1 citations · 1 across the 7 of their papers we have counts for
8 papers
PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting
Jelena Simeunović, Baptiste Schubnel, Pierre-Jean Alet +2
Advection-diffusion plays an essential role in transport phenomena such as atmospheric and cloud dynamics. Improved forecasting of cloud motion, in turn, enhances solar power produ…
Geodesics of Dynamic Graphs for Regime Change Detection
William Cappelletti, Étienne Voutaz, Pascal Frossard
Traditional change point detection in dynamic networks assumes abrupt transitions between stationary states, overlooking scenarios of continuous evolution which arise in most real-…
rETF-semiSL: Semi-Supervised Learning for Neural Collapse in Temporal Data
Yuhan Xie, William Cappelletti, Mahsa Shoaran +1
Deep neural networks for time series must capture complex temporal patterns, to effectively represent dynamic data. Self- and semi-supervised learning methods show promising result…
NMT-Obfuscator Attack: Ignore a sentence in translation with only one word
Sahar Sadrizadeh, César Descalzo, Ljiljana Dolamic +1
Neural Machine Translation systems are used in diverse applications due to their impressive performance. However, recent studies have shown that these systems are vulnerable to car…
Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data
William Cappelletti, Pascal Frossard
Representing and exploiting multivariate signals requires capturing relations between variables, which we can represent by graphs. Graph dictionaries allow to describe complex rela…
Deep End-to-End Survival Analysis with Temporal Consistency
Mariana Vargas Vieyra, Pascal Frossard
In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspiration from Reinforcement Learni…
PriPHiT: Privacy-Preserving Hierarchical Training of Deep Neural Networks
Yamin Sepehri, Pedram Pad, Pascal Frossard +1
The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training d…
Subgraph Matching via Partial Optimal Transport
Wen-Xin Pan, Isabel Haasler, Pascal Frossard
In this work, we propose a novel approach for subgraph matching, the problem of finding a given query graph in a large source graph, based on the fused Gromov-Wasserstein distance.…