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20152023
most citedTowards Proving the Adversarial Robustness of Deep Neural Networks

119 citations · 465 across the 38 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cs.LG2019★ 20 cited

G2SAT: Learning to Generate SAT Formulas

Jiaxuan You, Haoze Wu, Clark Barrett +2

The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, a…

cs.LO2019★ 1 cited

Simplifying Neural Networks using Formal Verification

Sumathi Gokulanathan, Alexander Feldsher, Adi Malca +2

Deep neural network (DNN) verification is an emerging field, with diverse verification engines quickly becoming available. Demonstrating the effectiveness of these engines on real-…

cs.LO2019★ 2 cited

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

Burak Ekici, Arjun Viswanathan, Yoni Zohar +2

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-vect…

cs.LO2019★ 1 cited

CVC4SY for SyGuS-COMP 2019

Andrew Reynolds, Haniel Barbosa, Andres Nötzli +2

CVC4Sy is a syntax-guided synthesis (SyGuS) solver based on bounded term enumeration and, for restricted fragments, quantifier elimination. The enumerative strategies are based on…

cs.LO2019

DRAT-based Bit-Vector Proofs in CVC4

Alex Ozdemir, Aina Niemetz, Mathias Preiner +2

Many state-of-the-art Satisfiability Modulo Theories (SMT) solvers for the theory of fixed-size bit-vectors employ an approach called bit-blasting, where a given formula is transla…

cs.LO2019

Towards Bit-Width-Independent Proofs in SMT Solvers

Aina Niemetz, Mathias Preiner, Andrew Reynolds +3

Many SMT solvers implement efficient SAT-based procedures for solving fixed-size bit-vector formulas. These approaches, however, cannot be used directly to reason about bit-vectors…

cs.LG2019★ 34 cited

Algorithms for Verifying Deep Neural Networks

Changliu Liu, Tomer Arnon, Christopher Lazarus +3

Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control. Although these networks involve the composition…

cs.LO2019★ 12 cited

Symbolic QED Pre-silicon Verification for Automotive Microcontroller Cores: Industrial Case Study

Eshan Singh, Keerthikumara Devarajegowda, Sebastian Simon +8

We present an industrial case study that demonstrates the practicality and effectiveness of Symbolic Quick Error Detection (Symbolic QED) in detecting logic design flaws (logic bug…