Data Science Seminar: Robust Representation Learning13 Oct 2020, by Sponsored events in
5 November 2020 (online)
Taylan Cemgil, Research Scientist, DeepMind UK
Machine learning systems are not robust by default. Even systems that are reported to outperform humans in a particular domain can be often shown to fail at solving problems with virtually small variations on the problem data. This talk will focus on robustness in unsupervised learning and representation learning. In particular, we will give an outline of the current work on robust training. Our goal will be to highlight the nature of the challenges that are faced in ensuring that learning systems work according to desired specifications.
The Jean Golding Institute has teamed up with the Heilbronn Institute for Mathematical Research to showcase the latest research in Data Science – methodology with roots in Mathematics and Computer Science with important applied implications. The series will feature a range of internationally regarded high-profile speakers on topics that will be relevant to a broad audience.
Please take a look at Bristol Data Science Seminars for more information about the series.