Lin, Xuankang

Zhu, He

Samanta, Roopsha

Jagannathan, Suresh

Ivrii, Alexander

Strichman, Ofer

2020-09-11T15:19:56Z

2020-09-11T15:19:56Z

2020

<div class="csl-bib-body"> <div class="csl-entry">Lin, X., Zhu, H., Samanta, R., &#38; Jagannathan, S. (2020). ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks. In A. Ivrii &#38; O. Strichman (Eds.), <i>Proceedings of the 20th Conference on Formal Methods in Computer-Aided Design – FMCAD 2020</i> (pp. 148–157). TU Wien Academic Press. https://doi.org/10.34727/2020/isbn.978-3-85448-042-6_22</div> </div>

http://hdl.handle.net/20.500.12708/15518

http://dx.doi.org/10.34727/2020/isbn.978-3-85448-042-6_22

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TU Wien Academic Press

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computer-aided system design

ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks

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Inproceedings

Creative Commons Namensnennung 4.0 International

Creative Commons Attribution 4.0 International

10.34727/2020/isbn.978-3-85448-042-6_22

Purdue University, USA

Rutgers University, USA

Purdue University, USA

Purdue University, USA

IBM Research, Israel

Technion - Israel Institute of Technology, Israel

978-3-85448-042-6

148

157

2708-7824

Wien

Proceedings of the 20th Conference on Formal Methods in Computer-Aided Design – FMCAD 2020

1

10.34727/2020/isbn.978-3-85448-042-6

vor

22

E192-04 - Forschungsbereich Formal Methods in Systems Engineering

10

Conference Series: Formal Methods in Computer-Aided Design

CC BY 4.0

CC BY 4.0

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http://purl.org/coar/resource_type/c_18cf

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Inproceedings

Purdue University, USA

Rutgers University, USA

Purdue University, USA

Purdue University, USA