Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks

11 April 2021

Papers citing "Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks"

9 / 9 papers shown

Title
Compositional Curvature Bounds for Deep Neural Networks Taha Entesari Sina Sharifi Mahyar Fazlyab AAML 42 0 0 07 Jun 2024
Weak Limits for Empirical Entropic Optimal Transport: Beyond Smooth Costs Alberto González Sanz Shayan Hundrieser OT 34 9 0 16 May 2023
Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian Patricia Pauli Ruigang Wang I. Manchester Frank Allgöwer 32 8 0 20 Mar 2023
Convolutional Neural Networks as 2-D systems Dennis Gramlich Patricia Pauli C. Scherer Frank Allgöwer C. Ebenbauer 3DV 36 8 0 06 Mar 2023
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks El Mehdi Achour Franccois Malgouyres Franck Mamalet 16 20 0 12 Aug 2021
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100 Sahil Singla Surbhi Singla S. Feizi AAML 38 54 0 05 Aug 2021
Bridging the Gap Between Adversarial Robustness and Optimization Bias Fartash Faghri Sven Gowal C. N. Vasconcelos David J. Fleet Fabian Pedregosa Nicolas Le Roux AAML 195 7 0 17 Feb 2021
Fast and accurate optimization on the orthogonal manifold without retraction Pierre Ablin Gabriel Peyré 59 27 0 15 Feb 2021
Learning Unitary Operators with Help From u(n) Stephanie L. Hyland Gunnar Rätsch 97 41 0 17 Jul 2016