Murray at 05:21, 27 December 2018

2018-12-27T05:21:34Z

← Older revision		Revision as of 05:21, 27 December 2018
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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/~~aaaYY~~-~~place~~.pdf		\|URL=http://www.cds.caltech.edu/~murray/preprints/dgm19-aiaa.pdf
	\|Type=Conference paper		\|Type=Conference paper
	\|ID=2018e		\|ID=2018e

Murray: Created page with "{{Paper |Title=Inverse Abstraction of Neural Networks Using Symbolic Interpolation |Authors=Sumanth Dathathri, Sicun Gao, Richard M. Murray |Source=To appear, 2019 AAAI Confer..."

2018-12-27T05:21:22Z

Created page with "{{Paper |Title=Inverse Abstraction of Neural Networks Using Symbolic Interpolation |Authors=Sumanth Dathathri, Sicun Gao, Richard M. Murray |Source=To appear, 2019 AAAI Confer..."

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{{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/aaaYY-place.pdf
|Type=Conference paper
|ID=2018e
|Tag=dgm19-aiaa
|Funding=NSF VeHICaL
}}

Inverse Abstraction of Neural Networks Using Symbolic Interpolation - Revision history

Murray at 05:21, 27 December 2018

Murray: Created page with "{{Paper |Title=Inverse Abstraction of Neural Networks Using Symbolic Interpolation |Authors=Sumanth Dathathri, Sicun Gao, Richard M. Murray |Source=To appear, 2019 AAAI Confer..."