T4V: exploring neural network architectures that improve the scalability of neural network verification
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Publication:6113995
DOI10.1007/978-3-031-22337-2_28zbMath1528.68230MaRDI QIDQ6113995
Vivian Lin, James Weimer, Insup Lee, Oleg Sokolsky, Radoslav Ivanov
Publication date: 10 August 2023
Published in: Lecture Notes in Computer Science (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Specification and verification (program logics, model checking, etc.) (68Q60)
Cites Work
- Verisig 2.0: verification of neural network controllers using Taylor model preconditioning
- Reluplex: an efficient SMT solver for verifying deep neural networks
- Reachability is NP-complete even for the simplest neural networks
- Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
- Case study
- Formal verification of neural network controlled autonomous systems
- Reachability analysis for neural feedback systems using regressive polynomial rule inference
- Verisig
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