148 citations · 176 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Trust in Language Grounding: a new AI challenge for human-robot teams
David M. Bossens, Christine Evers
The challenge of language grounding is to fully understand natural language by grounding language in real-world referents. While AI techniques are available, the widespread adoptio…
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…