6 citations · 12 across the 10 of their papers we have counts for
12 papers
Proof Minimization in Neural Network Verification
Omri Isac, Idan Refaeli, Haoze Wu +2
The widespread adoption of deep neural networks (DNNs) requires efficient techniques for verifying their safety. DNN verifiers are complex tools, which might contain bugs that coul…
Abstraction-Based Proof Production in Formal Verification of Neural Networks
Yizhak Yisrael Elboher, Omri Isac, Guy Katz +2
Modern verification tools for deep neural networks (DNNs) increasingly rely on abstraction to scale to realistic architectures. In parallel, proof production is becoming a critical…
PICID: Proof-Driven Clause Learning in Neural Network Verification
Omri Isac, Idan Refaeli, Haoze Wu +2
Current Deep Neural Network (DNN) verifiers are typically designed to prioritize scalability over reliability. Reliability can be reinforced through the generation of proofs that a…
Neural Network Verification is a Programming Language Challenge
Lucas C. Cordeiro, Matthew L. Daggitt, Julien Girard-Satabin +8
Neural network verification is a new and rapidly developing field of research. So far, the main priority has been establishing efficient verification algorithms and tools, while pr…
A Certified Proof Checker for Deep Neural Network Verification in Imandra
Remi Desmartin, Omri Isac, Grant Passmore +3
Recent advances in the verification of deep neural networks (DNNs) have opened the way for a broader usage of DNN verification technology in many application areas, including safet…
NLP Verification: Towards a General Methodology for Certifying Robustness
Marco Casadio, Tanvi Dinkar, Ekaterina Komendantskaya +6
Machine Learning (ML) has exhibited substantial success in the field of Natural Language Processing (NLP). For example large language models have empirically proven to be capable o…
Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing
Yizhak Elboher, Raya Elsaleh, Omri Isac +6
As deep neural networks (DNNs) are becoming the prominent solution for many computational problems, the aviation industry seeks to explore their potential in alleviating pilot work…
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
Haoze Wu, Omri Isac, Aleksandar Zeljić +14
This paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool's architectural design a…
Towards a Certified Proof Checker for Deep Neural Network Verification
Remi Desmartin, Omri Isac, Grant Passmore +3
Recent developments in deep neural networks (DNNs) have led to their adoption in safety-critical systems, which in turn has heightened the need for guaranteeing their safety. These…
DNN Verification, Reachability, and the Exponential Function Problem
Omri Isac, Yoni Zohar, Clark Barrett +1
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 saf…
ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification
Marco Casadio, Luca Arnaboldi, Matthew L. Daggitt +5
Verification of machine learning models used in Natural Language Processing (NLP) is known to be a hard problem. In particular, many known neural network verification methods that…
Neural Network Verification with Proof Production
Omri Isac, Clark Barrett, Min Zhang +1
Deep neural networks (DNNs) are increasingly being employed in safety-critical systems, and there is an urgent need to guarantee their correctness. Consequently, the verification c…