338 citations · 1.3k across the 45 of their papers we have counts for
10 papers · 1 filter
Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks
Kyriakos Stylianopoulos, Paolo Di Lorenzo, George C. Alexandropoulos
In the Edge Inference (EI) paradigm, where a Deep Neural Network (DNN) is split across the transceivers to wirelessly communicate goal-defined features in solving a computational t…
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new fron…
Learning Multi-Frequency Partial Correlation Graphs
Gabriele D'Acunto, Paolo Di Lorenzo, Francesco Bonchi +2
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 correlat…
Generalized Simplicial Attention Neural Networks
Claudio Battiloro, Lucia Testa, Lorenzo Giusti +3
Graph machine learning methods excel at leveraging pairwise relations present in the data. However, graphs are unable to fully capture the multi-way interactions inherent in many c…
Cell Attention Networks
Lorenzo Giusti, Claudio Battiloro, Lucia Testa +3
Since their introduction, graph attention networks achieved outstanding results in graph representation learning tasks. However, these networks consider only pairwise relationships…
Multiscale Causal Structure Learning
Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa
The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system. This paper exposes a novel method, named Multiscale-Causa…
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
Paolo Di Lorenzo, Paolo Banelli, Elvin Isufi +2
The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly time-varying) subset of vertices. We…
Distributed Adaptive Learning of Graph Signals
P. Di Lorenzo, P. Banelli, S. Barbarossa +1
The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables dis…
Adaptive Least Mean Squares Estimation of Graph Signals
Paolo Di Lorenzo, Sergio Barbarossa, Paolo Banelli +1
The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a…
Sparse Distributed Learning Based on Diffusion Adaptation
Paolo Di Lorenzo, Ali H. Sayed
This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach reli…