Abstract
Deep Neural Networks (DNNs) are popular machine learning models which have found successful application in many different domains across computer science.
Nevertheless, providing formal guarantees on the behaviour of neural networks is hard and therefore their reliability in safety-critical domains is still a concern.
Verification and repair emerged as promising solutions to address this issue. In the following, I will present some of my recent efforts in this area.
Publisher
International Joint Conferences on Artificial Intelligence Organization
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献