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ORCIDMichael Weiss 0004
Person information
- affiliation: Università della Svizzera italiana, Lugano, Switzerland
- affiliation (former): University of Zurich, Switzerland
Other persons with the same name
- Michael Weiss — disambiguation page
- Michael Weiss 0001 — Carleton University, Ottawa, Canada (and 1 more)
- Michael Weiss 0002 — University of South Wales, UK
- Michael Weiss 0003 — Technical University of Berlin, Germany
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2020 – today
- 2024
[j5]Michael Weiss
, Paolo Tonella:
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks. ACM Trans. Softw. Eng. Methodol. 33(1): 28:1-28:29 (2024)- [j4]Michael Weiss, Paolo Tonella:
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks - RCR Report. ACM Trans. Softw. Eng. Methodol. 33(1): 29:1-29:4 (2024) - 2023
- [j3]Michael Weiss, André García Gómez, Paolo Tonella:
Generating and detecting true ambiguity: a forgotten danger in DNN supervision testing. Empir. Softw. Eng. 28(6): 146 (2023) - [j2]Michael Weiss, Paolo Tonella:
Uncertainty quantification for deep neural networks: An empirical comparison and usage guidelines. Softw. Test. Verification Reliab. 33(6) (2023) - [i8]Michael Weiss, Paolo Tonella:
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks. CoRR abs/2304.02654 (2023) - 2022
- [c8]Michael Weiss, Paolo Tonella:
Simple techniques work surprisingly well for neural network test prioritization and active learning (replicability study). ISSTA 2022: 139-150 - [c7]Michael Weiss:
CheapET-3: cost-efficient use of remote DNN models. ESEC/SIGSOFT FSE 2022: 1811-1813 - [i7]Michael Weiss, Paolo Tonella:
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study). CoRR abs/2205.00664 (2022) - [i6]Michael Weiss, André García Gómez, Paolo Tonella:
A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity. CoRR abs/2207.10495 (2022) - [i5]Michael Weiss:
CheapET-3: Cost-Efficient Use of Remote DNN Models. CoRR abs/2208.11552 (2022) - [i4]Michael Weiss, Paolo Tonella:
Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines. CoRR abs/2212.07118 (2022) - 2021
- [c6]Michael Weiss, Rwiddhi Chakraborty, Paolo Tonella:
A Review and Refinement of Surprise Adequacy. DeepTest@ICSE 2021: 17-24 - [c5]Michael Weiss, Paolo Tonella:
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring. ICST 2021: 24-35 - [c4]Michael Weiss, Paolo Tonella:
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification. ICST 2021: 436-441 - [i3]Michael Weiss, Paolo Tonella:
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification. CoRR abs/2101.00982 (2021) - [i2]Michael Weiss, Paolo Tonella:
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring. CoRR abs/2102.00902 (2021) - [i1]Michael Weiss, Rwiddhi Chakraborty, Paolo Tonella:
A Review and Refinement of Surprise Adequacy. CoRR abs/2103.05939 (2021) - 2020
- [j1]Vincenzo Riccio, Gunel Jahangirova, Andrea Stocco, Nargiz Humbatova, Michael Weiss, Paolo Tonella:
Testing machine learning based systems: a systematic mapping. Empir. Softw. Eng. 25(6): 5193-5254 (2020) - [c3]Andrea Stocco, Michael Weiss, Marco Calzana, Paolo Tonella:
Misbehaviour prediction for autonomous driving systems. ICSE 2020: 359-371
2010 – 2019
- 2017
- [c2]Michael Weiss, Benjamin Lubin, Sven Seuken:
SATS: A Universal Spectrum Auction Test Suite. AAMAS 2017: 51-59 - 2015
- [c1]Timo Mennle, Michael Weiss, Basil Philipp, Sven Seuken:
The Power of Local Manipulation Strategies in Assignment Mechanisms. IJCAI 2015: 82-89
Coauthor Index
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last updated on 2024-06-10 23:27 CEST by the dblp team
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