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Found 8 papers, 1 papers with code
AGNES: Abstraction-guided Framework for Deep Neural Networks Security
One prominent application is image recognition in autonomous driving, where the correct classification of objects, such as traffic signs, is essential for safe driving.
Causal Analysis for Robust Interpretability of Neural Networks
The result is task-specific causal explanatory graphs that can audit model behavior and express the actual causes underlying its performance.
Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks
Runtime monitoring provides a more realistic and applicable alternative to verification in the setting of real neural networks used in industry.
Backdoor Mitigation in Deep Neural Networks via Strategic Retraining
Deep Neural Networks (DNN) are becoming increasingly more important in assisted and automated driving.
Interventional Black-Box Explanations
Finally, we believe that our method is orthogonal to logic-based explanation methods and can be leveraged to improve their explanations.
DeepAbstract: Neural Network Abstraction for Accelerating Verification
While abstraction is a classic tool of verification to scale it up, it is not used very often for verifying neural networks.
Towards Safety Verification of Direct Perception Neural Networks
We study the problem of safety verification of direct perception neural networks, where camera images are used as inputs to produce high-level features for autonomous vehicles to make control decisions.
Multi-Objective Approaches to Markov Decision Processes with Uncertain Transition Parameters
Markov decision processes (MDPs) are a popular model for performance analysis and optimization of stochastic systems.
