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Found 7 papers, 2 papers with code
DeepKnowledge: Generalisation-Driven Deep Learning Testing
Despite their unprecedented success, DNNs are notoriously fragile to small shifts in data distribution, demanding effective testing techniques that can assess their dependability.
Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
Deploying deep learning models in safety-critical applications remains a very challenging task, mandating the provision of assurances for the dependable operation of these models.
Bayesian Learning for the Robust Verification of Autonomous Robots
Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments.
Fast Parametric Model Checking through Model Fragmentation
Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.)
Software Engineering Formal Languages and Automata Theory Robotics
Genetic Improvement @ ICSE 2020
Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the 42nd ACM/IEEE International Conference on Software Engineering on Friday 3rd July 2020).
Importance-Driven Deep Learning System Testing
Deep Learning (DL) systems are key enablers for engineering intelligent applications due to their ability to solve complex tasks such as image recognition and machine translation.
DeepFault: Fault Localization for Deep Neural Networks
Deep Neural Networks (DNNs) are increasingly deployed in safety-critical applications including autonomous vehicles and medical diagnostics.
