Learning with Confident Examples: Rank Pruning for Robust Classification
with Noisy Labels

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Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

4 May 2017

Curtis G. Northcutt

Isaac L. Chuang

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Papers citing "Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels"

11 / 61 papers shown

Title
Classification with imperfect training labels T. Cannings Yingying Fan R. Samworth 67 47 0 29 May 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels Zhilu Zhang M. Sabuncu NoLa 148 2,623 0 20 May 2018
Learning to detect chest radiographs containing lung nodules using visual attention networks E. Pesce P. Ypsilantis S. Withey R. Bakewell Vicky Goh Giovanni Montana 65 109 0 04 Dec 2017
Information-Theoretic Representation Learning for Positive-Unlabeled Classification Tomoya Sakai Gang Niu Masashi Sugiyama 25 2 0 15 Oct 2017
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples Haw-Shiuan Chang Erik Learned-Miller Andrew McCallum 100 355 0 24 Apr 2017
Training Deep Neural Networks on Noisy Labels with Bootstrapping Scott E. Reed Honglak Lee Dragomir Anguelov Christian Szegedy D. Erhan Andrew Rabinovich NoLa 135 1,023 0 20 Dec 2014
A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models Marc Claesen F. Smet Johan A. K. Suykens B. De Moor NoLa 102 97 0 13 Feb 2014
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising Gilles Blanchard Marek Flaska G. Handy Sara Pozzi Clayton Scott NoLa 120 244 0 05 Mar 2013
Predicting accurate probabilities with a ranking loss A. Menon Xiaoqian Jiang Shankar Vembu Charles Elkan L. Ohno-Machado 94 72 0 18 Jun 2012
Multiple Kernel Learning from Noisy Labels by Stochastic Programming Tianbao Yang M. Mahdavi Rong Jin Lijun Zhang Yang Zhou NoLa 98 27 0 18 Jun 2012
A bagging SVM to learn from positive and unlabeled examples F. Mordelet Jean-Philippe Vert 117 289 0 05 Oct 2010