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Structured deep models: Deep learning on graphs and beyond

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In the recent years there has been an increasing number of success stories in applying deep learning techniques to graph-structured data. The workhorse in this emerging field is the graph neural network: a message passing algorithm parameterized by neural networks, trained via backpropagation. Variants of graph neural networks now define the state of the art in many classical graph or network problems, such as node classification, graph classification, and link prediction.

In this talk, I will give an overview of structured deep models that employ graph neural networks as a key component and discuss trade-offs for a few popular model variants such as graph convolutional networks (GCNs) [1] and graph attention networks (GATs) [2]. I will further introduce two emerging research directions: learning deep generative models of graphs and inference of latent graph structure. Structured deep models are ideal candidates for these areas and hold promise for applications such as chemical synthesis, program induction, and modeling of interacting physical and multi-agent systems.

[1] Kipf & Welling, Semi-supervised classification with graph convolutional networks, ICLR 2017 [2] Veličković et al., Graph attention networks, ICLR 2018

This talk is part of the Machine Learning @ CUED series.

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