4 citations · 9 across the 7 of their papers we have counts for
7 papers
Inherently Interpretable Time Series Classification via Multiple Instance Learning
Joseph Early, Gavin KC Cheung, Kurt Cutajar +3
Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multi…
Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations
Gregory Everett, Ryan J. Beal, Tim Matthews +3
Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS prob…
Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification
Joseph Early, Ying-Jung Deweese, Christine Evers +1
Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use ma…
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning
Joseph Early, Tom Bewley, Christine Evers +1
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in…
Model Agnostic Interpretability for Multiple Instance Learning
Joseph Early, Christine Evers, Sarvapali Ramchurn
In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handf…
Non-Asimov Explanations Regulating AI through Transparency
Chris Reed, Keri Grieman, Joseph Early
An important part of law and regulation is demanding explanations for actual and potential failures. We ask questions like: What happened (or might happen) to cause this failure? A…
Reducing catastrophic forgetting when evolving neural networks
Joseph Early
A key stepping stone in the development of an artificial general intelligence (a machine that can perform any task), is the production of agents that can perform multiple tasks at…