On the Lipschitz Constant of Deep Networks and Double Descent

28 January 2023

Papers citing "On the Lipschitz Constant of Deep Networks and Double Descent"

10 / 10 papers shown

Title
On Space Folds of ReLU Neural Networks Michal Lewandowski Hamid Eghbalzadeh Bernhard Heinzl Raphael Pisoni Bernhard A.Moser MLT 81 1 0 17 Feb 2025
Understanding the Role of Optimization in Double Descent Chris Liu Jeffrey Flanigan 32 0 0 06 Dec 2023
Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization Elan Rosenfeld Andrej Risteski 25 10 0 07 Nov 2023
On progressive sharpening, flat minima and generalisation L. MacDonald Jack Valmadre Simon Lucey 25 4 0 24 May 2023
Some Fundamental Aspects about Lipschitz Continuity of Neural Networks Grigory Khromov Sidak Pal Singh 29 7 0 21 Feb 2023
Why neural networks find simple solutions: the many regularizers of geometric complexity Benoit Dherin Michael Munn M. Rosca David Barrett 53 30 0 27 Sep 2022
Deep Double Descent via Smooth Interpolation Matteo Gamba Erik Englesson Marten Bjorkman Hossein Azizpour 63 10 0 21 Sep 2022
What Happens after SGD Reaches Zero Loss? --A Mathematical Framework Zhiyuan Li Tianhao Wang Sanjeev Arora MLT 90 98 0 13 Oct 2021
Benefits of depth in neural networks Matus Telgarsky 148 602 0 14 Feb 2016
Norm-Based Capacity Control in Neural Networks Behnam Neyshabur Ryota Tomioka Nathan Srebro 119 577 0 27 Feb 2015