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cs.LG2023
On Reducing Undesirable Behavior in Deep Reinforcement Learning Models
Ophir M. Carmel, Guy Katz
Deep reinforcement learning (DRL) has proven extremely useful in a large variety of application domains. However, even successful DRL-based software can exhibit highly undesirable…
cs.LG2023
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…
cs.LG2023
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…