论文标题
实施输入输出规格的安全预测指标
Safe Predictors for Enforcing Input-Output Specifications
论文作者
论文摘要
我们提出了一种方法，用于设计正确的构造神经网络（以及其他机器学习模型），这些神经网络（以及其他机器学习模型）保证与算法培训之前，期间和之后的输入输出规格集合一致。我们的方法涉及为每组兼容约束设计一个约束的预测指标，并通过其预测的凸组合安全地组合它们。我们展示了我们在合成数据集和飞机避免碰撞问题的方法。
We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training. Our method involves designing a constrained predictor for each set of compatible constraints, and combining them safely via a convex combination of their predictions. We demonstrate our approach on synthetic datasets and an aircraft collision avoidance problem.
