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Deep learning models often raise privacy concerns as they leak information about their training data.
A simple weight decay can improve generalization
Krogh, A. and Hertz, J. A · 1992
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Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping
Caruana, R., Lawrence, S., and Giles, C. L · 2001
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Differential privacy: A survey of results
Dwork, C · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Understanding dropout
Baldi, P. and Sadowski, P. J · 2013
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Regularization of neural networks using dropconnect
Wan, L., Zeiler, M., Zhang, S., Le Cun, Y., and Fergus, R · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Privacy-preserving deep learning
Shokri, R. and Shmatikov, V · 2015
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Efficient object localization using convolutional networks
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., and Bregler, C · 2015
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Towards dropout training for convolutional neural networks
Wu, H. and Gu, X · 2015
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Cited alongside, same era.
On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2016
Cited alongside, same era.
The unintended consequences of overfitting: Training data inference attacks
Yeom, S., Fredrikson, M., and Jha, S · 2017
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The secret sharer: Measuring unintended neural network memorization & extracting secrets
Carlini, N., Liu, C., Kos, J., Erlingsson, Ú., and Song, D · 2018
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Membership inference attack against differentially private deep learning model
Rahman, M. A., Rahman, T., Laganière, R., Mohammed, N., and Wang, Y · 2018
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Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., and Backes, M · 2018
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Simple black-box adversarial attacks
Guo, C., Gardner, J., You, Y., Wilson, A. G., and Weinberger, K · 2019
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A closer look at memorization in deep networks
Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al · 2017
Cited alongside, same era.
Gastaldi, X · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Cited alongside, same era.
The space of transferable adversarial examples
Tramèr, F., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Cited alongside, same era.
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Terminal brain damage: Exposing the graceless degradation in deep neural networks under hardware fault attacks
Hong, S., Frigo, P., Kaya, Y., Giuffrida, C., and Dumitras, T · 2019
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Evaluating differentially private machine learning in practice
Jayaraman, B. and Evans, D · 2019
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Shallow-Deep Networks: Understanding and mitigating network overthinking
Kaya, Y., Hong, S., and Dumitra s · 2019
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When does label smoothing help?
Müller, R., Kornblith, S., and Hinton, G. E · 2019
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On the convergence of adam and beyond
Reddi, S. J., Kale, S., and Kumar, S · 2019
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Adversarial neural network inversion via auxiliary knowledge alignment
Yang, Z., Chang, E.-C., and Liang, Z · 2019
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