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Found 6 papers, 1 papers with code
nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems
Can engineering neural networks be approached in a disciplined way similar to how engineers build software for civil aircraft?
Runtime Monitoring Neuron Activation Patterns
For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training.
Towards Dependability Metrics for Neural Networks
Artificial neural networks (NN) are instrumental in realizing highly-automated driving functionality.
Verification of Binarized Neural Networks via Inter-Neuron Factoring
We study the problem of formal verification of Binarized Neural Networks (BNN), which have recently been proposed as a energy-efficient alternative to traditional learning networks.
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives
We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards.
Maximum Resilience of Artificial Neural Networks
The deployment of Artificial Neural Networks (ANNs) in safety-critical applications poses a number of new verification and certification challenges.
