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Found 29 papers, 8 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.
Dynamic Relative Representations for Goal-Oriented Semantic Communications
This paper presents a novel framework for goal-oriented semantic communication, leveraging relative representations to mitigate semantic mismatches via latent space alignment.
Position Paper: Challenges and Opportunities in Topological Deep Learning
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models.
Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents.
Control Aspects for Using RIS in Latency-Constrained Mobile Edge Computing
This paper investigates the role and the impact of control operations for dynamic mobile edge computing (MEC) empowered by Reconfigurable Intelligent Surfaces (RISs), in which multiple devices offload their computation tasks to an access point (AP) equipped with an edge server (ES), with the help of the RIS.
Enabling Edge Artificial Intelligence via Goal-oriented Deep Neural Network Splitting
In this work, we explore the opportunity of DNN splitting at the edge of 6G wireless networks to enable low energy cooperative inference with target delay and accuracy with a goal-oriented perspective.
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.
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a given graph topology by dynamically learning it.
Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces
In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable intelligent surfaces (RISs).
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.
Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back
We define tangent bundle filters and tangent bundle neural networks (TNNs) based on this convolution operation, which are novel continuous architectures operating on tangent bundle signals, i. e. vector fields over the manifolds.
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.
Tangent Bundle Filters and Neural Networks: from Manifolds to Cellular Sheaves and Back
In this work we introduce a convolution operation over the tangent bundle of Riemannian manifolds exploiting the Connection Laplacian operator.
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.)
Pooling Strategies for Simplicial Convolutional Networks
The goal of this paper is to introduce pooling strategies for simplicial convolutional neural networks.
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.
Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees
Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e. g. sensors), possibly pre-processed (e. g. data compression), and finally processed remotely to output the result of training and/or inference phases.
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.
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).
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.
Distributed Training of Graph Convolutional Networks
After formulating the centralized GCN training problem, we first show how to make inference in a distributed scenario where the underlying data graph is split among different agents.
Distributed Stochastic Nonconvex Optimization and Learning based on Successive Convex Approximation
We study distributed stochastic nonconvex optimization in multi-agent networks.
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.
Stochastic Training of Neural Networks via Successive Convex Approximations
Additionally, we show how the algorithm can be easily parallelized over multiple computational units without hindering its performance.
A Framework for Parallel and Distributed Training of Neural Networks
The aim of this paper is to develop a general framework for training neural networks (NNs) in a distributed environment, where training data is partitioned over a set of agents that communicate with each other through a sparse, possibly time-varying, connectivity pattern.
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.
