Verifying low-dimensional input neural networks via input quantization

DOI10.1007/978-3-030-88806-0_10zbMath1497.68310arXiv2108.07961OpenAlexW3208555259MaRDI QIDQ2145327

Kai Jia, Martin C. Rinard

Publication date: 17 June 2022

Full work available at URL: https://arxiv.org/abs/2108.07961

zbMATH Keywords

neural network verificationACAS Xu network verificationverification by enumeration

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Roundoff error (65G50) Specification and verification (program logics, model checking, etc.) (68Q60) Networks and circuits as models of computation; circuit complexity (68Q06)

Related Items (2)

\textsf{CLEVEREST}: accelerating CEGAR-based neural network verification via adversarial attacks ⋮ Quantitative Verification for Neural Networks using ProbStars

Uses Software

• Reluplex
• NNV
• torchdiffeq

Cites Work

• Reluplex: an efficient SMT solver for verifying deep neural networks
• Improved geometric path enumeration for verifying ReLU neural networks

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