6 citations · 7 across the 7 of their papers we have counts for
7 papers
Unsupervised Optimisation of GNNs for Node Clustering
William Leeney, Ryan McConville
Graph Neural Networks (GNNs) can be trained to detect communities within a graph by learning from the duality of feature and connectivity information. Currently, the common approac…
Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection
William Leeney, Ryan McConville
(1) The enhanced capability of Graph Neural Networks (GNNs) in unsupervised community detection of clustered nodes is attributed to their capacity to encode both the connectivity a…
The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Management
Harry Emerson, Ryan McConville, Matthew Guy
Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D) management strategies to be evaluated without patient harm. Deep learning algorithms provide a promising…
Multimodal Indoor Localisation in Parkinson's Disease for Detecting Medication Use: Observational Pilot Study in a Free-Living Setting
Ferdian Jovan, Catherine Morgan, Ryan McConville +3
Parkinson's disease (PD) is a slowly progressive, debilitating neurodegenerative disease which causes motor symptoms including gait dysfunction. Motor fluctuations are alterations…
Privacy in Multimodal Federated Human Activity Recognition
Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane +5
Human Activity Recognition (HAR) training data is often privacy-sensitive or held by non-cooperative entities. Federated Learning (FL) addresses such concerns by training ML models…
Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognition
Armand K. Koupai, Mohammud J. Bocus, Raul Santos-Rodriguez +2
The pervasiveness of Wi-Fi signals provides significant opportunities for human sensing and activity recognition in fields such as healthcare. The sensors most commonly used for pa…