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Found 21 papers, 6 papers with code
Opportunistic Information-Bottleneck for Goal-oriented Feature Extraction and Communication
This approach is particularly useful every time a device needs to transmit data (or features) to a server that has to fulfil an inference task, as it provides a principled way to extract the most relevant features for the task to be executed, while looking for the best trade-off between the size of the feature vector to be transmitted, inference accuracy, and complexity.
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks
Disclosing generative models capabilities in semantic communication paves the way for a paradigm shift with respect to conventional communication systems, which has great potential to reduce the amount of data traffic and offers a revolutionary versatility to novel tasks and applications that were not even conceivable a few years ago.
Stability of Graph Convolutional Neural Networks through the lens of small perturbation analysis
In this work, we study the problem of stability of Graph Convolutional Neural Networks (GCNs) under random small perturbations in the underlying graph topology, i. e. under a limited number of insertions or deletions of edges.
Learning Multi-Frequency Partial Correlation Graphs
Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlations but fail to discriminate across distinct frequency bands.
Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization
Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data.
Generalized Simplicial Attention Neural Networks
The aim of this work is to introduce Generalized Simplicial Attention Neural Networks (GSANs), i. e., novel neural architectures designed to process data defined on simplicial complexes using masked self-attentional layers.
Generative Semantic Communication: Diffusion Models Beyond Bit Recovery
We prove, through an in-depth assessment of multiple scenarios, that our method outperforms existing solutions in generating high-quality images with preserved semantic information even in cases where the received content is significantly degraded.
Multi-user Goal-oriented Communications with Energy-efficient Edge Resource Management
A common challenge in running inference tasks from remote is to extract and transmit only the features that are most significant for the inference task.
Topological Signal Processing over Weighted Simplicial Complexes
Weighing the topological domain over which data can be represented and analysed is a key strategy in many signal processing and machine learning applications, enabling the extraction and exploitation of meaningful data features and their (higher order) relationships.
Topological Slepians: Maximally Localized Representations of Signals over Simplicial Complexes
This paper introduces topological Slepians, i. e., a novel class of signals defined over topological spaces (e. g., simplicial complexes) that are maximally concentrated on the topological domain (e. g., over a set of nodes, edges, triangles, etc.)
Cell Attention Networks
In this paper, we introduce Cell Attention Networks (CANs), a neural architecture operating on data defined over the vertices of a graph, representing the graph as the 1-skeleton of a cell complex introduced to capture higher order interactions.
Ranked #7 on
Graph Classification
on NCI109
Multiscale Causal Structure Learning
The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system.
Goal-Oriented Communication for Edge Learning based on the Information Bottleneck
The IB principle is used to design the encoder in order to find an optimal balance between representation complexity and relevance of the encoded data with respect to the goal.
Topological Signal Processing over Generalized Cell Complexes
To overcome this limit, in this paper we extend TSP to deal with signals defined over cell complexes and we also generalize the concept of cell complexes to include hollow cells.
Dynamic Edge Computing empowered by Reconfigurable Intelligent Surfaces
In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs).
Topological Signal Processing over Cell Complexes
The Topological Signal Processing (TSP) framework has been recently developed to analyze signals defined over simplicial complexes, i. e. topological spaces represented by finite sets of elements that are closed under inclusion of subsets [1].
6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications
The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the correct reception/interpretation of a packet is going to have on the goal accomplishment.
Discontinuous Computation Offloading for Energy-Efficient Mobile Edge Computing
We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided beyond 5G networks.
Topological Signal Processing over Simplicial Complexes
The goal of this paper is to establish the fundamental tools to analyze signals defined over a topological space, i. e. a set of points along with a set of neighborhood relations.
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed sampling and reconstruction strategies for (possibly distributed) adaptive learning of signals defined over graphs.
Adaptive Least Mean Squares Estimation of Graph Signals
The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs.
