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| authors | citekey | publish_date | publisher | location | pages | last_import | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
duttaOutputRangeAnalysis2018 | 2018-01-01 | Springer International Publishing | Cham | 121-138 | 2025-05-12 |
Indexing Information
Published: 2018-01
DOI 10.1007/978-3-319-77935-5_9 ISBN 978-3-319-77935-5
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[!Abstract] Given a neural network (NN) and a set of possible inputs to the network described by polyhedral constraints, we aim to compute a safe over-approximation of the set of possible output values. This operation is a fundamental primitive enabling the formal analysis of neural networks that are extensively used in a variety of machine learning tasks such as perception and control of autonomous systems. Increasingly, they are deployed in high-assurance applications, leading to a compelling use case for formal verification approaches. In this paper, we present an efficient range estimation algorithm that iterates between an expensive global combinatorial search using mixed-integer linear programming problems, and a relatively inexpensive local optimization that repeatedly seeks a local optimum of the function represented by the NN. We implement our approach and compare it with Reluplex, a recently proposed solver for deep neural networks. We demonstrate applications of our approach to computing flowpipes for neural network-based feedback controllers. We show that the use of local search in conjunction with mixed-integer linear programming solvers effectively reduces the combinatorial search over possible combinations of active neurons in the network by pruning away suboptimal nodes.>[!seealso] Related Papers
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Notes
!Paper Notes/Output Range Analysis for Deep Feedforward Neural Networks.md