Distribution Models for Falsification and Verification of DNNs
Felipe Toledo, David Shriver, Sebastian Elbaum, Matthew B. Dwyer
International Conference on Automated Software Engineering (ASE)
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2021
Felipe Toledo, David Shriver, Sebastian Elbaum, Matthew B. Dwyer. 2021. Distribution Models for Falsification and Verification of DNNs. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE). 317-329. https://doi.org/10.1109/ASE51524.2021.9678590
DNNV: A Framework for Deep Neural Network Verification
David Shriver, Sebastian Elbaum, Matthew B. Dwyer
Computer Aided Verification
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2021
David Shriver, Sebastian Elbaum, Matthew B. Dwyer. 2021. DNNV: A Framework for Deep Neural Network Verification. In Computer Aided Verification. 137--150. https://doi.org/10.1007/978-3-030-81685-8_6
Reducing DNN Properties to Enable Falsification with Adversarial Attacks
David Shriver, Sebastian Elbaum, Matthew B. Dwyer
International Conference on Software Engineering (ICSE)
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2021
David Shriver, Sebastian Elbaum, Matthew B. Dwyer. 2021. Reducing DNN Properties to Enable Falsification with Adversarial Attacks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). 275-287. https://doi.org/10.1109/ICSE43902.2021.00036
Systematic Generation of Diverse Benchmarks for DNN Verification
Dong Xu, David Shriver, Matthew B. Dwyer, Sebastian Elbaum
Computer Aided Verification
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2020
Dong Xu, David Shriver, Matthew B. Dwyer, Sebastian Elbaum. 2020. Systematic Generation of Diverse Benchmarks for DNN Verification. In Computer Aided Verification - 32nd International Conference, CAV 2020, Los Angeles, CA, USA, July 21-24, 2020, Proceedings, Part I. 97-121. https://doi.org/10.1007/978-3-030-53288-8_5
Refactoring Neural Networks for Verification
David Shriver, Dong Xu, Sebastian Elbaum, Matthew B. Dwyer
arXiv preprint arXiv:1908.08026
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2019
David Shriver, Dong Xu, Sebastian Elbaum, Matthew B. Dwyer. 2019. Refactoring Neural Networks for Verification. arXiv:1908.08026. https://arxiv.org/abs/1908.08026
Evaluating Recommender System Stability with Influence-Guided Fuzzing
David Shriver, Sebastian Elbaum, Matthew B. Dwyer, David S. Rosenblum
AAAI Conference on Artificial Intelligenc
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2019
David Shriver, Sebastian Elbaum, Matthew B. Dwyer, David S. Rosenblum. 2019. Evaluating Recommender System Stability with Influence-Guided Fuzzing. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. 4934-4942. https://doi.org/10.1609/aaai.v33i01.33014934
Toward the development of richer properties for recommender systems
David Shriver
International Conference on Software Engineering: Companion Proceedings
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2018
David Shriver. 2018. Toward the development of richer properties for recommender systems. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceedings, ICSE 2018, Gothenburg, Sweden, May 27 - June 03, 2018. 173-174. https://doi.acm.org/10.1145/3183440.3195082
Assessing the Quality and Stability of Recommender Systems
David Shriver
University of Nebraska-Lincoln
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2018
David Shriver. Assessing the Quality and Stability of Recommender Systems. MS Thesis, University of Nebraska-Lincoln, 2018.
At the End of Synthesis: Narrowing Program Candidates
David Shriver, Sebastian G. Elbaum, Kathryn T. Stolee
International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track
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2017
David Shriver, Sebastian G. Elbaum, Kathryn T. Stolee. 2017. At the End of Synthesis: Narrowing Program Candidates. In 39th IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track, ICSE-NIER 2017, Buenos Aires, Argentina, May 20-28, 2017. 19-22. https://doi.org/10.1109/ICSE-NIER.2017.7
