1 citations · 1 across the 3 of their papers we have counts for
11 papers
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…
Bias Remediation in Driver Drowsiness Detection systems using Generative Adversarial Networks
Mkhuseli Ngxande, Jules-Raymond Tapamo, Michael Burke
Datasets are crucial when training a deep neural network. When datasets are unrepresentative, trained models are prone to bias because they are unable to generalise to real world s…
Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based medium classification
Artūras Straižys, Michael Burke, Subramanian Ramamoorthy
Precision cutting of soft-tissue remains a challenging problem in robotics, due to the complex and unpredictable mechanical behaviour of tissue under manipulation. Here, we conside…
Disentangled Relational Representations for Explaining and Learning from Demonstration
Yordan Hristov, Daniel Angelov, Michael Burke +2
Learning from demonstration is an effective method for human users to instruct desired robot behaviour. However, for most non-trivial tasks of practical interest, efficient learnin…
Composing Diverse Policies for Temporally Extended Tasks
Daniel Angelov, Yordan Hristov, Michael Burke +1
Robot control policies for temporally extended and sequenced tasks are often characterized by discontinuous switches between different local dynamics. These change-points are often…
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…