2 papers
cs.LG2020
NSL: Hybrid Interpretable Learning From Noisy Raw Data
Daniel Cunnington, Alessandra Russo, Mark Law +2
Inductive Logic Programming (ILP) systems learn generalised, interpretable rules in a data-efficient manner utilising existing background knowledge. However, current ILP systems re…
cs.AI2019
Synthetic Ground Truth Generation for Evaluating Generative Policy Models
Daniel Cunnington, Graham White, Geeth de Mel
Generative Policy-based Models aim to enable a coalition of systems, be they devices or services to adapt according to contextual changes such as environmental factors, user prefer…