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Dec, 2022
神经网络验证的优化符号化区间传播
Optimized Symbolic Interval Propagation for Neural Network Verification
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Philipp Kern, Marko Kleine Büning, Carsten Sinz
TL;DR
本文提出了一种新的基于符号区间传播和变量分裂的分支定界求解器DPNeurifyFV,该方法采用新的启发式算法来选择区间变量,以改善变量相关性问题,在结合其他改进措施的情况下,可以显著提高深度学习神经网络验证的效率,并在空中碰撞避免网络ACAS Xu上实现了运行时改进。
Abstract
neural networks
are increasingly applied in
safety critical domains
, their
verification
thus is gaining importance. A large class of recen
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