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Found 9 papers, 6 papers with code
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks
Systems relying on large-scale DNNs thus have to call the corresponding model over the network, leading to substantial costs for hosting and running the large-scale remote model, costs which are often charged on a per-use basis.
Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines
After overviewing the main approaches to uncertainty estimation and discussing their pros and cons, we motivate the need for a specific empirical assessment method that can deal with the experimental setting in which supervisors are used, where the accuracy of the DNN matters only as long as the supervisor lets the DLS continue to operate.
CheapET-3: Cost-Efficient Use of Remote DNN Models
On complex problems, state of the art prediction accuracy of Deep Neural Networks (DNN) can be achieved using very large-scale models, consisting of billions of parameters.
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing
In this paper, we propose a novel way to generate ambiguous inputs to test DNN supervisors and used it to empirically compare several existing supervisor techniques.
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)
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 labeling costs.
A Review and Refinement of Surprise Adequacy
As an adequacy criterion, it has been used to assess the strength of DL test suites.
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring
Modern software systems rely on Deep Neural Networks (DNN) when processing complex, unstructured inputs, such as images, videos, natural language texts or audio signals.
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
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.
Misbehaviour Prediction for Autonomous Driving Systems
Deep Neural Networks (DNNs) are the core component of modern autonomous driving systems.
Signal Processing
