论文标题
安全的神经肯定学习，具有可区分的象征性执行
Safe Neurosymbolic Learning with Differentiable Symbolic Execution
论文作者
论文摘要
我们研究了使用神经网络以及符号，人工编写的代码的程序的学习最差安全参数的问题。这种神经肯定程序在许多安全关键领域中出现。但是，由于他们可以使用非不同的操作，因此很难使用现有的基于基于梯度的安全学习的方法来学习其参数。我们解决此问题的方法，可区分的符号执行（DSE），样品控制程序中的流程路径象征性地构建了这些路径沿这些路径的最坏情况“安全损失”，并使用加强估计器的概括通过程序操作将这些损失的梯度反射。我们评估了合成任务和现实基准测试的混合物的方法。我们的实验表明，DSE在这些任务上的最新diffai方法显着胜过。
We study the problem of learning worst-case-safe parameters for programs that use neural networks as well as symbolic, human-written code. Such neurosymbolic programs arise in many safety-critical domains. However, because they can use nondifferentiable operations, it is hard to learn their parameters using existing gradient-based approaches to safe learning. Our approach to this problem, Differentiable Symbolic Execution (DSE), samples control flow paths in a program, symbolically constructs worst-case "safety losses" along these paths, and backpropagates the gradients of these losses through program operations using a generalization of the REINFORCE estimator. We evaluate the method on a mix of synthetic tasks and real-world benchmarks. Our experiments show that DSE significantly outperforms the state-of-the-art DiffAI method on these tasks.
