Graph generation and probabilistic methods

Abstract

This short lecture will present three different approaches to graph generation from the ML literature, using a variety of techniques based on deep learning and probabilistic building blocks. We will then cover the motivation for incorporating uncertainty when making predictions and briefly discuss a novel approach to learning graph representations that achieves this.

Dr Cătălina Cangea

Quantitative Researcher

Quantitative researcher with 9 years of ML experience, most recently co-lead of Generative Music at Google DeepMind, with a PhD from the University of Cambridge, and inhaler of music :) Motivated by contributing ML-based knowledge and improvements to real-world systems!