9 citations · 27 across the 5 of their papers we have counts for
5 papers
Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning
Dimitrios Boursinos, Xenofon Koutsoukos
Deep neural networks are frequently used by autonomous systems for their ability to learn complex, non-linear data patterns and make accurate predictions in dynamic environments. H…
Improving Prediction Confidence in Learning-Enabled Autonomous Systems
Dimitrios Boursinos, Xenofon Koutsoukos
Autonomous systems use extensively learning-enabled components such as deep neural networks (DNNs) for prediction and decision making. In this paper, we utilize a feedback loop bet…
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning
Dimitrios Boursinos, Xenofon Koutsoukos
Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, such components may introduce new types of hazards that can…
Trusted Confidence Bounds for Learning Enabled Cyber-Physical Systems
Dimitrios Boursinos, Xenofon Koutsoukos
Cyber-physical systems (CPS) can benefit by the use of learning enabled components (LECs) such as deep neural networks (DNNs) for perception and decision making tasks. However, DNN…
Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components
Dimitrios Boursinos, Xenofon Koutsoukos
Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, they may introduce new types of hazards that can have disast…