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Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples.
On the uniform convergence of relative frequencies of events to their probabilities
V. Vapnik and A. Y. Chervonenkis · 1971
Earlier work this paper cites.
Improving generalization performance using double backpropagation
H. Drucker and Y. Le Cun · 1992
Earlier work this paper cites.
Transformation invariance in pattern recognition—tangent distance and tangent propagation
P. Simard, Y. LeCun, J. Denker, and B. Victorri · 1998
Earlier work this paper cites.
Statistical learning theory
V. N. Vapnik · 1998
Earlier work this paper cites.
Vicinal risk minimization
O. Chapelle, J. Weston, L. Bottou, and V. Vapnik · 2000
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 2001
Earlier work this paper cites.
SMOTE: synthetic minority over-sampling technique
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer · 2002
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al · 2012
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
One billion word benchmark for measuring progress in statistical language modeling
C. Chelba, T. Mikolov, M. Schuster, Q. Ge, T. Brants, P. Koehn, and T. Robinson · 2013
Earlier work this paper cites.
Speech recognition with deep recurrent neural networks
A. Graves, A.-r. Mohamed, and G. Hinton · 2013
Earlier work this paper cites.
UCI machine learning repository, 2013
M. Lichman · 2013
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
Cited alongside, same era.
ImageNet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
Cited alongside, same era.
Deep speech 2: End-to-end speech recognition in English and Mandarin
D. Amodei, S. Ananthanarayanan, R. Anubhai, J. Bai, E. Battenberg, C. Case, J. Casper, B. Catanzaro, Q. Cheng, G. Chen, et al · 2016
Sobolev training for neural networks
W. M. Czarnecki, S. Osindero, M. Jaderberg, G. Świrszcz, and R. Pascanu · 2017
Closest in time.
Dataset augmentation in feature space
T. DeVries and G. W. Taylor · 2017
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Accurate, large minibatch SGD: Training ImageNet in 1 hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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Improved training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
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Nearly-tight VC-dimension bounds for piecewise linear neural networks
N. Harvey, C. Liaw, and A. Mehrabian · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
M. Hein and M. Andriushchenko · 2017
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Cited alongside, same era.
Tutorial: Generative adversarial networks
I. Goodfellow · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al · 2016
Cited alongside, same era.
Rethinking the Inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2016
Cited alongside, same era.
A closer look at memorization in deep networks
D. Arpit, S. Jastrzebski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio, et al · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
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URL https://github.com/kuangliu/pytorch-cifar
K. Liu, 2017 · 2017
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Regularizing neural networks by penalizing confident output distributions
G. Pereyra, G. Tucker, J. Chorowski, Ł. Kaiser, and G. Hinton · 2017
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URL https://github.com/andreasveit
A. Veit, 2017 · 2017
Closest in time.
URL https://research.googleblog.com/2017/08/launching-speech-commands-dataset.html
P. Warden, 2017 · 2017
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Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2017
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URL https://github.com/pluskid/fitting-random-labels
C. Zhang, 2017 · 2017
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Random erasing data augmentation
Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang · 2017
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