6 citations · 6 across the 2 of their papers we have counts for
4 papers
Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning
Todor Davchev, Sarah Bechtle, Subramanian Ramamoorthy +1
Inverse reinforcement learning is a paradigm motivated by the goal of learning general reward functions from demonstrated behaviours. Yet the notion of generality for learnt costs…
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…
Vid2Param: Modelling of Dynamics Parameters from Video
Martin Asenov, Michael Burke, Daniel Angelov +3
Videos provide a rich source of information, but it is generally hard to extract dynamical parameters of interest. Inferring those parameters from a video stream would be beneficia…
An Empirical Evaluation of Adversarial Robustness under Transfer Learning
Todor Davchev, Timos Korres, Stathi Fotiadis +2
In this work, we evaluate adversarial robustness in the context of transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10. Specifically, we st…