25 citations · 82 across the 37 of their papers we have counts for
39 papers
RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization
Yan Lin, Ziheng Wang, Shuang Chen +2
Medical image classifiers are often trained within one source population, yet clinical deployment requires robustness to patients whose appearance, acquisition style, and disease p…
My Emotion on your face: The use of Facial Keypoint Detection to preserve Emotions in Latent Space Editing
Jingrui He, Andrew Stephen McGough
Generative Adversarial Network approaches such as StyleGAN/2 provide two key benefits: the ability to generate photo-realistic face images and possessing a semantically structured…
O-TALC: Steps Towards Combating Oversegmentation within Online Action Segmentation
Matthew Kent Myers, Nick Wright, A. Stephen McGough +1
Online temporal action segmentation shows a strong potential to facilitate many HRI tasks where extended human action sequences must be tracked and understood in real time. Traditi…
Insights from the Use of Previously Unseen Neural Architecture Search Datasets
Rob Geada, David Towers, Matthew Forshaw +2
The boundless possibility of neural networks which can be used to solve a problem -- each with different performance -- leads to a situation where a Deep Learning expert is require…
The Effects of Signal-to-Noise Ratio on Generative Adversarial Networks Applied to Marine Bioacoustic Data
Georgia Atkinson, Nick Wright, A. Stephen McGough +1
In recent years generative adversarial networks (GANs) have been used to supplement datasets within the field of marine bioacoustics. This is driven by factors such as the cost to…
The Forecastability of Underlying Building Electricity Demand from Time Series Data
Mohamad Khalil, A. Stephen McGough, Hussain Kazmi +1
Forecasting building energy consumption has become a promising solution in Building Energy Management Systems for energy saving and optimization. Furthermore, it can play an import…
How much data do I need? A case study on medical data
Ayse Betul Cengiz, A. Stephen McGough
The collection of data to train a Deep Learning network is costly in terms of effort and resources. In many cases, especially in a medical context, it may have detrimental impacts.…
NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies
Atif Khan, Conor Lawless, Amy Vincent +4
Single cell analysis of human skeletal muscle (SM) tissue cross-sections is a fundamental tool for understanding many neuromuscular disorders. For this analysis to be reliable and…
Introducing NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies
Atif Khan, Conor Lawless, Amy Vincent +4
Single cell analysis of skeletal muscle (SM) tissue is a fundamental tool for understanding many neuromuscular disorders. For this analysis to be reliable and reproducible, identif…
Predicting the Performance of a Computing System with Deep Networks
Mehmet Cengiz, Matthew Forshaw, Amir Atapour-Abarghouei +1
Predicting the performance and energy consumption of computing hardware is critical for many modern applications. This will inform procurement decisions, deployment decisions, and…
Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets
Michael Luke Battle, Amir Atapour-Abarghouei, Andrew Stephen McGough
Skin cancer is the most common malignancy in the world. Automated skin cancer detection would significantly improve early detection rates and prevent deaths. To help with this aim,…
Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology
Cameron Trotter, Nick Wright, A. Stephen McGough +6
Photo-identification (photo-id) is one of the main non-invasive capture-recapture methods utilised by marine researchers for monitoring cetacean (dolphin, whale, and porpoise) popu…
Explainable Deep Learning to Profile Mitochondrial Disease Using High Dimensional Protein Expression Data
Atif Khan, Conor Lawless, Amy E Vincent +3
Mitochondrial diseases are currently untreatable due to our limited understanding of their pathology. We study the expression of various mitochondrial proteins in skeletal myofibre…
Analysis of Reinforcement Learning for determining task replication in workflows
Andrew Stephen McGough, Matthew Forshaw
Executing workflows on volunteer computing resources where individual tasks may be forced to relinquish their resource for the resource's primary use leads to unpredictability and…
Long-term Reproducibility for Neural Architecture Search
David Towers, Matthew Forshaw, Amir Atapour-Abarghouei +1
It is a sad reflection of modern academia that code is often ignored after publication -- there is no academic 'kudos' for bug fixes / maintenance. Code is often unavailable or, if…
Machine learning applications for electricity market agent-based models: A systematic literature review
Alexander J. M. Kell, Stephen McGough, Matthew Forshaw
The electricity market has a vital role to play in the decarbonisation of the energy system. However, the electricity market is made up of many different variables and data inputs.…
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics
Rob Geada, Andrew Stephen McGough
Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a vari…
Application of deep learning to camera trap data for ecologists in planning / engineering -- Can captivity imagery train a model which generalises to the wild?
Ryan Curry, Cameron Trotter, Andrew Stephen McGough
Understanding the abundance of a species is the first step towards understanding both its long-term sustainability and the impact that we may be having upon it. Ecologists use came…
The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets
Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw
Electricity supply must be matched with demand at all times. This helps reduce the chances of issues such as load frequency control and the chances of electricity blackouts. To gai…
Resolving the cybersecurity Data Sharing Paradox to scale up cybersecurity via a co-production approach towards data sharing
Amir Atapour-Abarghouei, Andrew Stephen McGough, David Stanley Wall
As cybercriminals scale up their operations to increase their profits or inflict greater harm, we argue that there is an equal need to respond to their threats by scaling up cybers…
Exploring market power using deep reinforcement learning for intelligent bidding strategies
Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough
Decentralized electricity markets are often dominated by a small set of generator companies who control the majority of the capacity. In this paper, we explore the effect of the to…
Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs
Alexander J. M. Kell, Pablo Salas, Jean-Francois Mercure +2
A change from a high-carbon emitting electricity power system to one based on renewables would aid in the mitigation of climate change. Decarbonization of the electricity grid woul…
Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks
John Brennan, Stephen Bonner, Amir Atapour-Abarghouei +3
With the growing significance of graphs as an effective representation of data in numerous applications, efficient graph analysis using modern machine learning is receiving a growi…
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners
Rob Geada, Dennis Prangle, Andrew Stephen McGough
One-shot Neural Architecture Search (NAS) aims to minimize the computational expense of discovering state-of-the-art models. However, in the past year attention has been drawn to t…