18 citations · 37 across the 14 of their papers we have counts for
3 papers · 1 filter
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection
Nawid Keshtmand, Raul Santos-Rodriguez, Jonathan Lawry
A recent popular approach to out-of-distribution (OOD) detection is based on a self-supervised learning technique referred to as contrastive learning. There are two main variants o…
Reward Learning with Trees: Methods and Evaluation
Tom Bewley, Jonathan Lawry, Arthur Richards +2
Recent efforts to learn reward functions from human feedback have tended to use deep neural networks, whose lack of transparency hampers our ability to explain agent behaviour or v…
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction
Tom Bewley, Jonathan Lawry, Arthur Richards
We introduce a data-driven, model-agnostic technique for generating a human-interpretable summary of the salient points of contrast within an evolving dynamical system, such as the…