… machine learning (AML): Machine learning in adversarial settings is a field that lies at the
intersection of machine learning and computer security… the robustness of neural network models…

… to enhance their reliability and resilience to counter such threats. Flexible and … deep neural
networks (DNNs) to compromise the security and dependency of machine learning systems …

… with neural networks, especially the deep neural networks (… to improve reliability, security,
and efficiency of the electrical system … training can increase the robustness of neural networks …

… , convolutional neural networks (CNNs) have proven to give high performance in medical
image classification tasks when compared with other state-of-the-art non-learning based …

… yet robust assessment rules and closed-loop control strategies, while also taking into account
the reliability of protection, … Recently, due to the success of deep neural networks in other …

… where wrong predictions impose a security risk—imagine a … robust reconstruction schemes.
This offers an alternative and novel perspective on the reliability of deep learning strategies …

… deep machine learning and IoT-based monitoring system. Diverse … tools and smart factories
to have better reliability and efficiency of … In this work, the deep neural network architecture is …

… deep neural networks. Addressing this issue requires a reliable way to evaluate the robustness
of a network… developed to compute robustness quantification for neural networks, namely…

… Abstract—As we seek to deploy machine learning models … , or just wants to reduce the
reliability of the algorithm by forcing a … deep neural networks (DNN) and convolutional neural …

… The need for proactive, well-calibrated, fast, reliable and advanced security and stability …
The deep neural network based TSVMNN was used to eliminate computational complexity of …