Search Results for author: Ryan McConville

Found 21 papers, 7 papers with code

Unsupervised Optimisation of GNNs for Node Clustering

no code implementations12 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.

Clustering graph partitioning +2

A Framework for Exploring Federated Community Detection

no code implementations14 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.

Community Detection Federated Learning

Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection

no code implementations14 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.

Community Detection Task 2

The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Management

1 code implementation23 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.

Management

Multimodal Indoor Localisation in Parkinson's Disease for Detecting Medication Use: Observational Pilot Study in a Free-Living Setting

1 code implementation3 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.

Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detection

no code implementations10 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.

Benchmarking Community Detection

Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda

no code implementations5 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.

Misinformation

Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers

no code implementations12 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.

Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes

1 code implementation7 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).

Offline RL Reinforcement Learning (RL)

Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills

no code implementations16 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.

reinforcement-learning Reinforcement Learning (RL)

OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors

1 code implementation8 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.

Human Activity Recognition Multimodal Activity Recognition

Photos Are All You Need for Reciprocal Recommendation in Online Dating

no code implementations26 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.

Collaborative Filtering Recommendation Systems

Self-Supervised WiFi-Based Activity Recognition

no code implementations19 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.

Activity Recognition Contrastive Learning +1

PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance

no code implementations28 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.

Perceptual Distance

N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding

5 code implementations16 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.

Clustering Deep Clustering +4

Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance

no code implementations9 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.

Image Segmentation Image-to-Image Translation +2

Cannot find the paper you are looking for? You can Submit a new open access paper.