Agenda

Categoría: Conferencias

Descripción: Graph-structured data is ubiquitous and occurs in several application domains. The talk will provide an overview of graph representation learning approaches such a graph convolutional networks. We show that these approaches can be roughly divided in two groups: as instances of tensor factorization approaches and as algorithms that learn from local graph structures such as paths and neighborhoods. The talk will also discuss applications of graph neural networks that we are currently working on such as polypharmacy and patient outcome prediction.

Fecha Inicio: 27-06-2019

Fecha Fin: 27-06-2019

Hora: 11:00

Convocante: GRAM Research Group

Fichero Adjunto:

• Conferencia_Mathias_Niepert_27_Junio_2019.pdf