MDL Follow on: LMS Invited Lectures on the Mathematics of Deep Learning
MDLW06
28 February 2022 to 4 March 2022
Workshop theme
Background
This workshop will feature an introductory lecture series across the week, by Professor Gitta Kutyniok (LMU, Munich) on the mathematics of deep learning. Four accompanying lectures will also be given.
Neural networks were originally introduced in 1943 by McCulloch and Pitts as an approach to develop learning algorithms by mimicking the human brain. The key goal at that time was the introduction of a theory of artificial intelligence. However, the limited amount of data and the lack of high-performance computers made the training of deep neural networks, (networks with many layers), unfeasible.
Today, massive amounts of training data are available complemented by a tremendously increased computing power, allowing for the first time the application of deep learning algorithms. It is for this reason that deep neural networks have recently seen an impressive comeback. Spectacular applications of deep learning are AlphaGo, which for the first time enabled a computer to beat the top world players in the game Go - a game by far more complex than chess-, or the speech recognition systems available on each smartphone these days.
We currently witness how algorithms based on deep neural networks are used in numerous aspects of the public sector such as being used for pre-screening job applications or revolutionising the healthcare industry. In fact, the U.S. Food and Drug Administration (FDA) has already approved the marketing of the first medical device for detecting diabetic retinopathy which is based on such methodologies.
Aims and Objectives
The workshop will aim to provide a mathematical foundation of deep learning by an introduction to the main mathematical questions and concepts of deep neural networks and their training within two realms:
Theoretical foundations of deep learning independent of a particular application
Theoretical analysis of the potential and the limitations of deep learning for mathematical methodologies, in particular, for inverse problems and partial differential equations.
The Programme is currently being developed and will feature 2 lectures each day as well as the opportunity for discussion and networking. A poster session will also take place.
More information about the background and the event can be seen here.
Posters
There will be the opportunity to present a poster. If you would like to express an interest in presenting a poster, please indicate this on the registration form and submit a short extract.
To express your interest in registering, please complete the form here
Applications for this workshop are now closed
- Fees
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Registration
We are planning to run this as a hybrid event with a limited number of physical places. Please indicate whether you are planning to participate in person or remotely when registering.
A nominal registration fee is charged for attendance:
Student Registration £25.00
Other Academics £50.00
Industry Participant £100.00We aim to offer financial support for early-career researchers to attend this meeting. If you would like to be considered for this, please include this in your registration.
Formal Dinner Only
- Formal Dinner: £55
Participants on the Accommodation Package or Registration Package, including organisers and speakers, are automatically included in this event. For all remaining participants who would like to attend, such as programme participants, the above charge will apply.
- Accommodation
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Unfortunately we do not have any accommodation to offer so all successful applicants will need to source their own accommodation.
Please see the Hotels Combined website for a list of local hotels and guesthouses. - Meals
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Lunch Lunch timings and location will be confirmed with timetable.
Evening Meal Participants are free to make their own arrangements for dinner.
Formal Dinner The Formal Dinner location and date is to be confirmed.
Photo Gallery
Organisers
- Carola-Bibiane Schönlieb University of Cambridge
Supported By
