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Found 6 papers, 2 papers with code
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
This paper serves as a comprehensive system description of version 2. 0 of the Marabou framework for formal analysis of neural networks.
DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace
Software development in the aerospace domain requires adhering to strict, high-quality standards.
Formally Explaining Neural Networks within Reactive Systems
We evaluate our approach on two popular benchmarks from the domain of automated navigation; and observe that our methods allow the efficient computation of minimal and minimum explanations, significantly outperforming the state of the art.
veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System
In this short paper, we present our ongoing work on the veriFIRE project -- a collaboration between industry and academia, aimed at using verification for increasing the reliability of a real-world, safety-critical system.
Minimal Multi-Layer Modifications of Deep Neural Networks
The novel repair procedure implemented in 3M-DNN computes a modification to the network's weights that corrects its behavior, and attempts to minimize this change via a sequence of calls to a backend, black-box DNN verification engine.
Neural Network Robustness as a Verification Property: A Principled Case Study
Neural networks are very successful at detecting patterns in noisy data, and have become the technology of choice in many fields.
