2 papers
eess.SP2025
Machine Intelligence on the Edge: Interpretable Cardiac Pattern Localisation Using Reinforcement Learning
Haozhe Tian, Qiyu Rao, Nina Moutonnet +2
Matched filters are widely used to localise signal patterns due to their high efficiency and interpretability. However, their effectiveness deteriorates for low signal-to-noise rat…
cs.LG2025
Inducing, Detecting and Characterising Neural Modules: A Pipeline for Functional Interpretability in Reinforcement Learning
Anna Soligo, Pietro Ferraro, David Boyle
Interpretability is crucial for ensuring RL systems align with human values. However, it remains challenging to achieve in complex decision making domains. Existing methods frequen…