1 citations · 1 across the 11 of their papers we have counts for
11 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…
Evaluating Deep Multivariate Imputation Models on Wearable Device Data
Skye Goodman, Roussel Desmond Nzoyem, Leandro Junges +5
Wearable device data enables continuous health monitoring, but suffers from structured missingness: features sharing a physical sensor drop out together. Deep imputation methods su…
Circadian Phase Locking of Epilepsy Seizures in Wearable Data: A Single-Patient Case Study
Berenika Ewart-James, Matthew Wragg, Nawid Keshtmand +3
Epilepsy is a common, chronic neurological disorder characterized by recurrent seizures caused by sudden bursts of abnormal electrical activity in the brain. Seizures can often be…
FLEX: Feature Importance from Layered Counterfactual Explanations
Nawid Keshtmand, Roussel Desmond Nzoyem, Jeffrey Nicholas Clark
Machine learning models achieve state-of-the-art performance across domains, yet their lack of interpretability limits safe deployment in high-stakes settings. Counterfactual expla…
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…
Weight-Space Linear Recurrent Neural Networks
Roussel Desmond Nzoyem, Nawid Keshtmand, Enrique Crespo Fernandez +4
We introduce WARP (Weight-space Adaptive Recurrent Prediction), a simple yet powerful model that unifies weight-space learning with linear recurrence to redefine sequence modeling.…
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…
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…
TraCE: Trajectory Counterfactual Explanation Scores
Jeffrey N. Clark, Edward A. Small, Nawid Keshtmand +5
Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box c…
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…
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…