论文标题
使用基于方案的建模进行深入的深入学习
Guarded Deep Learning using Scenario-Based Modeling
论文作者
论文摘要
深度神经网络（DNN）变得普遍，通常超过手动创建的系统。不幸的是，DNN模型对人类来说是不透明的，在部署时可能会以意外的方式行事。一种允许更安全的DNN模型部署的方法要求使用手工制作的替代规则来增强它们，这些规则可在满足某些标准时由DNN模型做出的决定。在这里，我们建议通过将这些替代规则表示为简单而直观的场景，将DNN和基于方案的建模范式汇总在一起。这种方法可以导致人类可理解的规则，但也足够表达和强大，可以提高模型的整体安全性。我们描述了如何将基于方案的建模扩展到此新设置，并在多个DNN模型上演示了我们提出的技术。
Deep neural networks (DNNs) are becoming prevalent, often outperforming manually-created systems. Unfortunately, DNN models are opaque to humans, and may behave in unexpected ways when deployed. One approach for allowing safer deployment of DNN models calls for augmenting them with hand-crafted override rules, which serve to override decisions made by the DNN model when certain criteria are met. Here, we propose to bring together DNNs and the well-studied scenario-based modeling paradigm, by expressing these override rules as simple and intuitive scenarios. This approach can lead to override rules that are comprehensible to humans, but are also sufficiently expressive and powerful to increase the overall safety of the model. We describe how to extend and apply scenario-based modeling to this new setting, and demonstrate our proposed technique on multiple DNN models.
