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Bounding the Complexity of Formally Verifying Neural Networks: A
  Geometric Approach

Bounding the Complexity of Formally Verifying Neural Networks: A Geometric Approach

22 December 2020
James Ferlez
Yasser Shoukry
ArXivPDFHTML

Papers citing "Bounding the Complexity of Formally Verifying Neural Networks: A Geometric Approach"

5 / 5 papers shown
Title
Neurosymbolic Motion and Task Planning for Linear Temporal Logic Tasks
Neurosymbolic Motion and Task Planning for Linear Temporal Logic Tasks
Xiaowu Sun
Yasser Shoukry
48
11
0
11 Oct 2022
Polynomial-Time Reachability for LTI Systems with Two-Level Lattice
  Neural Network Controllers
Polynomial-Time Reachability for LTI Systems with Two-Level Lattice Neural Network Controllers
James Ferlez
Yasser Shoukry
29
1
0
20 Sep 2022
Fast BATLLNN: Fast Box Analysis of Two-Level Lattice Neural Networks
Fast BATLLNN: Fast Box Analysis of Two-Level Lattice Neural Networks
James Ferlez
Haitham Khedr
Yasser Shoukry
24
12
0
17 Nov 2021
Output Reachable Set Estimation and Verification for Multi-Layer Neural
  Networks
Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks
Weiming Xiang
Hoang-Dung Tran
Taylor T. Johnson
88
293
0
09 Aug 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
249
1,842
0
03 Feb 2017
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