti, Jary Pomponi, Paolo Di Lorenzo +1
This paper proposes a novel communication-efficient Split Learning (SL) framework, named Attention-based Double Compression (ADC), which reduces the communication overhead required…
Latent Space Alignment for AI-Native MIMO Semantic Communications
Mario Edoardo Pandolfo, Simone Fiorellino, Emilio Calvanese Strinati +1
Semantic communications focus on prioritizing the understanding of the meaning behind transmitted data and ensuring the successful completion of tasks that motivate the exchange of…
RIS-aided Latent Space Alignment for Semantic Channel Equalization
Tomás Hüttebräucker, Mario Edoardo Pandolfo, Simone Fiorellino +2
Semantic communication systems introduce a new paradigm in wireless communications, focusing on transmitting the intended meaning rather than ensuring strict bit-level accuracy. Th…
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…
Adaptive Semantic Token Communication for Transformer-based Edge Inference
Alessio Devoto, Jary Pomponi, Mattia Merluzzi +2
This paper presents an adaptive framework for edge inference based on a dynamically configurable transformer-powered deep joint source channel coding (DJSCC) architecture. Motivate…
Semantic and Goal-oriented Wireless Network Coverage: The Area of Effectiveness
Mattia Merluzzi, Giuseppe Di Poce, Paolo Di Lorenzo
Assessing wireless coverage is a fundamental task for public network operators and private deployments, whose goal is to guarantee quality of service across the network while minim…
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)…
Causal Abstraction Learning based on the Semantic Embedding Principle
Gabriele D'Acunto, Fabio Massimo Zennaro, Yorgos Felekis +1
Structural causal models (SCMs) allow us to investigate complex systems at multiple levels of resolution. The causal abstraction (CA) framework formalizes the mapping between high-…
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…
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…