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Computer Science > Machine Learning

arXiv:2103.00124 (cs)
[Submitted on 27 Feb 2021]

Title:NEUROSPF: A tool for the Symbolic Analysis of Neural Networks

Authors:Muhammad Usman, Yannic Noller, Corina Pasareanu, Youcheng Sun, Divya Gopinath
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Abstract:This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and translates them into a Java representation that is amenable for analysis using the Symbolic PathFinder symbolic execution tool. Notably, NEUROSPF encodes specialized peer classes for parsing the model's parameters, thereby enabling efficient analysis. With NEUROSPF the user has the flexibility to specify either the inputs or the network internal parameters as symbolic, promoting the application of program analysis and testing approaches from software engineering to the field of machine learning. For instance, NEUROSPF can be used for coverage-based testing and test generation, finding adversarial examples and also constraint-based repair of neural networks, thus improving the reliability of neural networks and of the applications that use them. Video URL: this https URL
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2103.00124 [cs.LG]
  (or arXiv:2103.00124v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2103.00124

Submission history

From: Muhammad Usman [view email]
[v1] Sat, 27 Feb 2021 04:28:11 UTC (292 KB)
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Muhammad Usman
Yannic Noller
Corina S. Pasareanu
Youcheng Sun
Divya Gopinath
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