Fast polyhedra abstract domain

By Gagandeep Singh, Markus Püschel, Martin T. Vechev. 2017

116 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Hardware and Architecture

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Timon Gehr
2. Martin T. Vechev
3. Gagandeep Singh
4. Markus Püschel
5. Petar Tsankov
6. Matthew Mirman
7. Miltiadis Allamanis
8. Charles Sutton

In Computational Theory and Mathematics

1. Timon Gehr
2. Martin T. Vechev
3. Gagandeep Singh
4. Markus Püschel

In Hardware and Architecture

1. Timon Gehr
2. Martin T. Vechev
3. Gagandeep Singh
4. Markus Püschel
5. Petar Tsankov

Citations in the corpus

• AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation (2018, neutral): “, in numerical domains [39]).”
• An abstract domain for certifying neural networks (2019, methodology): “The analyzer is written in Python and the abstract transformers of our domain are implemented on top of the ELINA library [Singh et al. 2017, 2018b] for numerical abstractions.”
• Securify: Practical Security Analysis of Smart Contracts (2018, methodology): “. First, the current version of Securify cannot reason about numerical properties, such as overflows. To address this limitation, we plan to extend Securify with numerical analysis (e.g., using ELINA [48]), which would not only improve the precision of Securify but also enable the checking of numeri”
• Fast and Effective Robustness Certification (2018, methodology): “The Zonotope approximations were implemented on top of the public ELINA [1, 25] library.”