Graph Representation Learning: Recent Advances and Open Challenges
Speaker: William Hamilton, McGill University and MILA Title: Graph Representation Learning: Recent Advances and Open Challenges Abstract: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial if we want systems that can learn, reason, and generalize from this kind of […]