Michael Weiss
Orcid: 0000-0002-8944-389XAffiliations:
- Università della Svizzera italiana, Lugano, Switzerland
- University of Zurich, Switzerland (former)
According to our database1,
Michael Weiss
authored at least 28 papers
between 2015 and 2024.
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Bibliography
2024
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks - RCR Report.
ACM Trans. Softw. Eng. Methodol., January, 2024
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks.
ACM Trans. Softw. Eng. Methodol., January, 2024
2023
Generating and detecting true ambiguity: a forgotten danger in DNN supervision testing.
Empir. Softw. Eng., November, 2023
Uncertainty quantification for deep neural networks: An empirical comparison and usage guidelines.
Softw. Test. Verification Reliab., September, 2023
Replication package for the EMSE paper "Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing".
Dataset, September, 2023
Uncertainty-wizard: Fast and user-friendly neural network uncertainty quantification.
Dataset, February, 2023
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replication Paper).
Dataset, February, 2023
2022
Replication Package: Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning.
Dataset, April, 2022
Replication Package: Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning.
Dataset, April, 2022
Replication Package: Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning.
Dataset, April, 2022
Replication Package: Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning.
Dataset, April, 2022
Replication Package: Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning.
Dataset, April, 2022
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replication Paper).
Dataset, April, 2022
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replication Paper).
Dataset, April, 2022
A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity.
CoRR, 2022
Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2022
Simple techniques work surprisingly well for neural network test prioritization and active learning (replicability study).
Proceedings of the ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, South Korea, July 18, 2022
2021
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification.
Proceedings of the 14th IEEE Conference on Software Testing, Verification and Validation, 2021
Proceedings of the 14th IEEE Conference on Software Testing, Verification and Validation, 2021
Proceedings of the 3rd IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning, 2021
2020
Uncertainty-wizard: Fast and user-friendly neural network uncertainty quantification.
Dataset, December, 2020
Uncertainty-wizard: Fast and user-friendly neural network uncertainty quantification.
Dataset, December, 2020
Uncertainty-wizard: Fast and user-friendly neural network uncertainty quantification.
Dataset, December, 2020
Uncertainty-wizard: Fast and user-friendly neural network uncertainty quantification.
Dataset, December, 2020
Empir. Softw. Eng., 2020
Proceedings of the ICSE '20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June, 2020
2017
Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017
2015
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015