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Found 2 papers, 1 papers with code
Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
A promising approach for providing strong guarantees on an agent's behavior is to use Neural Lyapunov Barrier (NLB) certificates, which are learned functions over the system whose properties indirectly imply that an agent behaves as desired.
Towards Efficient Verification of Quantized Neural Networks
Quantization replaces floating point arithmetic with integer arithmetic in deep neural network models, providing more efficient on-device inference with less power and memory.
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