From Weakly Supervised Learning to Biquality Learning: an Introduction

16 December 2020

Papers citing "From Weakly Supervised Learning to Biquality Learning: an Introduction"

26 / 26 papers shown

Title
Learning active learning at the crossroads? evaluation and discussion L. Desreumaux V. Lemaire 51 12 0 16 Dec 2020
Importance Reweighting for Biquality Learning Pierre Nodet V. Lemaire A. Bondu Antoine Cornuéjols NoLa 35 6 0 19 Oct 2020
Rethinking Importance Weighting for Deep Learning under Distribution Shift Tongtong Fang Nan Lu Gang Niu Masashi Sugiyama 48 139 0 08 Jun 2020
DivideMix: Learning with Noisy Labels as Semi-supervised Learning Junnan Li R. Socher Guosheng Lin NoLa 91 1,026 0 18 Feb 2020
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods Eyke Hüllermeier Willem Waegeman PER UD 196 1,405 0 21 Oct 2019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty Dan Hendrycks Mantas Mazeika Saurav Kadavath D. Song OOD SSL 50 944 0 28 Jun 2019
Are Anchor Points Really Indispensable in Label-Noise Learning? Xiaobo Xia Tongliang Liu N. Wang Bo Han Chen Gong Gang Niu Masashi Sugiyama NoLa 68 377 0 01 Jun 2019
On Symmetric Losses for Learning from Corrupted Labels Nontawat Charoenphakdee Jongyeong Lee Masashi Sugiyama NoLa 51 105 0 27 Jan 2019
Learning from positive and unlabeled data: a survey Jessa Bekker Jesse Davis 72 560 0 12 Nov 2018
Discovering General-Purpose Active Learning Strategies Ksenia Konyushkova Raphael Sznitman Pascal Fua 62 35 0 09 Oct 2018
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice Kunkun Pang Mingzhi Dong Yang Wu Timothy M. Hospedales 25 18 0 29 Sep 2018
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning Kunkun Pang Mingzhi Dong Yang Wu Timothy M. Hospedales OffRL 37 91 0 12 Jun 2018
Strong Baselines for Neural Semi-supervised Learning under Domain Shift Sebastian Ruder Barbara Plank 36 172 0 25 Apr 2018
Deep Co-Training for Semi-Supervised Image Recognition Siyuan Qiao Wei Shen Zhishuai Zhang Bo Wang Alan Yuille 55 450 0 15 Mar 2018
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise Dan Hendrycks Mantas Mazeika Duncan Wilson Kevin Gimpel NoLa 127 553 0 14 Feb 2018
Snorkel: Rapid Training Data Creation with Weak Supervision Alexander Ratner Stephen H. Bach Henry R. Ehrenberg Jason Alan Fries Sen Wu Christopher Ré 73 1,024 0 28 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 269 9,743 0 25 Oct 2017
Learning Active Learning from Data Ksenia Konyushkova Raphael Sznitman Pascal Fua 49 302 0 09 Mar 2017
Asymmetric Tri-training for Unsupervised Domain Adaptation Kuniaki Saito Yoshitaka Ushiku Tatsuya Harada 113 587 0 27 Feb 2017
Multiple Instance Learning: A Survey of Problem Characteristics and Applications M. Carbonneau Veronika Cheplygina Eric Granger G. Gagnon 56 624 0 11 Dec 2016
A Benchmark and Comparison of Active Learning for Logistic Regression Yazhou Yang Marco Loog 56 151 0 25 Nov 2016
Learning from Untrusted Data Moses Charikar Jacob Steinhardt Gregory Valiant FedML OOD 86 295 0 07 Nov 2016
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Giorgio Patrini A. Rozza A. Menon Richard Nock Zhuang Li NoLa 90 1,448 0 13 Sep 2016
Can Active Learning Experience Be Transferred? Hong-Min Chu Hsuan-Tien Lin 41 30 0 02 Aug 2016
Training Deep Neural Networks on Noisy Labels with Bootstrapping Scott E. Reed Honglak Lee Dragomir Anguelov Christian Szegedy D. Erhan Andrew Rabinovich NoLa 109 1,019 0 20 Dec 2014
One-Class Classification: Taxonomy of Study and Review of Techniques Shehroz S. Khan Michael G. Madden 68 567 0 30 Nov 2013