Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation

13 October 2022

Papers citing "Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation"

10 / 10 papers shown

Title
TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification Dongyoon Yang Jihu Lee Yongdai Kim 29 0 0 10 May 2025
Adaptive Branch-and-Bound Tree Exploration for Neural Network Verification Kota Fukuda Guanqin Zhang Zhenya Zhang Yulei Sui Jianjun Zhao 45 0 0 02 May 2025
Neural Network Verification with Branch-and-Bound for General Nonlinearities Zhouxing Shi Qirui Jin Zico Kolter Suman Jana Cho-Jui Hsieh Huan Zhang 48 11 0 31 May 2024
Explicit Lipschitz Value Estimation Enhances Policy Robustness Against Perturbation Xulin Chen Ruipeng Liu Garret E. Katz 42 0 0 22 Apr 2024
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization Mahyar Fazlyab Taha Entesari Aniket Roy Ramalingam Chellappa AAML 16 11 0 29 Sep 2023
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources Feiyang Kang H. Just Anit Kumar Sahu R. Jia 59 10 0 05 Jul 2023
CQnet: convex-geometric interpretation and constraining neural-network trajectories Bas Peters 32 0 0 09 Feb 2023
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing Yatong Bai Brendon G. Anderson Aerin Kim Somayeh Sojoudi AAML 36 18 0 29 Jan 2023
Globally-Robust Neural Networks Klas Leino Zifan Wang Matt Fredrikson AAML OOD 80 125 0 16 Feb 2021
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 249 1,838 0 03 Feb 2017