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As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data.
Variations of box plots
Robert McGill, John W Tukey, and Wayne A Larsen · 1978
Earlier work this paper cites.
Primer of applied regression and analysis of variance , volume 309
Stanton A Glantz, Bryan K Slinker, and Torsten B Neilands · 1990
Earlier work this paper cites.
The Nature of Statistical Learning Theory
Vladimir N. Vapnik · 1995
Earlier work this paper cites.
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
P. L. Bartlett · 1998
Earlier work this paper cites.
Boosting the margin: A new explanation for the effectiveness of voting methods
Robert E Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee, et al · 1998
Earlier work this paper cites.
On generalization bounds, projection profile, and margin distribution
Ashutosh Garg, Sariel Har-Peled, and Dan Roth · 2002
Earlier work this paper cites.
Pac-bayes margins
John Langford and John Shawe-Taylor · 2002
Earlier work this paper cites.
How boosting the margin can also boost classifier complexity
Lev Reyzin and Robert E Schapire · 2006
Earlier work this paper cites.
Min Lin, Qiang Chen, and Shuicheng Yan · 2013
Earlier work this paper cites.
Optimal margin distribution clustering, 2018
Teng Zhang and Zhi-Hua Zhou · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
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Shizhao Sun, Wei Chen, Liwei Wang, and Tie-Yan Liu · 2015
Cited alongside, same era.
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Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 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.
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Cited alongside, same era.
Soft-margin softmax for deep classification
Xuezhi Liang, Xiaobo Wang, Zhen Lei, Shengcai Liao, and Stan Z Li · 2017
Later among the works it cites.
Theory of deep learning iii: explaining the non-overfitting puzzle
Tomaso Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, and Hrushikesh Mhaskar · 2017
Later among the works it cites.
Multi-class optimal margin distribution machine
Teng Zhang and Zhi-Hua Zhou · 2017
Later among the works it cites.
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Jure Sokolic, Raja Giryes, Guillermo Sapiro, and Miguel R. D. Rodrigues · 2016
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Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
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A rotation and a translation suffice: Fooling cnns with simple transformations
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Optimal margin distribution network, 2019
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