Weekly Seminar – 03/02/2018 – Graph Convolutional Neural Networks

While classical CNNs have been very successful when dealing with signals such as speech, images, or video, in which there is an underlying Euclidean structure (regular grids), recently there has been a growing interest in trying to apply CNNs on non-Euclidean geometric data. Some examples of such data include

– 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.”
——————————————————-

References:

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.
——————————————————

Venue:2222 Coover Hall

Time: 12.00 – 1.00PM Friday, March 2nd.