9 papers
Group-Equivariant Poincaré Convolutional Networks
Aiden Durrant, Rahul Baburajan, Georgios Leontidis
While recent advancements like the Poincaré ResNet have demonstrated the potential of learning visual representations directly in hyperbolic space, their optimisation remains hamp…
HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers
Andy Li, Aiden Durrant, Milan Markovic +1
Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on resource-constraint hardware. Most structured pruning me…
HANCLIP: A Family of Hyperbolic Angular Negation Vision Language Models
Hoang-Bao Le, Aiden Durrant, Thai Son Mai +3
Vision-Language Models (VLMs) are typically pre-trained on large-scale image-text datasets to capture semantic correspondences between visual content and natural language. However,…
Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data
Rebecca Potts, Aiden Durrant, Rick Hackney +1
Machine learning-based predictive emissions monitoring systems offer a practical alternative to direct emissions measurement, but their deployment across gas turbine fleets is chal…
Agent-Based Post-Hoc Correction of Agricultural Yield Forecasts
Matthew Beddows, Aiden Durrant, Georgios Leontidis
Accurate crop yield forecasting in commercial soft fruit production is constrained by the data available in typical commercial farm records, which lack the sensor networks, satelli…
Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning
Andy Li, Aiden Durrant, Milan Markovic +5
Pruning of deep neural networks has been an effective technique for reducing model size while preserving most of the performance of dense networks, crucial for deploying models on…