Search Results for author:
Found 6 papers, 4 papers with code
Inherently Interpretable Time Series Classification via Multiple Instance Learning
Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes.
Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations
By imputing player locations from easy to obtain event data, we increase the accessibility of downstream tasks.
Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification
Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation.
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards.
Model Agnostic Interpretability for Multiple Instance Learning
In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag.
Reducing catastrophic forgetting when evolving neural networks
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 once instead of just one.
