This week’s speaker for the DSRG seminar series is Rahul Singh. He will talk about Graph Convolutional Neural Networks. The abstract and references are as follows.
– In social networks, the characteristics of users can be modeled as signals on the vertices of the social graph
– In sensor networks, the sensor reading are modeled as time-dependent signals on the vertices
– In genetics, gene expression data are modeled as signals defined on the regulatory network
The non-Euclidean nature of such data implies that there are no such familiar properties as common system of coordinates, vector space structure, or shift-invariance. Consequently, basic operations like convolution and shifting that are taken for granted in the Euclidean case are even not well defined on non-Euclidean domains.
The talk will be about the recent efforts made towards the generalization of CNNs from low-dimensional regular grids to high-dimensional irregular domains such as graphs.”
Michaël Defferrard et al., “Convolutional neural networks on graphs with fast localized spectral filtering.” In Advances in Neural Information Processing Systems, pp. 3844-3852. 2016.
Thomas Kipf and Max Welling, ” Semi-supervised classification with graph convolutional networks.” In Proceedings of International Conference on Learning Representations, 2017.
Michael Bronstein et al., “Geometric deep learning: going beyond euclidean data.” IEEE Signal Processing Magazine 34.4 (2017): 18-42.