Search Results for author: Sebastian Elbaum

Found 5 papers, 5 papers with code

Reducing DNN Properties to Enable Falsification with Adversarial Attacks

1 code implementation IEEE/ACM 43rd International Conference on Software Engineering (ICSE) 2021 David Shriver, Sebastian Elbaum, Matthew B. Dwyer

Deep Neural Networks (DNN) are increasingly being deployed in safety-critical domains, from autonomous vehicles to medical devices, where the consequences of errors demand techniques that can provide stronger guarantees about behavior than just high test accuracy.

Adversarial Attack Autonomous Vehicles

DNNV: A Framework for Deep Neural Network Verification

1 code implementation26 May 2021 David Shriver, Sebastian Elbaum, Matthew B. Dwyer

In this work we present DNNV, a framework for reducing the burden on DNN verifier researchers, developers, and users.

Deep Learning & Software Engineering: State of Research and Future Directions

1 code implementation17 Sep 2020 Prem Devanbu, Matthew Dwyer, Sebastian Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, Xiangyu Zhang

The intent of this report is to serve as a potential roadmap to guide future work that sits at the intersection of SE & DL.

Systematic Generation of Diverse Benchmarks for DNN Verification

1 code implementation14 Jul 2020 Dong Xu, David Shriver, Matthew B. Dwyer, Sebastian Elbaum

The field of verification has advanced due to the interplay of theoretical development and empirical evaluation.

Refactoring Neural Networks for Verification

2 code implementations6 Aug 2019 David Shriver, Dong Xu, Sebastian Elbaum, Matthew B. Dwyer

Deep neural networks (DNN) are growing in capability and applicability.

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