54 citations · 59 across the 8 of their papers we have counts for
9 papers
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…
Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines
Michael Weiss, Paolo Tonella
Deep Neural Networks (DNN) are increasingly used as components of larger software systems that need to process complex data, such as images, written texts, audio/video signals. DNN…
CheapET-3: Cost-Efficient Use of Remote DNN Models
Michael Weiss
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. Such mo…
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing
Michael Weiss, André García Gómez, Paolo Tonella
Deep Neural Networks (DNNs) are becoming a crucial component of modern software systems, but they are prone to fail under conditions that are different from the ones observed durin…
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…
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…