View source for Inverse Abstraction of Neural Networks Using Symbolic Interpolation

{{Paper
|Title=Inverse Abstraction of Neural Networks Using Symbolic Interpolation
|Authors=Sumanth Dathathri, Sicun Gao, Richard M. Murray
|Source=To appear, 2019 AAAI Conference on Artificial Intelligence
|Abstract=Neural networks in real-world applications have to satisfy critical properties such as safety and reliability. The analysis of such properties typically involves extracting informa- tion through computing pre-images of neural networks, but it is well-known that explicit computation of pre-images is intractable. We introduce new methods for computing compact symbolic abstractions of pre-images. Our approach relies on computing approximations that provably overapproximate and underapproximate the pre-images at all layers. The abstraction of pre-images enables formal analysis and knowl- edge extraction without modifying standard learning algo- rithms. We show how to use inverse abstractions to automatically extract simple control laws and compact representations for pre-images corresponding to unsafe outputs. We illustrate that the extracted abstractions are often interpretable and can be used for analyzing complex properties.
|URL=http://www.cds.caltech.edu/~murray/preprints/dgm19-aiaa.pdf
|Type=Conference paper
|ID=2018e
|Tag=dgm19-aiaa
|Funding=NSF VeHICaL
}}