University of Cambridge > Talks.cam > ML@CL Seminar Series > Differential geometry for representation learning

Differential geometry for representation learning

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact .

234088

A common assumption in machine learning is that the data lie near a low dimensional manifold, which the shortest paths between points should respect. In this talk, we focus on differential geometry and present computational methods that enables us to learn and use this underlying structure. We rely on the latent space of generative models, where we capture the geometry of the data manifold. We can then compute the associated shortest paths, which is a distance measure invariant under reparametrizations of the latent space. We demonstrate though that this approach requires to quantify meaningfully the uncertainty of the generative process. Finally, we show that we can use the latent geometry in several ways, as well as for applications in robotics and life sciences.

This talk is part of the ML@CL Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2022 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity