Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
Michael Weiß, Paolo Tonella
Abstract
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 supervision. We present Uncertainty-Wizard, a tool that allows to quantify such uncertainty and confidence in artificial neural networks. It is built on top of the industry-leading TF.KERAS deep learning API and it provides a near-transparent and easy to understand interface. At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.
Topics & Concepts
WizardComputer scienceArtificial neural networkArtificial intelligenceMachine learningUncertainty quantificationDeep learningVariety (cybernetics)Selection (genetic algorithm)Interface (matter)Deep neural networksData miningOperating systemBubbleMaximum bubble pressure methodAdversarial Robustness in Machine LearningAnomaly Detection Techniques and ApplicationsFault Detection and Control Systems