Speaker |
Peter Orbanz |
|---|---|
Affiliation |
Gatsby Computational Neuroscience Unit UCL |
Date |
Friday, 06 October 2023 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
The symmetries of crystals, periodic tilings and similar repetitive geometries are described by a class of groups called crystallographic groups. Motivated by problems in materials science, I will explain how to obtain a representation of continuous functions invariant under such a group, (1) by factoring through a structure that geometers call an orbifold and (2) by a certain generalization of the Fourier transform. This representation is constructive, can be implemented algorithmically, and allows us to construct machine learning models with crystallographic symmetries, such as neural networks, kernel machines, and Gaussian processes. |
Biography |
Peter Orbanz is a Professor of Machine Learning in the Gatsby Unit. He moved here from Columbia University, where he was associate professor of statistics. He has also been a postdoc at Cambridge, an office mate of Marc Deisenroth, a Microsoft employee, and a PhD student at ETH Zurich, in no particular order. |