About: Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #90815 Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis
sameAs	Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis
Title	Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2020-06-16
Creator	Jan Kronqvist Alessio Lomuscio Ruth Misener Elena Botoeva Panagiotis Kouvaros
status	peer reviewed published
Publisher	AAAI Press
abstract	We introduce an efficient method for the verification of ReLU-based feed-forward neural networks. We derive an automated procedure that exploits dependency relations between the ReLU nodes, thereby pruning the search tree that needs to be considered by MILP-based formulations of the verification problem. We augment the resulting algorithm with methods for input domain splitting and symbolic interval propagation. We present Venus, the resulting verification toolkit, and evaluate it on the ACAS collision avoidance networks and models trained on the MNIST and CIFAR-10 datasets. The experimental results obtained indicate considerable gains over the present state-of-the-art tools.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	QA 75 Electronic computers. Computer science
list of authors	https://kar.kent.ac.uk/id/eprint/90815#authors
presented at	The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020
issue	04
volume	34
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #90815 Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis

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