4 papers · 1 filter
Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models
Nicholas I-Hsien Kuo, Louisa Jorm, Sebastiano Barbieri
This paper presents a novel approach to simulating electronic health records (EHRs) using diffusion probabilistic models (DPMs). Specifically, we demonstrate the effectiveness of D…
The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms
Nicholas I-Hsien Kuo, Mark N. Polizzotto, Simon Finfer +6
In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not ope…
Learning to Continually Learn Rapidly from Few and Noisy Data
Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier +3
Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution. Continual learning could be…
MTL2L: A Context Aware Neural Optimiser
Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier +3
Learning to learn (L2L) trains a meta-learner to assist the learning of a task-specific base learner. Previously, it was shown that a meta-learner could learn the direct rules to u…