233 citations · 821 across the 23 of their papers we have counts for
7 papers · 1 filter
Predicting the Computational Cost of Deep Learning Models
Daniel Justus, John Brennan, Stephen Bonner +1
Deep learning is rapidly becoming a go-to tool for many artificial intelligence problems due to its ability to outperform other approaches and even humans at many problems. Despite…
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning
Stephen Bonner, John Brennan, Ibad Kureshi +3
Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning…
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments
A. Stephen McGough, Matthew Forshaw, John Brennan +2
High Throughput Computing (HTC) provides a convenient mechanism for running thousands of tasks. Many HTC systems exploit computers which are provisioned for other purposes by utili…
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
David Rohde, Stephen Bonner, Travis Dunlop +2
Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to frien…
Style Augmentation: Data Augmentation via Style Randomization
Philip T. Jackson, Amir Atapour-Abarghouei, Stephen Bonner +2
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 class…
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
Stephen Bonner, Ibad Kureshi, John Brennan +3
Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation,…