3 citations · 4 across the 16 of their papers we have counts for
7 papers · 1 filter
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.…
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…
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…
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…
TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text
Fady Medhat, Mahnaz Mohammadi, Sardar Jaf +6
Handling large corpuses of documents is of significant importance in many fields, no more so than in the areas of crime investigation and defence, where an organisation may be pres…
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…