Algorithms for verifying deep neural networks
Deep neural networks are widely used for nonlinear function approximation, with
applications ranging from computer vision to control. Although these networks involve the …
applications ranging from computer vision to control. Although these networks involve the …
Assuring the machine learning lifecycle: Desiderata, methods, and challenges
Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …
successful applications. The potential for this success to continue and accelerate has placed …
Are formal methods applicable to machine learning and artificial intelligence?
Formal approaches can provide strict correctness guarantees for the development of both
hardware and software systems. In this work, we examine state-of-the-art formal methods for …
hardware and software systems. In this work, we examine state-of-the-art formal methods for …
NNV: the neural network verification tool for deep neural networks and learning-enabled cyber-physical systems
This paper presents the Neural Network Verification (NNV) software tool, a set-based
verification framework for deep neural networks (DNNs) and learning-enabled cyber …
verification framework for deep neural networks (DNNs) and learning-enabled cyber …
First three years of the international verification of neural networks competition (VNN-COMP)
This paper presents a summary and meta-analysis of the first three iterations of the annual
International Verification of Neural Networks Competition (VNN-COMP), held in 2020, 2021 …
International Verification of Neural Networks Competition (VNN-COMP), held in 2020, 2021 …
The second international verification of neural networks competition (vnn-comp 2021): Summary and results
This report summarizes the second International Verification of Neural Networks
Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for …
Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for …
Towards quantum enhanced adversarial robustness in machine learning
Abstract Machine learning algorithms are powerful tools for data-driven tasks such as image
classification and feature detection. However, their vulnerability to adversarial examples …
classification and feature detection. However, their vulnerability to adversarial examples …
The third international verification of neural networks competition (VNN-COMP 2022): Summary and results
This report summarizes the 3rd International Verification of Neural Networks Competition
(VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled …
(VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled …
[PDF][PDF] DEEPSPLIT: An Efficient Splitting Method for Neural Network Verification via Indirect Effect Analysis.
We propose a novel, complete algorithm for the verification and analysis of feed-forward,
ReLU-based neural networks. The algorithm, based on symbolic interval propagation …
ReLU-based neural networks. The algorithm, based on symbolic interval propagation …
NNV 2.0: the neural network verification tool
This manuscript presents the updated version of the Neural Network Verification (NNV) tool.
NNV is a formal verification software tool for deep learning models and cyber-physical …
NNV is a formal verification software tool for deep learning models and cyber-physical …