Thu, 22 Oct 2020

16:15 - 17:00
Virtual

The C*-algebras associated to a Wieler solenoid

Robin Deeley
(University of Colorado Boulder)
Further Information

Part of UK virtual operator algebras seminar: https://sites.google.com/view/uk-operator-algebras-seminar/home

Abstract

Wieler has shown that every irreducible Smale space with totally disconnected stable sets is a solenoid (i.e., obtained via a stationary inverse limit construction). Through examples I will discuss how this allows one to compute the K-theory of the stable algebra, S, and the stable Ruelle algebra, S\rtimes Z. These computations involve writing S as a stationary inductive limit and S\rtimes Z as a Cuntz-Pimsner algebra. These constructions reemphasize the view point that Smale space C*-algebras are higher dimensional generalizations of Cuntz-Krieger algebras. The main results are joint work with Magnus Goffeng and Allan Yashinski.

Thu, 22 Oct 2020

15:30 - 16:15
Virtual

Von Neumann algebras and equivalences between groups

Lauren Ruth
(Mercy College)
Further Information

Part of UK virtual operator algebras seminar: https://sites.google.com/view/uk-operator-algebras-seminar/home

Abstract

We have various ways of describing the extent to which two countably infinite groups are "the same." Are they isomorphic? If not, are they commensurable? Measure equivalent? Quasi-isometric? Orbit equivalent? W*-equivalent? Von Neumann equivalent? In this expository talk, we will define these notions of equivalence, discuss the known relationships between them, and work out some examples. Along the way, we will describe recent joint work with Ishan Ishan and Jesse Peterson.

Thu, 26 Nov 2020

16:00 - 17:00
Virtual

On the Happy Marriage of Kernel Methods and Deep Learning

Julien Mairal
(Inria Grenoble)
Further Information

datasig.ox.ac.uk/events

Abstract

In this talk, we present simple ideas to combine nonparametric approaches based on positive definite kernels with deep learning models. There are many good reasons for bridging these two worlds. On the one hand, we want to provide regularization mechanisms and a geometric interpretation to deep learning models, as well as a functional space that allows to study their theoretical properties (eg invariance and stability). On the other hand, we want to bring more adaptivity and scalability to traditional kernel methods, which are crucially lacking. We will start this presentation by introducing models to represent graph data, then move to biological sequences, and images, showing that our hybrid models can achieves state-of-the-art results for many predictive tasks, especially when large amounts of annotated data are not available. This presentation is based on joint works with Alberto Bietti, Dexiong Chen, and Laurent Jacob.

Fri, 25 Jun 2021

11:45 - 13:15
Virtual

InFoMM CDT Group Meeting

Joel Dyer, Constantin Puiu, Markus Dablander
(Mathematical Institute)
Fri, 28 May 2021

11:45 - 13:15
Virtual

InFoMM CDT Group Meeting

Anna Berryman, Georgia Brennan, Matthew Shirley,
(Mathematical Institute)
Fri, 30 Apr 2021

11:45 - 13:15
Virtual

InFoMM CDT Group Meeting

Giancarlo Antonucci, Thomas Babb, Yu Tian, Sophie Abrahams
(Mathematical Institute)
Fri, 26 Mar 2021

11:45 - 13:15
Virtual

InFoMM CDT Group Meeting

Huining Yang, Deqing Jiang, Joe Roberts
(Mathematical Institute)
Fri, 26 Feb 2021

11:45 - 13:15
Virtual

InFoMM CDT Group Meeting

Zhen Shao, John Fitzgerald, Brady Metherall, James Harris
(Mathematical Institute)
Fri, 29 Jan 2021

11:45 - 13:15
Virtual

InFoMM CDT Group Meeting

Rodrigo Leal Cervantes, Isabelle Scott, Meredith Ellis, Oliver Bond
(Mathematical Institute)
Fri, 11 Dec 2020

11:45 - 13:15
Virtual

InFoMM CDT Group Meeting

Harry Renolds, Lingyi Yang, Alexandru Puiu, Arkady Wey
(Mathematical Institute)
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