Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks

12 June 2019

George J. Pappas

Papers citing "Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks"

50 / 106 papers shown

Title
Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees Rui Zhou Thanin Quartz H. Sterck Jun Liu 22 47 0 04 Jun 2022
Safety Certification for Stochastic Systems via Neural Barrier Functions Frederik Baymler Mathiesen S. Calvert Luca Laurenti 31 35 0 03 Jun 2022
CertiFair: A Framework for Certified Global Fairness of Neural Networks Haitham Khedr Yasser Shoukry FedML 26 20 0 20 May 2022
ALLSH: Active Learning Guided by Local Sensitivity and Hardness Shujian Zhang Chengyue Gong Xingchao Liu Pengcheng He Weizhu Chen Mingyuan Zhou 27 26 0 10 May 2022
Deep Unlearning via Randomized Conditionally Independent Hessians Ronak R. Mehta Sourav Pal Vikas Singh Sathya Ravi MU 27 81 0 15 Apr 2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks Sebastian Neumayer Alexis Goujon Pakshal Bohra M. Unser 47 16 0 13 Apr 2022
A Quantitative Geometric Approach to Neural-Network Smoothness Zehao Wang Gautam Prakriya S. Jha 43 13 0 02 Mar 2022
A Domain-Theoretic Framework for Robustness Analysis of Neural Networks Can Zhou R. A. Shaikh Yiran Li Amin Farjudian OOD 41 4 0 01 Mar 2022
Adversarial robustness of sparse local Lipschitz predictors Ramchandran Muthukumar Jeremias Sulam AAML 34 13 0 26 Feb 2022
Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods Charles Dawson Sicun Gao Chuchu Fan 43 232 0 23 Feb 2022
Controlling the Complexity and Lipschitz Constant improves polynomial nets Zhenyu Zhu Fabian Latorre Grigorios G. Chrysos V. Cevher 27 10 0 10 Feb 2022
Neural network training under semidefinite constraints Patricia Pauli Niklas Funcke Dennis Gramlich Mohamed Amine Msalmi Frank Allgöwer GAN 23 13 0 03 Jan 2022
Robust Graph Neural Networks via Probabilistic Lipschitz Constraints R. Arghal Eric Lei Shirin Saeedi Bidokhti 19 19 0 14 Dec 2021
A Simple and Efficient Sampling-based Algorithm for General Reachability Analysis T. Lew Lucas Janson Riccardo Bonalli Marco Pavone 32 18 0 10 Dec 2021
Over-the-Air Federated Learning with Retransmissions (Extended Version) Henrik Hellström Viktoria Fodor Carlo Fischione 29 2 0 19 Nov 2021
Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations Lars Lindemann Alexander Robey Lejun Jiang Satyajeet Das Stephen Tu Nikolai Matni 47 42 0 18 Nov 2021
Fast BATLLNN: Fast Box Analysis of Two-Level Lattice Neural Networks James Ferlez Haitham Khedr Yasser Shoukry 24 12 0 17 Nov 2021
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds Yujia Huang Huan Zhang Yuanyuan Shi J Zico Kolter Anima Anandkumar 41 76 0 02 Nov 2021
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives Zida Cheng Chuanwei Ruan Siheng Chen Sushant Kumar Ya Zhang 27 16 0 23 Oct 2021
Towards Understanding the Data Dependency of Mixup-style Training Muthuraman Chidambaram Xiang Wang Yuzheng Hu Chenwei Wu Rong Ge UQCV 49 24 0 14 Oct 2021
GoTube: Scalable Stochastic Verification of Continuous-Depth Models Sophie Gruenbacher Mathias Lechner Ramin Hasani Daniela Rus T. Henzinger S. Smolka Radu Grosu 26 17 0 18 Jul 2021
Provable Lipschitz Certification for Generative Models Matt Jordan A. Dimakis 22 14 0 06 Jul 2021
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting Shaoru Chen Eric Wong Zico Kolter Mahyar Fazlyab 47 15 0 16 Jun 2021
Analytical bounds on the local Lipschitz constants of ReLU networks Trevor Avant K. Morgansen FAtt 27 12 0 29 Apr 2021
