54 citations · 92 across the 13 of their papers we have counts for
6 papers · 1 filter
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification
Ruben Grewal, Paolo Tonella, Andrea Stocco
The automated real-time recognition of unexpected situations plays a crucial role in the safety of autonomous vehicles, especially in unsupported and unpredictable scenarios. This…
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks
Michael Weiss, Paolo Tonella
Recent decades have seen the rise of large-scale Deep Neural Networks (DNNs) to achieve human-competitive performance in a variety of artificial intelligence tasks. Often consistin…
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)
Michael Weiss, Paolo Tonella
Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important technique to handle the typically very large test datasets efficiently, saving computation and labelin…
DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search
Tahereh Zohdinasab, Vincenzo Riccio, Alessio Gambi +1
Deep Learning (DL) has been successfully applied to a wide range of application domains, including safety-critical ones. Several DL testing approaches have been recently proposed i…
A Review and Refinement of Surprise Adequacy
Michael Weiss, Rwiddhi Chakraborty, Paolo Tonella
Surprise Adequacy (SA) is one of the emerging and most promising adequacy criteria for Deep Learning (DL) testing. As an adequacy criterion, it has been used to assess the strength…
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
Michael Weiss, Paolo Tonella
Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques proposed for deep learning testing, including test data selection and system supervi…