119 citations · 468 across the 39 of their papers we have counts for
43 papers
Formally Explaining Neural Networks within Reactive Systems
Shahaf Bassan, Guy Amir, Davide Corsi +2
Deep neural networks (DNNs) are increasingly being used as controllers in reactive systems. However, DNNs are highly opaque, which renders it difficult to explain and justify their…
Towards a Certified Proof Checker for Deep Neural Network Verification
Remi Desmartin, Omri Isac, Grant Passmore +3
Recent developments in deep neural networks (DNNs) have led to their adoption in safety-critical systems, which in turn has heightened the need for guaranteeing their safety. These…
DelBugV: Delta-Debugging Neural Network Verifiers
Raya Elsaleh, Guy Katz
Deep neural networks (DNNs) are becoming a key component in diverse systems across the board. However, despite their success, they often err miserably; and this has triggered signi…
DNN Verification, Reachability, and the Exponential Function Problem
Omri Isac, Yoni Zohar, Clark Barrett +1
Deep neural networks (DNNs) are increasingly being deployed to perform safety-critical tasks. The opacity of DNNs, which prevents humans from reasoning about them, presents new saf…
Verifying Generalization in Deep Learning
Guy Amir, Osher Maayan, Tom Zelazny +2
Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the state of the art in numerous application domains. However, DNN-based decision rules are notor…
OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks
Xingwu Guo, Ziwei Zhou, Yueling Zhang +2
Occlusion is a prevalent and easily realizable semantic perturbation to deep neural networks (DNNs). It can fool a DNN into misclassifying an input image by occluding some segments…