Verifying Low-dimensional Input Neural Networks via Input Quantization

18 August 2021

Papers citing "Verifying Low-dimensional Input Neural Networks via Input Quantization"

9 / 9 papers shown

Title
Learning Verifiable Control Policies Using Relaxed Verification Puja Chaudhury Alexander Estornell Michael Everett 27 0 0 23 Apr 2025
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops Nicholas Rober Michael Everett Songan Zhang Jonathan P. How 13 9 0 14 Oct 2022
Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems Nicholas Rober Sydney M. Katz Chelsea Sidrane Esen Yel Michael Everett Mykel J. Kochenderfer Jonathan P. How 19 26 0 28 Sep 2022
A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables Adrien Benamira Tristan Guérand Thomas Peyrin Trevor Yap Bryan Hooi 16 1 0 18 Aug 2022
Backward Reachability Analysis for Neural Feedback Loops Nicholas Rober Michael Everett Jonathan P. How 6 10 0 14 Apr 2022
Neural Network Compression of ACAS Xu Early Prototype is Unsafe: Closed-Loop Verification through Quantized State Backreachability Stanley Bak Hoang-Dung Tran AAML 19 15 0 17 Jan 2022
Verifying Quantized Neural Networks using SMT-Based Model Checking Luiz Sena Xidan Song E. Alves I. Bessa Edoardo Manino Lucas C. Cordeiro Eddie Batista de Lima Filho 20 11 0 10 Jun 2021
Exploiting Verified Neural Networks via Floating Point Numerical Error Kai Jia Martin Rinard AAML 27 34 0 06 Mar 2020
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 222 1,818 0 03 Feb 2017