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AIML Special Presentation: Explaining the Uncertain - Stochastic Shapley values for Gaussian process models
- Date: Mon, 20 Nov 2023
- Location: AIML
In the rapidly evolving field of machine learning, it is important to quantify model uncertainty and explain algorithm decisions, especially for safety-critical domains such as healthcare. In this talk, Dr Chau presented a novel approach to explaining Gaussian processes which we term GP-SHAP. our method is based on the popular solution concept of Shapley values extended to stochastic cooperative games, resulting in explanations that are random variables.
AIML Mid-week Seminar: Using AI to predict clinical outcomes in dementia
- Date: Wed, 15 Nov 2023
- Location: AIML
In this presentation, Luke discussed the use of artificial intelligence and deep learning to analyse brain images in dementia, cognitively impaired and healthy individuals. The goal is to develop representations of disease progression that can differentiate normal aging from specific pathologies at both group and individual levels. The presentation aims to bridge the gap between group and individual disease progression analyses by combining morphometry, normative, and computational disease modelling techniques. It also outlines various approaches and results obtained so far, with plans to develop comprehensive normative models. This research has the potential to improve dementia severity assessment and guide precision medicine and aged care planning. Future work will explore different patient categories and intervention approaches.
[Read more about AIML Mid-week Seminar: Using AI to predict clinical outcomes in dementia]
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