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99 results on '"Yoni Zohar"'

1. Verifying Bit-vector Invertibility Conditions in Coq (Extended Abstract)

Author

Burak Ekici, Arjun Viswanathan, Yoni Zohar, Clark Barrett, and Cesare Tinelli

Subjects
Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

This work is a part of an ongoing effort to prove the correctness of invertibility conditions for the theory of fixed-width bit-vectors, which are used to solve quantified bit-vector formulas in the Satisfiability Modulo Theories (SMT) solver CVC4. While many of these were proved in a completely automatic fashion for any bit-width, some were only proved for bit-widths up to 65, even though they are being used to solve formulas over arbitrary bit-widths. In this paper we describe our initial efforts in proving a subset of these invertibility conditions in the Coq proof assistant. We describe the Coq library that we use, as well as the extensions that we introduced to it.

Published
2019
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15. cvc5: A Versatile and Industrial-Strength SMT Solver.

Author

Haniel Barbosa, Clark W. Barrett, Martin Brain, Gereon Kremer, Hanna Lachnitt, Makai Mann, Abdalrhman Mohamed, Mudathir Mohamed, Aina Niemetz, Andres Nötzli, Alex Ozdemir, Mathias Preiner, Andrew Reynolds 0001, Ying Sheng 0007, Cesare Tinelli, and Yoni Zohar

Published
2022
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20. The Move Prover.

Author

Jingyi Emma Zhong, Kevin Cheang, Shaz Qadeer, Wolfgang Grieskamp, Sam Blackshear, Junkil Park, Yoni Zohar, Clark W. Barrett, and David L. Dill

Published
2020
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45. DNN Verification, Reachability, and the Exponential Function Problem

Author

Omri Isac and Yoni Zohar and Clark Barrett and Guy Katz, Isac, Omri, Zohar, Yoni, Barrett, Clark, Katz, Guy, Omri Isac and Yoni Zohar and Clark Barrett and Guy Katz, Isac, Omri, Zohar, Yoni, Barrett, Clark, and Katz, Guy

Abstract

Deep neural networks (DNNs) are increasingly being deployed to perform safety-critical tasks. The opacity of DNNs, which prevents humans from reasoning about them, presents new safety and security challenges. To address these challenges, the verification community has begun developing techniques for rigorously analyzing DNNs, with numerous verification algorithms proposed in recent years. While a significant amount of work has gone into developing these verification algorithms, little work has been devoted to rigorously studying the computability and complexity of the underlying theoretical problems. Here, we seek to contribute to the bridging of this gap. We focus on two kinds of DNNs: those that employ piecewise-linear activation functions (e.g., ReLU), and those that employ piecewise-smooth activation functions (e.g., Sigmoids). We prove the two following theorems: (i) the decidability of verifying DNNs with a particular set of piecewise-smooth activation functions, including Sigmoid and tanh, is equivalent to a well-known, open problem formulated by Tarski; and (ii) the DNN verification problem for any quantifier-free linear arithmetic specification can be reduced to the DNN reachability problem, whose approximation is NP-complete. These results answer two fundamental questions about the computability and complexity of DNN verification, and the ways it is affected by the network’s activation functions and error tolerance; and could help guide future efforts in developing DNN verification tools.

Published
2023
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