To accompany our (proto-)book’s release, and elucidate the key concepts of our work, we have delivered a series of keynote talks at machine learning conferences and seminars. We make the most relevant such talks (including key talks by other speakers) easily accessible here.
| Geometric Deep Learning: Grids, Groups, Graphs, Geodesics and Gauges | Petar Veličković | Friedrich-Alexander Universität Erlangen-Nürnberg | Proto-book launch keynote, at the birthplace of the Erlangen Program |
| Geometric Deep Learning: The Erlangen Programme of ML | Michael M. Bronstein | ICLR 2021 Keynote Talk | An infographic ‘tour de force’ into all things GDL |
| Geometric Deep Learning | Geordie Williamson | Machine Learning for the Working Mathematician | A top representation theorist’s perspectives on GDL |
| Equivariant Networks | Taco Cohen, Risi Kondor | NeurIPS 2020 Tutorial | Focussed tutorial on building equivariant models: the essence of GDL |