AI-Fun Seminar | Fengxiang He: Building Up Deep Learning Theory: Perspectives of Optimisation and Loss Landscape
| Dates: | 19 June 2024 |
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| Times: | 10:30 - 11:30 |
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| What is it: | Seminar |
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| Organiser: | Faculty of Science and Engineering |
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| Who is it for: | University staff, External researchers, Current University students |
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| Speaker: | Fengxiang He |
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The Manchester Centre for AI Fundamentals is hosting a series of seminars featuring expert researchers working in the fundamentals of AI and our speaker on 19 June is Dr Fengxiang He.
Title:
Building Up Deep Learning Theory: Perspectives of Optimisation and Loss Landscape
Abstract:
Deep learning has long been criticised as a ‘black-box’ model. A major problem is why deep neural networks are over-parameterised, but can have good generalisability. In this talk, I will give a brief overview of efforts at building up deep learning theory, and my works from the perspectives of optimisation dynamics and the algebraic/geometric structures of deep neural networks’ loss landscape.
Fengxiang He is a Lecturer at the School of Informatics, University of Edinburgh. His research interest is in trustworthy AI, particularly deep learning theory and explainability, theory of decentralised learning, privacy in machine learning, symmetry in machine learning, learning theory in game-theoretical problems, and their applications in economics. He is an Area Chair of ICML, NeurIPS, UAI, AISTATS, and ACML.
All are welcome: in-person attendance is preferred but if you are unable to be there you can join online via the link opposite.
Speaker
Fengxiang He
Role: Lecturer
Organisation: University of Edinburgh
Travel and Contact Information
Find event
6.207
University Place
Manchester
