2 citations · 2 across the 19 of their papers we have counts for
21 papers
From Regional to Global: Transfer Learning for Atmospheric Transport Emulators
Jeff Clark, Elena Fillola, Nawid Keshtmand +2
Greenhouse gas emissions estimates can be derived using inverse methods by combining atmospheric concentration observations with chemical transport models. The latter traditionally…
Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics
Yuxuan Ye, Raul Santos-Rodriguez, Edwin Simpson
Reinforcement learning with evaluation metrics as rewards is widely used to enhance specific capabilities of language models. However, for tasks such as factually consistent summar…
Learning Generation Orders for Masked Discrete Diffusion Models via Variational Inference
David Fox, Sam Bowyer, Song Liu +3
Masked discrete diffusion models (MDMs) are a promising new approach to generative modelling, offering the ability for parallel token generation and therefore greater efficiency th…
Machine Learning for Climate Policy: Understanding Policy Progression in the European Green Deal
Patricia West, Michelle WL Wan, Alexander Hepburn +3
Climate change demands effective legislative action to mitigate its impacts. This study explores the application of machine learning (ML) to understand the progression of climate p…
Uncertainty assessment in satellite-based greenhouse gas emissions estimates using emulated atmospheric transport
Jeffrey N. Clark, Elena Fillola, Nawid Keshtmand +2
Monitoring greenhouse gas emissions and evaluating national inventories require efficient, scalable, and reliable inference methods. Top-down approaches, combined with recent advan…
Prototype-enhanced prediction in graph neural networks for climate applications
Nawid Keshtmand, Elena Fillola, Jeffrey Nicholas Clark +2
Data-driven emulators are increasingly being used to learn and emulate physics-based simulations, reducing computational expense and run time. Here, we present a structured way to…
Improving Local Air Quality Predictions Using Transfer Learning on Satellite Data and Graph Neural Networks
Finn Gueterbock, Raul Santos-Rodriguez, Jeffrey N. Clark
Air pollution is a significant global health risk, contributing to millions of premature deaths annually. Nitrogen dioxide (NO2), a harmful pollutant, disproportionately affects ur…
Exploring the Requirements of Clinicians for Explainable AI Decision Support Systems in Intensive Care
Jeffrey N. Clark, Matthew Wragg, Emily Nielsen +7
There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and…
Explainable AI for Classifying UTI Risk Groups Using a Real-World Linked EHR and Pathology Lab Dataset
Yujie Dai, Brian Sullivan, Axel Montout +8
The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces challenges due to data heterog…
Evaluation of a Data Annotation Platform for Large, Time-Series Datasets in Intensive Care: Mixed Methods Study
Marceli Wac, Raul Santos-Rodriguez, Chris McWilliams +1
Intensive Care Units are complex, data-rich environments where critically ill patients are treated using variety of clinical equipment. The data collected using this equipment can…
The Effect of Perceptual Metrics on Music Representation Learning for Genre Classification
Tashi Namgyal, Alexander Hepburn, Raul Santos-Rodriguez +2
The subjective quality of natural signals can be approximated with objective perceptual metrics. Designed to approximate the perceptual behaviour of human observers, perceptual met…
Towards Personalised Patient Risk Prediction Using Temporal Hospital Data Trajectories
Thea Barnes, Enrico Werner, Jeffrey N. Clark +1
Quantifying a patient's health status provides clinicians with insight into patient risk, and the ability to better triage and manage resources. Early Warning Scores (EWS) are wide…
An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations
Jonathan Erskine, Matt Clifford, Alexander Hepburn +1
Human-Computer Interaction has been shown to lead to improvements in machine learning systems by boosting model performance, accelerating learning and building user confidence. In…
Evaluating Perceptual Distance Models by Fitting Binomial Distributions to Two-Alternative Forced Choice Data
Alexander Hepburn, Raul Santos-Rodriguez, Javier Portilla
The Two Alternative Forced Choice (2AFC) paradigm offers advantages over the Mean Opinion Score (MOS) paradigm in psychophysics (PF), such as simplicity and robustness. However, wh…
Data is Overrated: Perceptual Metrics Can Lead Learning in the Absence of Training Data
Tashi Namgyal, Alexander Hepburn, Raul Santos-Rodriguez +2
Perceptual metrics are traditionally used to evaluate the quality of natural signals, such as images and audio. They are designed to mimic the perceptual behaviour of human observe…
Strategies for engaging clinical participants in the co-design of software for healthcare domains
Marceli Wac, Raul Santos-Rodriguez, Chris McWilliams +1
Co-design is an effective method for designing software, but implementing it within the clinical setting comes with a set of unique challenges. This makes recruitment and engagemen…
Image Statistics Predict the Sensitivity of Perceptual Quality Metrics
Alexander Hepburn, Valero Laparra, Raúl Santos-Rodriguez +1
Previously, Barlow and Attneave hypothesised a link between biological vision and information maximisation. Following Shannon, information was defined using the probability of natu…
Two-step counterfactual generation for OOD examples
Nawid Keshtmand, Raul Santos-Rodriguez, Jonathan Lawry
Two fundamental requirements for the deployment of machine learning models in safety-critical systems are to be able to detect out-of-distribution (OOD) data correctly and to be ab…
Identification, explanation and clinical evaluation of hospital patient subtypes
Enrico Werner, Jeffrey N. Clark, Ranjeet S. Bhamber +5
We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large U…
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection
Nawid Keshtmand, Raul Santos-Rodriguez, Jonathan Lawry
A recent popular approach to out-of-distribution (OOD) detection is based on a self-supervised learning technique referred to as contrastive learning. There are two main variants o…
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components
Kacper Sokol, Alexander Hepburn, Raul Santos-Rodriguez +1
Explainability techniques for data-driven predictive models based on artificial intelligence and machine learning algorithms allow us to better understand the operation of such sys…