Many scientific fields study data with an underlying graph or manifold structure such as social networks, sensor networks, biomedical knowledge graphs. The need for new optimization methods and neural network architectures that can accommodate these relational and non-Euclidean structures is becoming increasingly clear. Indeed, the statistical properties of data defined on high-dimensional graphs are very different form the stationarity, locality and compositionality assumptions at the core of the success of deep learning models in vision or NLP. For such type of data, I am working on a recent deep learning approach: graph neural networks able to learn a message passing algorithm and aggregation procedure to compute an embedding of the graph and its nodes.
LELARGE Marc
Speakers
Discover the all team
BAKER Antoine
Postdoctoral researcher
BLANKE Matthieu
PhD student
David ROBIN
PhD student