Outputs (2)

Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions (2019)
Presentation / Conference Contribution
Bonner, S., Atapour-Abarghouei, A., Jackson, P., Brennan, J., Kureshi, I., Theodoropoulos, G., McGough, S., & Obara, B. (2019, December). Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions. Presented at IEEE International Conference on Big Data (Deep Graph Learning: Methodologies and Applications), Los Angeles, CA, USA

Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to machine learning models, this rich temporal information is frequently di... Read More about Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions.

Style Augmentation: Data Augmentation via Style Randomization (2019)
Presentation / Conference Contribution
Jackson, P., Atapour-Abarghouei, A., Bonner, S., Breckon, T., & Obara, B. (2019, June). Style Augmentation: Data Augmentation via Style Randomization. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition, Deep Vision, Long Beach, CA, USA

We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of Convolutional Neural Networks (CNN) over both classification and regression based tasks. During training, style augmentat... Read More about Style Augmentation: Data Augmentation via Style Randomization.