Sponsored by Evolution AI: https://www.evolution.ai/

Tuesday 7 September 2021

Abstract: Graphs are a powerful abstraction: they allow us to efficiently describe data in the form of entities and their pairwise relationships. The past four years have seen an incredible proliferation of graph neural networks (GNNs): neural network architectures that are effective at learning and reasoning with data provided in the form of a graph. Rarely, however, do we ask the question where and how the entities and relations are obtained from in the first place on which we deploy our models, and how we can infer effective relational abstractions from data in cases where they are not available. This talk focuses on the question of how we can build effective relational machine learning models in the absence of annotated links or relations, or even in the absence of abstractions such as entities or objects in the first place. I will give a brief introduction to graph neural networks (GNNs) and cover work on GNN-based link prediction, on Neural Relational Inference, and more recent work on object discovery and relational learning with raw perceptual inputs, such as images or videos.

Bio: Thomas Kipf is a Research Scientist at Google Research in the Brain Team in Amsterdam. Prior to joining Google, he completed his PhD at University of Amsterdam under Prof. Max Welling on the topic "Deep Learning with Graph-Structured Representations”. His research interests lie in the area of relational learning and in developing models that can reason about the world in terms of structured abstractions such as objects and their relations.

Moderated by: Johannes Klicpera