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Understanding neural networks and quantification of their uncertainty via exactly solvable models
This talk is the annual Oxford Maths & Stats Colloquium. There will be a Drinks Reception after the talk in the ground floor social area.
The affinity between statistical physics and machine learning has a long history. Theoretical physics often proceeds in terms of solvable synthetic models; I will describe the related line of work on solvable models of simple feed-forward neural networks. I will then discuss how this approach allows us to analyze uncertainty quantification in neural networks, a topic that gained urgency in the dawn of widely deployed artificial intelligence. I will conclude with what I perceive as important specific open questions in the field.
Date:
5 May 2023, 15:30
Venue:
24-29 St Giles', 24-29 St Giles' OX1 3LB
Venue Details:
Large Lecture Theatre, Department of Statistics
Speaker:
Professor Lenka Lenka Zdeborová (École Polytechnique Fédérale de Lausanne)
Organising department:
Department of Statistics
Organisers:
Beverley Lane (Department of Statistics, University of Oxford),
Professor Simon Myers (University of Oxford)
Organiser contact email address:
events@stats.ox.ac.uk
Host:
Professor Simon Myers (University of Oxford)
Booking required?:
Required
Booking url:
https://forms.office.com/e/Nw3qSZtzCs
Booking email:
events@stats.ox.ac.uk
Audience:
Members of the University only
Editor:
Beverley Lane
