Learning nonlinear loop invariants with gated continuous logic networks-Reference-Cited by-同舟云学术

Learning nonlinear loop invariants with gated continuous logic networks

Published:2020-06-06 Issue: Volume: Page:
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Container-title:Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation
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Author:

Yao Jianan¹,Ryan Gabriel¹,Wong Justin¹,Jana Suman¹,Gu Ronghui¹

Affiliation:

1. Columbia University, USA

Funder

National Science Foundation

Office of Naval Research

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3385412.3385986

Reference39 articles.

1. Mislav Balunovic Pavol Bielik and Martin Vechev. 2018. Learning to solve SMT formulas. In Advances in Neural Information Processing Systems. 10317–10328. Mislav Balunovic Pavol Bielik and Martin Vechev. 2018. Learning to solve SMT formulas. In Advances in Neural Information Processing Systems. 10317–10328.

2. A. Biere H. van Maaren and T. Walsh. 2009. Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications. IOS Press Amsterdam The Netherlands The Netherlands. A. Biere H. van Maaren and T. Walsh. 2009. Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications. IOS Press Amsterdam The Netherlands The Netherlands.

3. Inadequacy of computable loop invariants

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