no code implementations • 12 Feb 2024 • William Leeney, Ryan McConville
Although modularity is a graph partitioning quality metric, we show that this can be used to optimise GNNs that also encode features without a drop in performance.
no code implementations • 14 Dec 2023 • William Leeney, Ryan McConville
(1) The enhanced capability of Graph Neural Networks (GNNs) in unsupervised community detection of clustered nodes is attributed to their capacity to encode both the connectivity and feature information spaces of graphs.
no code implementations • 14 Dec 2023 • William Leeney, Ryan McConville
Federated Learning is machine learning in the context of a network of clients whilst maintaining data residency and/or privacy constraints.
1 code implementation • 23 Oct 2023 • Harry Emerson, Ryan McConville, Matthew Guy
This work explores the implications of using deep learning algorithms trained on real-world data to model glucose dynamics.
1 code implementation • 3 Aug 2023 • Ferdian Jovan, Catherine Morgan, Ryan McConville, Emma L. Tonkin, Ian Craddock, Alan Whone
A sub-objective aims to evaluate whether indoor localisation, including its in-home gait speed features (i. e. the time taken to walk between rooms), could be used to evaluate motor fluctuations by detecting whether the person with PD is taking levodopa medications or withholding them.
no code implementations • 20 May 2023 • Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane, Armand K. Koupai, Mohammud J. Bocus, Raúl Santos-Rodríguez, Robert J. Piechocki, Ryan McConville
This work studies the impact of privacy in federated HAR at a user, environment, and sensor level.
no code implementations • 10 May 2023 • William Leeney, Ryan McConville
We find that by ensuring the same evaluation criteria is followed, there may be significant differences from the reported performance of methods at this task, but a more complete evaluation and comparison of methods is possible.
no code implementations • 5 Oct 2022 • Andrés Domínguez Hernández, Richard Owen, Dan Saattrup Nielsen, Ryan McConville
We conclude by offering a tentative path toward reflexive and responsible development of ML tools for moderating misinformation and other harmful content online.
no code implementations • 15 Aug 2022 • Armand K. Koupai, Mohammud J. Bocus, Raul Santos-Rodriguez, Robert J. Piechocki, Ryan McConville
We first propose the Fusion Transformer, an attention-based model for multimodal and multi-sensor fusion.
no code implementations • 12 May 2022 • Ferdian Jovan, Ryan McConville, Catherine Morgan, Emma Tonkin, Alan Whone, Ian Craddock
We use data collected from 10 people with Parkinson's, and 10 controls, each of whom lived for five days in a smart home with various sensors.
1 code implementation • 7 Apr 2022 • 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).
3 code implementations • 23 Feb 2022 • Dan Saattrup Nielsen, Ryan McConville
Training these machine learning models require datasets of sufficient scale, diversity and quality.
Ranked #1 on Node Classification on MuMiN-small
no code implementations • 16 Nov 2021 • Taku Yamagata, Ryan McConville, Raul Santos-Rodriguez
We empirically show that our approach can accurately learn the reliability of each trainer correctly and use it to maximise the information gained from the multiple trainers' feedback, even if some of the sources are adversarial.
1 code implementation • 8 Oct 2021 • Mohammud J. Bocus, Wenda Li, Shelly Vishwakarma, Roget Kou, Chong Tang, Karl Woodbridge, Ian Craddock, Ryan McConville, Raul Santos-Rodriguez, Kevin Chetty, Robert Piechocki
This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities.
no code implementations • 26 Aug 2021 • James Neve, Ryan McConville
Reciprocal Recommenders are a subset of recommender systems, where the items in question are people, and the objective is therefore to predict a bidirectional preference relation.
no code implementations • 19 Apr 2021 • Hok-Shing Lau, Ryan McConville, Mohammud J. Bocus, Robert J. Piechocki, Raul Santos-Rodriguez
Traditional approaches to activity recognition involve the use of wearable sensors or cameras in order to recognise human activities.
no code implementations • 28 Oct 2019 • Alexander Hepburn, Valero Laparra, Jesús Malo, Ryan McConville, Raul Santos-Rodriguez
Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations.
no code implementations • 16 Aug 2019 • Taku Yamagata, Raúl Santos-Rodríguez, Ryan McConville, Atis Elsts
However, few studies have considered the balance between wearable power consumption and activity recognition accuracy.
5 code implementations • 16 Aug 2019 • Ryan McConville, Raul Santos-Rodriguez, Robert J. Piechocki, Ian Craddock
We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is best able to find the most clusterable manifold in the embedding, suggesting local manifold learning on an autoencoded embedding is effective for discovering higher quality discovering clusters.
Ranked #1 on Image Clustering on pendigits
no code implementations • 9 Aug 2019 • Alexander Hepburn, Valero Laparra, Ryan McConville, Raul Santos-Rodriguez
While an important part of the evaluation of the generated images usually involves visual inspection, the inclusion of human perception as a factor in the training process is often overlooked.
2 code implementations • 25 Jun 2018 • Ryan McConville, Gareth Archer, Ian Craddock, Herman ter Horst, Robert Piechocki, James Pope, Raul Santos-Rodriguez
In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable.