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Regularization is an effective way to promote the generalization performance of machine learning models.
A simple weight decay can improve generalization
Anders Krogh and John A Hertz · 1992
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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On the multivariate runs test
Norbert Henze, Mathew D Penrose, et al · 1999
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Adaptive dropout with rademacher complexity regularization
Ke Zhai and Huan Wang · 2001
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Model selection and estimation in regression with grouped variables
Ming Yuan and Yi Lin · 2006
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A spectral regularization framework for multi-task structure learning
Andreas Argyriou, Massimiliano Pontil, Yiming Ying, and Charles A Micchelli · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Structural regularized support vector machine: a framework for structural large margin classifier
Hui Xue, Songcan Chen, and Qiang Yang · 2011
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Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 2013
Earlier work this paper cites.
Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Searching for exotic particles in high-energy physics with deep learning
Pierre Baldi, Peter Sadowski, and Daniel Whiteson · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Structure regularization for structured prediction
Xu Sun · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Meta learning of bounds on the bayes classifier error
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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Noise-tolerant deep learning for histopathological image segmentation
Weizhi Li, Xiaoning Qian, and Jim Ji · 2017
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Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
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Group sparse regularization for deep neural networks
Simone Scardapane, Danilo Comminiello, Amir Hussain, and Aurelio Uncini · 2017
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
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Kevin R Moon, Alfred O Hero, and B Véronique Delouille · 2015
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Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
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Empirically estimable classification bounds based on a nonparametric divergence measure
Visar Berisha, Alan Wisler, Alfred O Hero III, and Andreas Spanias · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Disturblabel: Regularizing cnn on the loss layer
Lingxi Xie, Jingdong Wang, Zhen Wei, Meng Wang, and Qi Tian · 2016
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Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Learning to bound the multi-class bayes error
Salimeh Yasaei Sekeh, Brandon Oselio, and Alfred O Hero · 2018
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey Hinton · 2019
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