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We present a formulation of deep learning that aims at producing a large margin classifier.
Support-vector networks
Cortes, Corinna and Vapnik, Vladimir · 1995
Earlier work this paper cites.
The Nature of Statistical Learning Theory
Vapnik, Vladimir N · 1995
Earlier work this paper cites.
Support vector regression machines
Drucker, Harris, Burges, Chris J. C., Kaufman, Linda, Smola, Alex, and Vapnik, Vladimir · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
Earlier work this paper cites.
Stability and generalization
Bousquet, Olivier and Elisseeff, André · 2002
Earlier work this paper cites.
Convex optimization
Boyd, Stephen and Vandenberghe, Lieven · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, Tijmen and Hinton, Geoffrey · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian J., and Fergus, Rob · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, Ian J, Shlens, Jonathon, and Szegedy, Christian · 2014
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Reed, Scott, Lee, Honglak, Anguelov, Dragomir, Szegedy, Christian, Erhan, Dumitru, and Rabinovich, Andrew · 2014
Earlier work this paper cites.
Training convolutional networks with noisy labels
Sukhbaatar, Sainbayar, Bruna, Joan, Paluri, Manohar, Bourdev, Lubomir, and Fergus, Rob · 2014
Earlier work this paper cites.
Learning with a strong adversary
Huang, Ruitong, Xu, Bing, Schuurmans, Dale, and Szepesvári, Csaba · 2015
Cited alongside, same era.
Semi-supervised learning with ladder networks
Rasmus, Antti, Berglund, Mathias, Honkala, Mikko, Valpola, Harri, and Raiko, Tapani · 2015
Cited alongside, same era.
Large margin deep neural networks: Theory and algorithms
Sun, Shizhao, Chen, Wei, Wang, Liwei, and Liu, Tie-Yan · 2015
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Tensorflow: A system for large-scale machine learning
Abadi, Martín, Barham, Paul, Chen, Jianmin, Chen, Zhifeng, Davis, Andy, Dean, Jeffrey, Devin, Matthieu, Ghemawat, Sanjay, Irving, Geoffrey, Isard, Michael, et al · 2016
Cited alongside, same era.
Adversarial Machine Learning at Scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Cited alongside, same era.
Zagoruyko, Sergey and Komodakis, Nikos · 2016
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Synthesizing robust adversarial examples
Athalye, Anish and Sutskever, Ilya · 2017
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Parseval networks: Improving robustness to adversarial examples
Cisse, Moustapha, Bojanowski, Piotr, Grave, Edouard, Dauphin, Yann, and Usunier, Nicolas · 2017
Later among the works it cites.
Deep bayesian active learning with image data
Gal, Yarin, Islam, Riashat, and Ghahramani, Zoubin · 2017
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Countering adversarial images using input transformations
Guo, Chuan, Rana, Mayank, Cissé, Moustapha, and van der Maaten, Laurens · 2017
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Large-margin softmax loss for convolutional neural networks
Liu, Weiyang, Wen, Yandong, Yu, Zhiding, and Yang, Meng · 2016
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Universal adversarial perturbations
Moosavi-Dezfooli, Seyed-Mohsen, Fawzi, Alhussein, Fawzi, Omar, and Frossard, Pascal · 2016
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Practical black-box attacks against deep learning systems using adversarial examples
Papernot, Nicolas, McDaniel, Patrick, Goodfellow, Ian, Jha, Somesh, Celik, Z Berkay, and Swami, Ananthram · 2016
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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Sharif, Mahmood, Bhagavatula, Sruti, Bauer, Lujo, and Reiter, Michael K · 2016
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Robust large margin deep neural networks
Sokolic, Jure, Giryes, Raja, Sapiro, Guillermo, and Rodrigues, Miguel R. D · 2016
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Rethinking the inception architecture for computer vision
Szegedy, Christian, Vanhoucke, Vincent, Ioffe, Sergey, Shlens, Jon, and Wojna, Zbigniew · 2016
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Matching networks for one shot learning
Vinyals, Oriol, Blundell, Charles, Lillicrap, Tim, Wierstra, Daan, et al · 2016
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, Matthias and Andriushchenko, Maksym · 2017
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Google’s cloud vision api is not robust to noise
Hosseini, Hossein, Xiao, Baicen, and Poovendran, Radha · 2017
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Soft-margin softmax for deep classification
Liang, Xuezhi, Wang, Xiaobo, Lei, Zhen, Liao, Shengcai, and Li, Stan Z · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, Aleksander, Makelov, Aleksandar, Schmidt, Ludwig, Tsipras, Dimitris, and Vladu, Adrian · 2017
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Margin maximization for robust classification using deep learning
Matyasko, Alexander and Chau, Lap-Pui · 2017
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Practical black-box attacks against machine learning
Papernot, Nicolas, McDaniel, Patrick, Goodfellow, Ian, Jha, Somesh, Celik, Z Berkay, and Swami, Ananthram · 2017
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The implicit bias of gradient descent on separable data
Soudry, Daniel, Hoffer, Elad, and Srebro, Nathan · 2017
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