Muddling Labels for Regularization, a novel approach to generalization

17 February 2021

Papers citing "Muddling Labels for Regularization, a novel approach to generalization"

9 / 9 papers shown

Title
Implicit differentiation of Lasso-type models for hyperparameter optimization Quentin Bertrand Quentin Klopfenstein Mathieu Blondel Samuel Vaiter Alexandre Gramfort Joseph Salmon 104 66 0 20 Feb 2020
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay L. Smith 289 1,035 0 26 Mar 2018
Autostacker: A Compositional Evolutionary Learning System Boyuan Chen Harvey Wu Warren Mo I. Chattopadhyay Hod Lipson BDL 51 84 0 02 Mar 2018
Regularization for Deep Learning: A Taxonomy J. Kukačka Vladimir Golkov Daniel Cremers 88 336 0 29 Oct 2017
Cross-validation failure: small sample sizes lead to large error bars Gaël Varoquaux 64 527 0 23 Jun 2017
Self-Normalizing Neural Networks Günter Klambauer Thomas Unterthiner Andreas Mayr Sepp Hochreiter 502 2,521 0 08 Jun 2017
Hyperparameter optimization with approximate gradient Fabian Pedregosa 135 450 0 07 Feb 2016
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 384 7,981 0 13 Jun 2012
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning E. Brochu Vlad M. Cora Nando de Freitas GP 140 2,449 0 12 Dec 2010