Property-Directed Verification and Robustness Certification of Recurrent Neural Networks.

Igor Khmelnitsky; Daniel Neider; Rajarshi Roy; Xuan Xie; Benoît Barbot; Benedikt Bollig; Alain Finkel; Serge Haddad; Martin Leucker; Lina Ye

doi:10.1007/978-3-030-88885-5_24

Property-Directed Verification and Robustness Certification of Recurrent Neural Networks.

Igor Khmelnitsky, Daniel Neider, Rajarshi Roy, Xuan Xie, Benoît Barbot, Benedikt Bollig, Alain Finkel, Serge Haddad, Martin Leucker, Lina Ye

15 Citations (Scopus)

Original language	English
Pages	364-380
DOIs	https://doi.org/10.1007/978-3-030-88885-5_24
Publication status	Published - 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 14 Life Below Water
SDG 15 Life on Land

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)

Access to Document

10.1007/978-3-030-88885-5_24

https://dblp.org/rec/conf/atva/KhmelnitskyNRXB21

Cite this

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title = "Property-Directed Verification and Robustness Certification of Recurrent Neural Networks.",

author = "Igor Khmelnitsky and Daniel Neider and Rajarshi Roy and Xuan Xie and Beno{\^i}t Barbot and Benedikt Bollig and Alain Finkel and Serge Haddad and Martin Leucker and Lina Ye",

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AU - Khmelnitsky, Igor

AU - Neider, Daniel

AU - Roy, Rajarshi

AU - Xie, Xuan

AU - Barbot, Benoît

AU - Bollig, Benedikt

AU - Finkel, Alain

AU - Haddad, Serge

AU - Leucker, Martin

AU - Ye, Lina

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PY - 2021

Y1 - 2021

U2 - 10.1007/978-3-030-88885-5_24

DO - 10.1007/978-3-030-88885-5_24

M3 - Conference Papers

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