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Modern neural networks have the capacity to overfit noisy labels frequently found in real-world datasets.
Accelerated greedy algorithms for maximizing submodular set functions
Michel Minoux · 1978
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Early stopping and non-parametric regression: an optimal data-dependent stopping rule
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Training deep neural networks on noisy labels with bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2014
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Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Learning with symmetric label noise: The importance of being unhinged
Brendan Van Rooyen, Aditya Menon, and Robert C Williamson · 2015
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Greedy column subset selection: New bounds and distributed algorithms
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Embracing error to enable rapid crowdsourcing
Ranjay A Krishna, Kenji Hata, Stephanie Chen, Joshua Kravitz, David A Shamma, Li Fei-Fei, and Michael S Bernstein · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Understanding deep learning requires rethinking generalization
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A closer look at memorization in deep networks
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Input sparsity time low-rank approximation via ridge leverage score sampling
Michael B Cohen, Cameron Musco, and Christopher Musco · 2017
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
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Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, and Li-Jia Li · 2017
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Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Making deep neural networks robust to label noise: A loss correction approach
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
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Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2019
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A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
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A generalization theory of gradient descent for learning over-parameterized deep relu networks
Yuan Cao and Quanquan Gu · 2019
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Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
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Learning from noisy large-scale datasets with minimal supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge Belongie · 2017
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Early stopping for kernel boosting algorithms: A general analysis with localized complexities
Yuting Wei, Fanny Yang, and Martin J Wainwright · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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Understanding and utilizing deep neural networks trained with noisy labels
Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang · 2019
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Gradient descent finds global minima of deep neural networks
Simon Du, Jason Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2019
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Understanding generalization of deep neural networks trained with noisy labels
Wei Hu, Zhiyuan Li, and Dingli Yu · 2019
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Deep learning with noisy labels: exploring techniques and remedies in medical image analysis
Davood Karimi, Haoran Dou, Simon K Warfield, and Ali Gholipour · 2019
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Mingchen Li, Mahdi Soltanolkotabi, and Samet Oymak · 2019
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Coresets for data-efficient training of machine learning models
Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec · 2019
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Xinshao Wang, Yang Hua, Elyor Kodirov, and Neil M Robertson · 2019
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Are anchor points really indispensable in label-noise learning?
Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama · 2019
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L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise
Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang · 2019
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Heteroskedastic and imbalanced deep learning with adaptive regularization
Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, and Tengyu Ma · 2020
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Towards moderate overparameterization: global convergence guarantees for training shallow neural networks
Samet Oymak and Mahdi Soltanolkotabi · 2020
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Distilling effective supervision from severe label noise
Han Zhang, Honglak Lee, Sercan Arik, Tomas Pfister, and Zizhao Zhang · 2020
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