lipschitz constant

Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers ICLR 2021 Functions with average smoothness: structure, algorithms, and learning COLT 2021 Large Norms of CNN Layers Do Not Hurt Adversarial Robustness AAAI 2021 Robust Implicit Networks via Non-Euclidean Contractions NeurIPS 2021 Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification NeurIPS 2021 SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation ICML 2021 Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective ICML 2021 Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons ICML 2021 Better depth-width trade-offs for neural networks through the lens of dynamical systems ICML 2020 Efficient Proximal Mapping of the 1-path-norm of Shallow Networks ICML 2020 Exactly Computing the Local Lipschitz Constant of ReLU Networks NeurIPS 2020 Lipschitz constant estimation of Neural Networks via sparse polynomial optimization ICLR 2020 Semialgebraic Optimization for Lipschitz Constants of ReLU Networks NeurIPS 2020 Stable Rank Normalization for Improved Generalization in Neural Networks and GANs ICLR 2020 Defensive Quantization: When Efficiency Meets Robustness ICLR 2019 L2-Nonexpansive Neural Networks ICLR 2019 Lipschitz Adaptivity with Multiple Learning Rates in Online Learning COLT 2019 RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications AAAI 2019 Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks NeurIPS 2018 Lipschitz regularity of deep neural networks: analysis and efficient estimation NeurIPS 2018 ConvNets with Smooth Adaptive Activation Functions for Regression AISTATS 2017 Global optimization of Lipschitz functions ICML 2017 Spectrally-normalized margin bounds for neural networks NeurIPS 2017 Batch Bayesian Optimization via Local Penalization AISTATS 2016 Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection ICML 2015