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 …
AI robustness: a human-centered perspective on technological challenges and opportunities
Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …
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 …
Critically assessing the state of the art in neural network verification
Recent research has proposed various methods to formally verify neural networks against
minimal input perturbations; this verification task is also known as local robustness …
minimal input perturbations; this verification task is also known as local robustness …
Robust training of neural networks against bias field perturbations
P Henriksen, A Lomuscio - Proceedings of the AAAI Conference on …, 2023 - ojs.aaai.org
We introduce the problem of training neural networks such that they are robust against a
class of smooth intensity perturbations modelled by bias fields. We first develop an approach …
class of smooth intensity perturbations modelled by bias fields. We first develop an approach …
Repairing misclassifications in neural networks using limited data
We present a novel and computationally efficient method for repairing a feed-forward neural
network with respect to a finite set of inputs that are misclassified. The method assumes no …
network with respect to a finite set of inputs that are misclassified. The method assumes no …
Robust explanations for human-neural multi-agent systems with formal verification
F Leofante, A Lomuscio - European Conference on Multi-Agent Systems, 2023 - Springer
The quality of explanations in human-agent interactions is fundamental to the development
of trustworthy AI systems. In this paper we study the problem of generating robust contrastive …
of trustworthy AI systems. In this paper we study the problem of generating robust contrastive …
Verification of semantic key point detection for aircraft pose estimation
Abstract We analyse Semantic Segmentation Neural Networks running on an autonomous
aircraft to estimate its 6DOF pose during landing. We show that automated reasoning …
aircraft to estimate its 6DOF pose during landing. We show that automated reasoning …
Formal and Practical Elements for the Certification of Machine Learning Systems
Over the past decade, machine learning has demonstrated impressive results, often
surpassing human capabilities in sensing tasks relevant to autonomous flight. Unlike …
surpassing human capabilities in sensing tasks relevant to autonomous flight. Unlike …
Verification-friendly networks: the case for parametric relus
F Leofante, P Henriksen… - 2023 International Joint …, 2023 - ieeexplore.ieee.org
It has increasingly been recognised that verification can contribute to the validation and
debugging of neural networks before deployment, particularly in safety-critical areas. While …
debugging of neural networks before deployment, particularly in safety-critical areas. While …