1 code implementation • 31 May 2022 • Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty
Current Graph Neural Networks (GNN) architectures generally rely on two important components: node features embedding through message passing, and aggregation with a specialized form of pooling.
Ranked #1 on Graph Classification on NCI1
1 code implementation • 6 Oct 2021 • Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty
To this end, the Gromov-Wasserstein (GW) distance, based on Optimal Transport (OT), has proven to be successful in handling the specific nature of the associated objects.
no code implementations • ICLR 2022 • Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty
To this end, the Gromov-Wasserstein (GW) distance, based on Optimal Transport (OT), has proven to be successful in handling the specific nature of the associated objects.
no code implementations • 31 Mar 2021 • Riccardo Rastelli, Marco Corneli
We create a framework to analyse the timing and frequency of instantaneous interactions between pairs of entities.
1 code implementation • 12 Feb 2021 • Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty
Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements.
Ranked #1 on Graph Classification on BZR
no code implementations • 1 Jan 2021 • Dingge LIANG, Marco Corneli, Pierre Latouche, Charles Bouveyron
The underlying motivation is that, when a user scores only a few products, the texts used in the reviews represent a significant source of information.
1 code implementation • 28 Oct 2020 • Andrea Zugarini, Enrico Meloni, Alessandro Betti, Andrea Panizza, Marco Corneli, Marco Gori
We formulate the problem in terms of a functional risk that depends on the learning variables through the solutions of a dynamic system.
no code implementations • 7 Apr 2020 • Laurent Vanni, Marco Corneli, Damon Mayaffre, Frédéric Precioso
A lot of effort is currently made to provide methods to analyze and understand deep neural network impressive performances for tasks such as image or text classification.
no code implementations • 10 Jul 2017 • Marco Corneli, Pierre Latouche, Fabrice Rossi
We develop a model in which interactions between nodes of a dynamic network are counted by non homogeneous Poisson processes.
no code implementations • 9 May 2016 • Marco Corneli, Pierre Latouche, Fabrice Rossi
The stochastic block model (SBM) is a flexible probabilistic tool that can be used to model interactions between clusters of nodes in a network.
no code implementations • 8 Sep 2015 • Marco Corneli, Pierre Latouche, Fabrice Rossi
To overcome this limitation, we propose a partition of the whole time horizon, in which interactions are observed, and develop a non stationary extension of the SBM, allowing to simultaneously cluster the nodes in a network along with fixed time intervals in which the interactions take place.
no code implementations • 12 Jun 2015 • Marco Corneli, Pierre Latouche, Fabrice Rossi
The latent block model (LBM) is a flexible probabilistic tool to describe interactions between node sets in bipartite networks, but it does not account for interactions of time varying intensity between nodes in unknown classes.