AGATE Seminar
Applied Geometry, Algebra, and Topology in Edinburgh
Applied Geometry, Algebra, and Topology in Edinburgh (AGATE)
AGATE is an informal seminar for anyone at the University of Edinburgh who is interested in applied aspects (broadly construed) of geometry, algebra and topology. AGATE’s remit spans topics including algebraic statistics, geometric deep learning, and topological data analysis. The seminar is open to computational, theoretical and statistical research as well as domain-specific applications.
When: Wednesdays 15:05 to 16:00
Autumn 2024 Location: 3.10/3.11 Dugald Stewart Building
Organizers: Sjoerd Beentjes, Darrick Lee, and Emily Roff
For the first semester, all talks will be by internal speakers. We welcome research talks and expository talks, by faculty, postdocs and students. You could tell us about your own latest paper, or something you’ve just read and found exciting. You could tell us the story of an interdisciplinary collaboration (what worked? what didn’t?). Or you might like to give a “What is…?”-style introduction to your broad area of research. To propose a talk, email Sjoerd, Darrick or Emily.
To join the mailing list, send an email to sympa at mlist.is.ed.ac.uk with nothing in the subject line and in the message body put the following:
SUBSCRIBE agate-seminar [your name]
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Autumn 2024 Talks
Path Signatures in Machine Learning
The path signature is a way to represent a path as an infinite sequence of tensors. We provide a high level introduction to signatures, highlighting the algebraic and geometric aspects of this construction, and discuss how this can be used to study sequences (time series) in machine learning.
Topological and Geometric Elements in Modern Deep Learning - Benefits and Challenges
This talk will take a simple introduction to machine learning, especially as used in computer vision. We then go on to see the different ways issues of geometry and topology turn up and are handled within the field. We examine the promise, in terms of generalisation, that building geometric understanding adds to a model. At the same time we recognise the challenges that imposing a rigid abstract geometry on a real world space can bring. I will give one example of our work decomposing structure and motion using a Hamiltonian model structure, before opening things up for discussion as to what the future opportunities are.
(Different Location: 5.45 Bayes Centre)
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(Different Time: 16:05 - 17:00)
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