2 citations · 2 across the 19 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
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…