16 citations · 80 across the 20 of their papers we have counts for
10 papers · 1 filter
Transfer Learning of RSSI to Improve Indoor Localisation Performance
Thanaphon Suwannaphong, Ryan McConville, Ian Craddock
With the growing demand for health monitoring systems, in-home localisation is essential for tracking patient conditions. The unique spatial characteristics of each house required…
Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices
Thanaphon Suwannaphong, Ferdian Jovan, Ian Craddock +1
This paper proposes small and efficient machine learning models (TinyML) for resource-constrained edge devices, specifically for on-device indoor localisation. Typical approaches f…
Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detection
William Leeney, Ryan McConville
Graph Neural Networks (GNNs) have improved unsupervised community detection of clustered nodes due to their ability to encode the dual dimensionality of the connectivity and featur…
Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers
Ferdian Jovan, Ryan McConville, Catherine Morgan +3
Parkinson's disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including number…
Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes
Harry Emerson, Matthew Guy, Ryan McConville
The widespread adoption of effective hybrid closed loop systems would represent an important milestone of care for people living with type 1 diabetes (T1D). These devices typically…
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset
Dan Saattrup Nielsen, Ryan McConville
Misinformation is becoming increasingly prevalent on social media and in news articles. It has become so widespread that we require algorithmic assistance utilising machine learnin…
Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills
Taku Yamagata, Ryan McConville, Raul Santos-Rodriguez
A promising approach to improve the robustness and exploration in Reinforcement Learning is collecting human feedback and that way incorporating prior knowledge of the target envir…
PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance
Alexander Hepburn, Valero Laparra, Jesús Malo +2
Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was us…
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback
Taku Yamagata, Raúl Santos-Rodríguez, Ryan McConville +1
Recent advances in both machine learning and Internet-of-Things have attracted attention to automatic Activity Recognition, where users wear a device with sensors and their outputs…
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
Ryan McConville, Raul Santos-Rodriguez, Robert J Piechocki +1
Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. Deep clustering algorithms usually combine representation learning…