118 citations · 216 across the 30 of their papers we have counts for
34 papers
A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway
Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen +5
Recent machine learning weather prediction (MLWP) models have demonstrated remarkable forecasting skill on global reanalysis-based benchmarks. However, their performance remains un…
Group-Equivariant Poincaré Convolutional Networks
Aiden Durrant, Rahul Baburajan, Georgios Leontidis
While recent methods like that of the Poincaré ResNet have demonstrated the ability to learning visual representations directly in hyperbolic space, their optimisation remains a ch…
CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework
Steven Wallace, William D. Harcourt, Richard Hann +3
Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain. Yet, pixel-level annotation of glacier sur…
Causal State-Space Model for Causal Inference: Estimating Longitudinal Individual Treatment Effects
Abisoye Abidakun, Mingjun Zhong, Georgios Leontidis
Estimating counterfactual outcomes over time from longitudinal observational data is central to clinical decision support. Existing methods rely on domain confusion -- adversarial…
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…
NOFE - Neural Operator Function Embedding
Lars Uebbing, Harald L. Joakimsen, Siyan Chen +6
Most dimensionality reduction methods treat data as discrete point clouds, ignoring the continuous domain structure inherent to many real-world processes. To bridge this gap, we in…
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…
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…
The Impact of Semantic Pairs on Self-Supervised Representation Learning
Mohammad Alkhalefi, Georgios Leontidis, Mingjun Zhong
Instance discrimination learns visual representations by treating different augmented views of the same image as positive pairs. While this encourages invariance to handcrafted tra…
Getting the Numbers Right$\unicode{x2014}$Modelling Multi-Class Object Counting in Dense and Varied Scenes
Villanelle O'Reilly, Jonathan Cox, Georgios Leontidis +3
Density map estimation enables accurate object counting in heavily occluded, and densely packed scenes where detection-based counting fails. In multi-class density estimation, clas…
EquiCaps: Predictor-Free Pose-Aware Pre-Trained Capsule Networks
Athinoulla Konstantinou, Georgios Leontidis, Mamatha Thota +1
Learning self-supervised representations that are invariant and equivariant to transformations is crucial for advancing beyond traditional visual classification tasks. However, man…
Multimodal Federated Learning With Missing Modalities through Feature Imputation Network
Pranav Poudel, Aavash Chhetri, Prashnna Gyawali +2
Multimodal federated learning holds immense potential for collaboratively training models from multiple sources without sharing raw data, addressing both data scarcity and privacy…
Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning
Tewodros Alemu Ayall, Andy Li, Matthew Beddows +2
Rapid global population growth underscores the need for digitally enabled agricultural systems that support sustainable food production and data-driven resource management for farm…
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…
Investigating the Capabilities of Deep Learning for Processing and Interpreting One-Shot Multi-offset GPR Data: A Numerical Case Study for Lunar and Martian Environments
Iraklis Giannakis, Craig Warren, Antonios Giannopoulos +5
Ground-penetrating radar (GPR) is a mature geophysical method that has gained increasing popularity in planetary science over the past decade. GPR has been utilised both for Lunar…
Capsule Network Projectors are Equivariant and Invariant Learners
Miles Everett, Aiden Durrant, Mingjun Zhong +1
Learning invariant representations has been the long-standing approach to self-supervised learning. However, recently progress has been made in preserving equivariant properties in…
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations
Mohammad Alkhalefi, Georgios Leontidis, Mingjun Zhong
Contrastive instance discrimination methods outperform supervised learning in downstream tasks such as image classification and object detection. However, these methods rely heavil…
Masked Capsule Autoencoders
Miles Everett, Mingjun Zhong, Georgios Leontidis
We propose Masked Capsule Autoencoders (MCAE), the first Capsule Network that utilises pretraining in a modern self-supervised paradigm, specifically the masked image modelling fra…
Tabular Machine Learning Methods for Predicting Gas Turbine Emissions
Rebecca Potts, Rick Hackney, Georgios Leontidis
Predicting emissions for gas turbines is critical for monitoring harmful pollutants being released into the atmosphere. In this study, we evaluate the performance of machine learni…
ProtoCaps: A Fast and Non-Iterative Capsule Network Routing Method
Miles Everett, Mingjun Zhong, Georgios Leontidis
Capsule Networks have emerged as a powerful class of deep learning architectures, known for robust performance with relatively few parameters compared to Convolutional Neural Netwo…
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination Methods
Mohammad Alkhalefi, Georgios Leontidis, Mingjun Zhong
Self-supervised learning algorithms (SSL) based on instance discrimination have shown promising results, performing competitively or even outperforming supervised learning counterp…
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes
Aiden Durrant, Georgios Leontidis
Hyperbolic manifolds for visual representation learning allow for effective learning of semantic class hierarchies by naturally embedding tree-like structures with low distortion w…
Vanishing Activations: A Symptom of Deep Capsule Networks
Miles Everett, Mingjun Zhong, Georgios Leontidis
Capsule Networks, an extension to Neural Networks utilizing vector or matrix representations instead of scalars, were initially developed to create a dynamic parse tree where visua…
S-JEA: Stacked Joint Embedding Architectures for Self-Supervised Visual Representation Learning
Alžběta Manová, Aiden Durrant, Georgios Leontidis
The recent emergence of Self-Supervised Learning (SSL) as a fundamental paradigm for learning image representations has, and continues to, demonstrate high empirical success in a v…