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Current federated learning architectures in mobile healthcare are limited to a centralised model without considering the full continuum of mobile-edge-cloud. Additionally, to support different data
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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model with each SNP independently, perhaps adjusting for other covariates such as age and sex. This project will focus on developing and applying novel machine learning and AI methods to improve
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financial, personal, and confidential information. This project seeks to introduce machine learning and artificial intelligence techniques to effectively detect phishing websites. By leveraging these advanced
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Machine Learning without Centralized Training Data”, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html [2] “Learning with Privacy at Scale”, https://machinelearning.apple.com/research
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This research project aims to address the critical need for privacy-enhancing techniques in machine learning (ML) applications, particularly in scenarios involving sensitive or confidential data
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This PhD project aims to mitigate the data scarcity of new NLP and Multimodal applications by developing novel active learning algorithms. In this project, the student will leverage large foundation
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machine learning can be used to understand complex behaviours such as reliability, competence, transparency and trust. The position will design and develop innovative reasoning algorithms and knowledge
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Play is fundamental and one of the main methods children learn skills, gain independence, and practice daily tasks that lead into adulthood. TechToy Library is an innovative, co-designed initiative
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conventional perception pipelines towards AI systems that can observe, remember, anticipate, and act. The project will examine questions around multimodal representation learning, temporal modelling, predictive