We present techniques for automatically inferring formal properties of feed-forward neural networks. We observe that a significant part (if not all) of the logic of feed forward networks is captured in the activation status (on or off) of its neurons. We propose to extract patterns based on neuron decisions as preconditions that imply certain desirable output property e.g., the prediction being a certain class. Together, the inferred preconditions and the output property form a {\em contract} for the network. We present techniques to extract input properties} encoding convex predicates on the input space that imply given output properties and layer properties, representing network properties captured in the hidden layers that imply the desired output behavior. We apply our techniques on networks for the MNIST and ACASXU applications. Our experiments highlight the use of the inferred properties in a variety of tasks, such as explaining predictions, providing robustness guarantees, simplifying proofs, and network distillation.
Thu 14 Nov
10:40 - 12:20: Papers - Deep Models at Cortez 2&3 Chair(s): Nazareno AguirreDept. of Computer Science FCEFQyN, University of Rio Cuarto | ||||||||||||||||||||||||||||||||||||||||||
| 10:40 - 11:00 Talk | Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement LearningACM SIGSOFT Distinguished Paper Award Yan ZhengTianjin University, Xiaofei XieNanyang Technological University, Ting SuETH Zurich, Lei MaKyushu University, Jianye HaoTianjin University, Zhaopeng MengTianjin University, Yang LiuNanyang Technological University, Singapore, Ruimin ShenFuxi AI Lab in Netease, Yinfeng ChenFuxi AI Lab in Netease, Changjie FanFuxi AI Lab in Netease | |||||||||||||||||||||||||||||||||||||||||
| 11:00 - 11:20 Talk | A Study of Oracle Approximations in Testing Deep Learning Libraries | |||||||||||||||||||||||||||||||||||||||||
| 11:20 - 11:40 Talk | Property Inference for Deep Neural Networks Divya GopinathCarnegie Mellon University, Hayes ConverseThe University of Texas at Austin, Corina S. PasareanuCarnegie Mellon University Silicon Valley, NASA Ames Research Center, Ankur TalyGoogle | |||||||||||||||||||||||||||||||||||||||||
| 11:40 - 12:00 Talk | An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms Qianyu GuoTianjin University, Sen ChenNanyang Technological University, Singapore, Xiaofei XieNanyang Technological University, Lei MaKyushu University, Qiang HuKyushu University, Japan, Hongtao LiuTianjin University, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University, Li XiaohongTianJin University Pre-print | |||||||||||||||||||||||||||||||||||||||||
| 12:00 - 12:10 Demonstration | DeepMutation++: a Mutation Testing Framework for Deep Learning Systems Qiang HuKyushu University, Japan, Lei MaKyushu University, Xiaofei XieNanyang Technological University, Bing YuKyushu University, Japan, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University | |||||||||||||||||||||||||||||||||||||||||
| 12:10 - 12:20 Demonstration | DeepHunter: A Coverage-Guided Fuzzer for Deep Neural Networks Xiaofei XieNanyang Technological University, Hongxu ChenNanyang Technological University, Yi LiNanyang Technological University, Lei MaKyushu University, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University | |||||||||||||||||||||||||||||||||||||||||