Search Results for author:
Found 7 papers, 1 papers with code
A Certified Proof Checker for Deep Neural Network Verification
Recent advances in the verification of deep neural networks (DNNs) have opened the way for broader usage of DNN verification technology in many application areas, including safety-critical ones.
NLP Verification: Towards a General Methodology for Certifying Robustness
We propose a number of practical NLP methods that can help to identify the effects of the embedding gap; and in particular we propose the metric of falsifiability of semantic subpspaces as another fundamental metric to be reported as part of the NLP verification pipeline.
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
This paper serves as a comprehensive system description of version 2. 0 of the Marabou framework for formal analysis of neural networks.
Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing
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 workload and in improving operational safety.
Towards a Certified Proof Checker for Deep Neural Network Verification
In this work, we present a novel implementation of a proof checker for DNN verification.
ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification
In particular, many known neural network verification methods that work for computer vision and other numeric datasets do not work for NLP.
Neural Network Verification with Proof Production
In this work, we present a novel mechanism for enhancing Simplex-based DNN verifiers with proof production capabilities: the generation of an easy-to-check witness of unsatisfiability, which attests to the absence of errors.
