no code implementations • 3 Feb 2024 • Hugues van Assel, Cédric Vincent-Cuaz, Nicolas Courty, Rémi Flamary, Pascal Frossard, Titouan Vayer
Unsupervised learning aims to capture the underlying structure of potentially large and high-dimensional datasets.
no code implementations • 5 Oct 2023 • Hugues van Assel, Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Nicolas Courty
We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes.
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.
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