You are all cordially invited to the AMLab seminar on Tuesday November 14 at 16:00 in C3.163, where Thomas Kipf will give a talk titled “End-to-end learning on graphs with graph convolutional networks”. Afterwards there are the usual drinks and snacks!
Abstract: Neural networks on graphs have gained renewed interest in the machine learning community. Recent results have shown that end-to-end trainable neural network models that operate directly on graphs can challenge well-established classical approaches, such as kernel-based methods or methods that rely on graph embeddings (e.g. DeepWalk). In this talk, I will motivate such an approach from an analogy to traditional convolutional neural networks and introduce our recent variant of graph convolutional networks (GCNs) that achieves promising results on a number of semi-supervised node classification tasks. If time permits, I will further introduce two extensions of this basic framework, namely: graph auto-encoders and relational GCNs. While graph auto-encoders provide a novel way of approaching problems like link prediction or recommendation, relational GCNs allow for efficient modeling of directed relational graphs, such as knowledge bases.