Norm-preserving Orthogonal Permutation Linear Unit Activation Functions (OPLU)

8 April 2016

Papers citing "Norm-preserving Orthogonal Permutation Linear Unit Activation Functions (OPLU)"

10 / 10 papers shown

Title
Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Bo-Han Lai Pin-Han Huang Bo-Han Kung Shang-Tse Chen 12 0 0 21 May 2025
Approximation theory for 1-Lipschitz ResNets Davide Murari Takashi Furuya Carola-Bibiane Schönlieb 26 0 0 17 May 2025
Convolutional Neural Networks as 2-D systems Dennis Gramlich Patricia Pauli C. Scherer Frank Allgöwer C. Ebenbauer 3DV 36 8 0 06 Mar 2023
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks Bernd Prach Christoph H. Lampert 39 36 0 05 Aug 2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness J. Liu Shreyas Padhy Jie Jessie Ren Zi Lin Yeming Wen Ghassen Jerfel Zachary Nado Jasper Snoek Dustin Tran Balaji Lakshminarayanan UQCV BDL 26 48 0 01 May 2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks Sebastian Neumayer Alexis Goujon Pakshal Bohra M. Unser 45 16 0 13 Apr 2022
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators S. Lowe Robert C. Earle Jason dÉon Thomas Trappenberg Sageev Oore 23 1 0 22 Oct 2021
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Zhe Liu Zi Lin Shreyas Padhy Dustin Tran Tania Bedrax-Weiss Balaji Lakshminarayanan UQCV BDL 37 437 0 17 Jun 2020
On orthogonality and learning recurrent networks with long term dependencies Eugene Vorontsov C. Trabelsi Samuel Kadoury C. Pal ODL 41 239 0 31 Jan 2017
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections Zakaria Mhammedi Andrew D. Hellicar Ashfaqur Rahman James Bailey 27 129 0 01 Dec 2016