You are all cordially invited to the AMLab seminar on Tuesday December 13 at 16:00 in C3.163, where Thomas Kipf will give a talk titled “Deep Learning on Graphs with Graph Convolutional Networks”. Afterwards there are the usual drinks and snacks!
Abstract: Deep learning has recently enabled breakthroughs in the fields of computer vision and natural language processing. Little attention, however, has been devoted to the generalization of deep neural network-based models to datasets that come in the form of graphs or networks (e.g. social networks, knowledge graphs or protein-interaction networks). Generalizing convolutional neural networks, the workhorse of deep learning, to graph-structured data is not straightforward and a number of different approaches have been introduced (see  for an overview). I will review some of these models and introduce our own variant of graph convolutional networks  that achieves competitive performance on a number of semi-supervised node classification tasks. I will further talk about extensions to the basic graph convolutional framework, with special focus on our recently introduced variational graph auto-encoder —a model for unsupervised learning and link prediction—and outline future research directions.
 Graph Convolutional Networks, http://tkipf.github.io/graph-convolutional-networks/
 TN Kipf and M Welling, Semi-Supervised Classification with Graph Convolutional Networks, arXiv:1609.02907, 2016
 TN Kipf and M Welling, Variational Graph Auto-Encoders, NIPS Bayesian Deep Learning Workshop, 2016