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Found 14 papers, 6 papers with code
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.
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes.
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.
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.
ICML 2023 Topological Deep Learning Challenge : Design and Results
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning.
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.
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
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.
