Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks

From MaRDI portal
Publication:5096089

DOI10.1007/978-3-319-68167-2_19zbMath1495.68131arXiv1705.01320OpenAlexW2963054787MaRDI QIDQ5096089

Rüdiger Ehlers

Publication date: 12 August 2022

Published in: Automated Technology for Verification and Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1705.01320




Related Items (32)

Deep Statistical Model CheckingDiffRNN: differential verification of recurrent neural networks\textsf{BDD4BNN}: a BDD-based quantitative analysis framework for binarized neural networksVerisig 2.0: verification of neural network controllers using Taylor model preconditioningRobustness verification of semantic segmentation neural networks using relaxed reachabilityStatic analysis of ReLU neural networks with tropical polyhedraExploiting verified neural networks via floating point numerical errorReachability in Simple Neural NetworksSparse polynomial optimisation for neural network verificationSpeeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolioReluplex: a calculus for reasoning about deep neural networks\textsf{CLEVEREST}: accelerating CEGAR-based neural network verification via adversarial attacksNeural Network Verification Using Residual ReasoningT4V: exploring neural network architectures that improve the scalability of neural network verificationRobustness analysis of continuous-depth models with Lagrangian techniquesFast BATLLNN: Fast Box Analysis of Two-Level Lattice Neural NetworksTowards a unifying logical framework for neural networksVerifying feedforward neural networks for classification in Isabelle/HOLReachability is NP-complete even for the simplest neural networksAn SMT-based approach for verifying binarized neural networksSyReNN: a tool for analyzing deep neural networksVerification of piecewise deep neural networks: a star set approach with zonotope pre-filterProbabilistic guarantees for safe deep reinforcement learningHow Many Bits Does it Take to Quantize Your Neural Network?A survey of safety and trustworthiness of deep neural networks: verification, testing, adversarial attack and defence, and interpretabilityImproving neural network verification through spurious region guided refinementAdvances in verification of ReLU neural networksEnhancing robustness verification for deep neural networks via symbolic propagationReachable sets of classifiers and regression models: (non-)robustness analysis and robust trainingAn SMT theory of fixed-point arithmeticRobustness verification of ReLU networks via quadratic programmingReachability analysis of a general class of neural ordinary differential equations






This page was built for publication: Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks