Universal Lipschitz Approximation in Bounded Depth Neural Networks

9 April 2019

Papers citing "Universal Lipschitz Approximation in Bounded Depth Neural Networks"

8 / 8 papers shown

Title
1-Lipschitz Neural Networks are more expressive with N-Activations Bernd Prach Christoph H. Lampert AAML FAtt 29 0 0 10 Nov 2023
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources Feiyang Kang H. Just Anit Kumar Sahu R. Jia 61 10 0 05 Jul 2023
Data Topology-Dependent Upper Bounds of Neural Network Widths Sangmin Lee Jong Chul Ye 33 0 0 25 May 2023
Approximation of Lipschitz Functions using Deep Spline Neural Networks Sebastian Neumayer Alexis Goujon Pakshal Bohra M. Unser 50 16 0 13 Apr 2022
Globally-Robust Neural Networks Klas Leino Zifan Wang Matt Fredrikson AAML OOD 80 126 0 16 Feb 2021
L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization Mina Basirat P. Roth 19 8 0 27 Oct 2019
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 251 1,842 0 03 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 312 3,115 0 04 Nov 2016