Verification of Neural Networks' Global Robustness
A Kabaha, D Drachsler-Cohen - arXiv preprint arXiv:2402.19322, 2024 - arxiv.org
Neural networks are successful in various applications but are also susceptible to
adversarial attacks. To show the safety of network classifiers, many verifiers have been …
adversarial attacks. To show the safety of network classifiers, many verifiers have been …
Neural network editing: algorithms and applications
F Fu - 2024 - open.bu.edu
Deep neural networks have demonstrated impressive performance in a wide variety of
applications. However, deep neural networks are not perfect. In many cases, additional …
applications. However, deep neural networks are not perfect. In many cases, additional …
Reachability-Based Robustness Verification of Deep Neural Networks with Emphasis on Safety-Critical Time-Series Applications
N Pal - 2024 - ir.vanderbilt.edu
The advancement of Deep Neural Network (DNN) technologies and their verification
methodologies has not fully extended to the realm of time-series neural network (NN) …
methodologies has not fully extended to the realm of time-series neural network (NN) …