Talk: Geometric Deep Learning
nikitamehrotra12 opened this issue · comments
Convolutional Neural Networks, Recurrent Neural Networks, and other deep learning approaches have achieved unprecedented performance on a broad range of problems (e.g. Computer Vision and Speech Recognition). Despite the results obtained, the current research has mainly focused on data defined on Euclidean domains (i.e. grids). Nonetheless, in a multitude of different fields, such as Biology, Physics, Network Science, and Computer Graphics; one may have to deal with data defined on non-Euclidean domains (i.e. graphs and manifolds). Therefore to learn complex graph representations and relationships in the non-Euclidean domain geometric deep learning techniques are used.
A comprehensive survey of geometric deep learning - https://arxiv.org/abs/1901.00596
@nikitamehrotra12 Will this talk be a condensed version of this survey paper?
@mohitsharma29 no I just provided the link for the reference of GNNs. The talk will be based on the applications of GNN's in program analysis applications.
Hey, Can you attach your presentation slides here and then close this issue?