Speaker
Raghavendra Selvan
(DIKU)
Description
Learning compact and continuous representation spaces for discrete and non-Euclidean objects such as graphs can be useful to study their properties and perform meaningful vector manipulations that can translate back into the data space. In this session, we will look into graph neural network based regularised auto-encoders that can learn embedding spaces, that can also be conditioned on additional physical properties.
