Inverse Abstraction of Neural Networks Using Symbolic Interpolation: Difference between revisions
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|Source=To appear, 2019 AAAI Conference on Artificial Intelligence | |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. | |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/ | |URL=http://www.cds.caltech.edu/~murray/preprints/dgm19-aiaa.pdf | ||
|Type=Conference paper | |Type=Conference paper | ||
|ID=2018e | |ID=2018e | ||
Latest revision as of 05:21, 27 December 2018
| Title | Inverse Abstraction of Neural Networks Using Symbolic Interpolation |
|---|---|
| Authors | Sumanth Dathathri, Sicun Gao and 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. |
| Type | Conference paper |
| URL | http://www.cds.caltech.edu/~murray/preprints/dgm19-aiaa.pdf |
| DOI | |
| Tag | dgm19-aiaa |
| ID | 2018e |
| Funding | NSF VeHICaL |
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