3 papers
cs.LG2023
Information based explanation methods for deep learning agents -- with applications on large open-source chess models
Patrik Hammersborg, Inga Strümke
With large chess-playing neural network models like AlphaZero contesting the state of the art within the world of computerised chess, two challenges present themselves: The questio…
cs.LG2023
Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network models
Patrik Hammersborg, Inga Strümke
Neural network models are widely used in a variety of domains, often as black-box solutions, since they are not directly interpretable for humans. The field of explainable artifici…
cs.LG2022
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts
Patrik Hammersborg, Inga Strümke
Self-trained autonomous agents developed using machine learning are showing great promise in a variety of control settings, perhaps most remarkably in applications involving autono…