Fetching the paper…
Reading the bibliography…
Many learning tasks in machine learning can be viewed as taking a gradient step towards minimizing the average loss of a batch of examples in each training iteration.
The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
Lev M Bregman · 1967
Earlier work this paper cites.
The weighted majority algorithm
Nick Littlestone and Manfred K Warmuth · 1994
Earlier work this paper cites.
Parameterisation of a stochastic model for human face identification
F. S. Samaria and A. C. Harter · 1994
Earlier work this paper cites.
Using and combining predictors that specialize
Y. Freund, R. E. Schapire, Y. Singer, and M. K. Warmuth · 1997
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
Earlier work this paper cites.
Exponentiated gradient versus gradient descent for linear predictors
Jyrki Kivinen and Manfred K Warmuth · 1997
Earlier work this paper cites.
Tracking the best expert
M. Herbster and M. K. Warmuth · 1998
Earlier work this paper cites.
Tracking a small set of experts by mixing past posteriors
O. Bousquet and M. K. Warmuth · 2002
Earlier work this paper cites.
Class noise vs. attribute noise: A quantitative study of their impacts
Xingquan Zhu and Xindong Wu · 2004
Earlier work this paper cites.
Active learning with real annotation costs
Burr Settles, Mark Craven, and Lewis Friedland · 2008
Earlier work this paper cites.
Randomized online pca algorithms with regret bounds that are logarithmic in the dimension
Manfred K Warmuth and Dima Kuzmin · 2008
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
L1-norm-based 2dpca
Xuelong Li, Yanwei Pang, and Yuan Yuan · 2010
Earlier work this paper cites.
Principal Component Analysis
Ian Jolliffe · 2011
Earlier work this paper cites.
AVA: A large-scale database for aesthetic visual analysis
Naila Murray, Luca Marchesotti, and Florent Perronnin · 2012
Earlier work this paper cites.
Face recognition: From theory to applications
Harry Wechsler, Jonathon P Phillips, Vicki Bruce, Francoise Fogelman Soulie, and Thomas S Huang · 2012
Earlier work this paper cites.
Optimal mean robust principal component analysis
Feiping Nie, Jianjun Yuan, and Heng Huang · 2014
Earlier work this paper cites.
Robust 2dpca with non-greedy l1-norm maximization for image analysis
Rong Wang, Feiping Nie, Xiaojun Yang, Feifei Gao, and Minli Yao · 2014
Earlier work this paper cites.
Learning from multiple annotators with varying expertise
Yan Yan, Rómer Rosales, Glenn Fung, Ramanathan Subramanian, and Jennifer Dy · 2014
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Training convolutional networks with noisy labels, 2015
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2015
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
Cited alongside, same era.
Blur image detection using laplacian operator and open-cv
Raghav Bansal, G. Raj, and T. Choudhury · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Learned perceptual image enhancement
Hossein Talebi and Peyman Milanfar · 2018
Later among the works it cites.
NIMA: Neural image assessment
Hossein Talebi and Peyman Milanfar · 2018
Later among the works it cites.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert R Sabuncu · 2018
Later among the works it cites.
Robust bi-tempered logistic loss based on Bregman divergences
Ehsan Amid, Manfred K Warmuth, Rohan Anil, and Tomer Koren · 2019
Later among the works it cites.
Two-temperature logistic regression based on the Tsallis divergence
Ehsan Amid, Manfred K Warmuth, and Sriram Srinivasan · 2019
Later among the works it cites.
Peer loss functions: Learning from noisy labels without knowing noise rates
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
Cited alongside, same era.
Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
Cited alongside, same era.
Mentornet: Regularizing very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2017
Cited alongside, same era.
Learning deep networks from noisy labels with dropout regularization
Ishan Jindal, Matthew S. Nokleby, and Xuewen Chen · 2017
Cited alongside, same era.
Making deep neural networks robust to label noise: a loss correction approach, 2017
Giorgio Patrini, Alessandro Rozza, Aditya Menon, Richard Nock, and Lizhen Qu · 2017
Cited alongside, same era.
Deep learning is robust to massive label noise
David Rolnick, Andreas Veit, Serge Belongie, and Nir Shavit · 2017
Cited alongside, same era.
Yang Liu and Hongyi Guo · 2019
Later among the works it cites.
Data parameters: A new family of parameters for learning a differentiable curriculum
Shreyas Saxena, Oncel Tuzel, and Dennis DeCoste · 2019
Later among the works it cites.
Derivative manipulation for general example weighting
Xinshao Wang, Elyor Kodirov, Yang Hua, and Neil M Robertson · 2019
Later among the works it cites.
Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey · 2019
Later among the works it cites.
How does disagreement help generalization against label corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, and Masashi Sugiyama · 2019
Later among the works it cites.
Robust principal component analysis with adaptive neighbors
Rui Zhang and Hanghang Tong · 2019
Later among the works it cites.
Label noise types and their effects on deep learning
Görkem Algan and Ilkay Ulusoy · 2020
Later among the works it cites.
Dividemix: Learning with noisy labels as semi-supervised learning, 2020
Junnan Li, Richard Socher, and Steven C. H. Hoi · 2020
Later among the works it cites.
Early-learning regularization prevents memorization of noisy labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda · 2020
Later among the works it cites.
Early-learning regularization prevents memorization of noisy labels, 2020
Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda · 2020
Later among the works it cites.
A deep learning system for differential diagnosis of skin diseases
Yuan Liu, Ayush Jain, Clara Eng, David H Way, Kang Lee, Peggy Bui, Kimberly Kanada, Guilherme de Oliveira Marinho, Jessica Gallegos, Sara Gabriele, et al · 2020
Later among the works it cites.
Normalized loss functions for deep learning with noisy labels, 2020
Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey · 2020
Later among the works it cites.
Matt Poyser, Amir Atapour-Abarghouei, and Toby P Breckon · 2020
Later among the works it cites.
Deep perceptual image quality assessment for compression
Juan Carlos Mier, Eddie Huang, Hossein Talebi, Feng Yang, and Peyman Milanfar · 2021
Closest in time.
Robust early-learning: Hindering the memorization of noisy labels
Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang · 2021
Closest in time.