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A Time-Vertex Signal Processing Framework

Related publications (64)

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We examine the connection of two graph parameters, the size of a minimum feedback arcs set and the acyclic disconnection. A feedback arc set of a directed graph is a subset of arcs such that after deletion the graph becomes acyclic. The acyclic disconnecti ...
Elsevier2024

Assessment framework for deepfake detection in real-world situations

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Detecting digital face manipulation in images and video has attracted extensive attention due to the potential risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been employed and ...
2024

Maximum Independent Set: Self-Training through Dynamic Programming

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This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...
2023

Equivariant Neural Architectures for Representing and Generating Graphs

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Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entitie ...
EPFL2023

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...
Association for the Advancement of Artificial Intelligence (AAAI)2023

Graph neural networks for dynamic modeling of roller bearings

Olga Fink, Vinay Sharma

Machine learning has paved the way for the real-time monitoring of complex infrastructure and industrial systems. However, purely data-driven methods have not been able to learn the underlying dynamics and generalize them to operating conditions that have ...
PHM Society2023

Graph-based approximate message passing iterations

Raphaël Jean Berthier

Approximate message passing (AMP) algorithms have become an important element of high-dimensional statistical inference, mostly due to their adaptability and concentration properties, the state evolution (SE) equations. This is demonstrated by the growing ...
OXFORD UNIV PRESS2023

Impact of Video Processing Operations in Deepfake Detection

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The detection of digital face manipulation in video has attracted extensive attention due to the increased risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been developed and ha ...
2023

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We consider the problem of learning implicit neural representations (INRs) for signals on non-Euclidean domains. In the Euclidean case, INRs are trained on a discrete sampling of a signal over a regular lattice. Here, we assume that the continuous signal e ...
2022

Quantization for Distributed Processing and Learning of Structured Data

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In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...
EPFL2022

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