3 citations · 14 across the 23 of their papers we have counts for
6 papers · 1 filter
Repurposing Obsolete Representations for Post-Deployment Adaptation
Daniel Bethell, Charmaine Barker, Simos Gerasimou
Deep neural networks are increasingly deployed in long-lived systems, where task requirements may change after training. In such settings, part of the original output space may bec…
Localisation-Aware Uncertainty for Pretrained Object Detection
Charmaine Barker, Daniel Bethell, Simos Gerasimou
Reliable uncertainty estimation is essential for deploying object detectors when distribution/covariate shift and adversarial attacks may occur. Existing approaches often require d…
Robust Evidential Learning Through Latent Consistency
Charmaine Barker, Daniel Bethell, Simos Gerasimou
Reliable uncertainty quantification is essential for deploying deep learning models in high-stakes settings, where out-of-distribution and adversarial inputs can induce confident b…
Leveraging System-Level Observations to Inform Bayesian Learning of Model Parameters for Quantitative Verification
Simos Gerasimou, Xingyu Zhao
Combining Bayesian learning and quantitative verification is a powerful toolset for analysing key quantitative properties of software systems, like reliability and response time. H…
Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs
Gricel Vázquez, Alexandros Evangelidis, Sepeedeh Shahbeigi +2
Integrating Large Language Models (LLMs) into complex software systems enables the generation of human-understandable explanations of opaque AI processes, such as automated task pl…
Formally Guaranteed Control Adaptation for ODD-Resilient Autonomous Systems
Gricel Vázquez, Calum Imrie, Sepeedeh Shahbeigi +5
Ensuring reliable performance in situations outside the Operational Design Domain (ODD) remains a primary challenge in devising resilient autonomous systems. We explore this challe…