Augusto Stoffel - Graph neural networks for information extraction with PyTorch

Graph neural networks for information extraction with PyTorch [EuroPython 2021 - Talk - 2021-07-30 - Parrot [Data Science]] [Online] By Augusto Stoffel In this talk, I will provide a primer on a class of deep learning models known as graph neural networks (GNNs). These models generalize the well-known convolutional neural networks (CNNs) ubiquitous in computer vision, and the approach of the talk will emphasize that analogy. Specifically, after a quick general recap on graphs, we will see how to think of an image as a graph where nodes represent pixels and edges represent the relationship of being a neighbor. After expressing the formulas for a CNN in terms of this graph, it is not hard to imagine ways to pass from the graph of an image to arbitrary graphs. This way, one naturally arrives at a basic GNN architecture. Other recent advances, for instance graph attention networks, can also be surveyed, time permitting. We will then survey the existing Python implementations and su