3 papers
cs.IR2020
Modurec: Recommender Systems with Feature and Time Modulation
Javier Maroto, Clément Vignac, Pascal Frossard
Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These a…
cs.LG2020
Building powerful and equivariant graph neural networks with structural message-passing
Clement Vignac, Andreas Loukas, Pascal Frossard
Message-passing has proved to be an effective way to design graph neural networks, as it is able to leverage both permutation equivariance and an inductive bias towards learning lo…
cs.SI2019
On the choice of graph neural network architectures
Clément Vignac, Guillermo Ortiz-Jiménez, Pascal Frossard
Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the developm…