54 citations · 211 across the 34 of their papers we have counts for
5 papers · 1 filter
Heteroscedasticity of Denoising Score Matching with Generalised Smooth Noise
Juyan Zhang, Rhys Newbury, Xinyang Zhang +3
Score Matching (SM) is a powerful framework for estimating the log-density derivatives of a distribution without calculating its normalizing constants. This capability has made it…
IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates
Tatiana Lopez-Guevara, Michael Burke, Nicholas K. Taylor +1
Model-free reinforcement learning (RL) is a powerful tool to learn a broad range of robot skills and policies. However, a lack of policy interpretability can inhibit their successf…
NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces
Miguel Jaques, Michael Burke, Timothy Hospedales
Learning low-dimensional latent state space dynamics models has been a powerful paradigm for enabling vision-based planning and learning for control. We introduce a latent dynamics…
Black-Box Saliency Map Generation Using Bayesian Optimisation
Mamuku Mokuwe, Michael Burke, Anna Sergeevna Bosman
Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches…
Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes
Todor Davchev, Michael Burke, Subramanian Ramamoorthy
Context plays a significant role in the generation of motion for dynamic agents in interactive environments. This work proposes a modular method that utilises a learned model of th…