2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2022
Evaluation of the Synthetic Electronic Health Records
Emily Muller, Xu Zheng, Jer Hayes
Generative models have been found effective for data synthesis due to their ability to capture complex underlying data distributions. The quality of generated data from these model…
cs.LG2022★ 2 cited
Synthesising Electronic Health Records: Cystic Fibrosis Patient Group
Emily Muller, Xu Zheng, Jer Hayes
Class imbalance can often degrade predictive performance of supervised learning algorithms. Balanced classes can be obtained by oversampling exact copies, with noise, or interpolat…
cs.CV2019
STaDA: Style Transfer as Data Augmentation
Xu Zheng, Tejo Chalasani, Koustav Ghosal +2
The success of training deep Convolutional Neural Networks (CNNs) heavily depends on a significant amount of labelled data. Recent research has found that neural style transfer alg…