Model Error Propagation via Learned Contraction Metrics for Safe Feedback Motion Planning of Unknown Systems Glen Chou N. Ozay Dmitry Berenson 32 25 0 18 Apr 2021
Linear systems with neural network nonlinearities: Improved stability analysis via acausal Zames-Falb multipliers Patricia Pauli Dennis Gramlich J. Berberich Frank Allgöwer 27 26 0 31 Mar 2021
Understanding the role of importance weighting for deep learning Da Xu Yuting Ye Chuanwei Ruan FAtt 39 43 0 28 Mar 2021
CLIP: Cheap Lipschitz Training of Neural Networks Leon Bungert René Raab Tim Roith Leo Schwinn Daniel Tenbrinck 32 32 0 23 Mar 2021
Provably Correct Training of Neural Network Controllers Using Reachability Analysis Xiaowu Sun Yasser Shoukry 22 7 0 22 Feb 2021
Globally-Robust Neural Networks Klas Leino Zifan Wang Matt Fredrikson AAML OOD 80 126 0 16 Feb 2021
Generalized Quantile Loss for Deep Neural Networks Dvir Ben-Or Michael Kolomenkin G. Shabat UQCV 14 5 0 28 Dec 2020
A case for new neural network smoothness constraints Mihaela Rosca T. Weber Arthur Gretton S. Mohamed AAML 35 48 0 14 Dec 2020
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints X. Li Xiangrui Li Deng Pan D. Zhu AAML 21 17 0 14 Dec 2020
Dissipative Deep Neural Dynamical Systems Ján Drgoňa Soumya Vasisht Aaron Tuor D. Vrabie 21 7 0 26 Nov 2020
Learning Certified Control using Contraction Metric Dawei Sun Susmit Jha Chuchu Fan 24 74 0 25 Nov 2020
Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty Andrew J. Taylor Victor D. Dorobantu Sarah Dean Benjamin Recht Yisong Yue Aaron D. Ames 27 35 0 21 Nov 2020
Learning Hybrid Control Barrier Functions from Data Lars Lindemann Haimin Hu Alexander Robey Hanwen Zhang Dimos V. Dimarogonas Stephen Tu Nikolai Matni 37 51 0 08 Nov 2020
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants Craig Knuth Glen Chou N. Ozay Dmitry Berenson 30 33 0 18 Oct 2020
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization P. Gyawali S. Ghimire Linwei Wang AAML 39 7 0 23 Sep 2020
SoK: Certified Robustness for Deep Neural Networks Linyi Li Tao Xie Bo-wen Li AAML 35 128 0 09 Sep 2020
Sampling-based Reachability Analysis: A Random Set Theory Approach with Adversarial Sampling T. Lew Marco Pavone AAML 30 53 0 24 Aug 2020
Convergence of Deep Fictitious Play for Stochastic Differential Games Jiequn Han Ruimeng Hu Jihao Long 29 19 0 12 Aug 2020
Adversarial Training Reduces Information and Improves Transferability M. Terzi Alessandro Achille Marco Maggipinto Gian Antonio Susto AAML 24 23 0 22 Jul 2020
ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers James Ferlez Mahmoud M. Elnaggar Yasser Shoukry C. Fleming AAML 62 33 0 16 Jun 2020
Provable tradeoffs in adversarially robust classification Yan Sun Hamed Hassani David Hong Alexander Robey 23 53 0 09 Jun 2020
The Lipschitz Constant of Self-Attention Hyunjik Kim George Papamakarios A. Mnih 14 136 0 08 Jun 2020
Scalable Plug-and-Play ADMM with Convergence Guarantees Yu Sun Zihui Wu Xiaojian Xu B. Wohlberg Ulugbek S. Kamilov BDL 35 74 0 05 Jun 2020
Joint Multi-Dimension Pruning via Numerical Gradient Update Zechun Liu Xinming Zhang Zhiqiang Shen Zhe Li Yichen Wei Kwang-Ting Cheng Jian Sun 47 19 0 18 May 2020
Training robust neural networks using Lipschitz bounds Patricia Pauli Anne Koch J. Berberich Paul Kohler Frank Allgöwer 19 156 0 06 May 2020
Learning Control Barrier Functions from Expert Demonstrations Alexander Robey Haimin Hu Lars Lindemann Hanwen Zhang Dimos V. Dimarogonas Stephen Tu Nikolai Matni 31 202 0 07 Apr 2020