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Inspired by biophysical principles underlying nonlinear dendritic computation in neural circuits, we develop a scheme to train deep neural networks to make them robust to adversarial attacks.
Methods of information geometry
Amari, S. and Nagaoka, H · 1993
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Biophysics of computation: Information processing in single neurons
Koch, C · 1999
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Dendritic computation
London, M. and Häusser, M · 2005
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Representational similarity analysis – connecting the branches of systems neuroscience
Kriegeskorte, N., Mur, M., and Bandettini, P · 2008
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Contractive auto-encoders: Explicit invariance during feature extraction
Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y · 2011
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., and Kingsbury, B · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
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Saturating auto-encoders
Goroshin, R. and LeCun, Y · 2013
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The log-dynamic brain: how skewed distributions affect network operations
Buzsáki, G. and Mizuseki, K · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J., and Fergus, R · 2014
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E., Seibert, D., and DiCarlo, J. J · 2014
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
Deep neural networks: A new framework for modeling biological vision and brain information processing
Kriegeskorte, N · 2015
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Deep knowledge tracing
Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., and Sohl-Dickstein, J · 2015
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Deep learning models of the retinal response to natural scenes
McIntosh, L. T., Maheswaranathan, N., Nayebi, A., Ganguli, S., and Baccus, S · 2016
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Distributional smoothing with virtual adversarial training
Miyato, T., Maeda, S., Koyama, M., Nakae, K., and Ishii, S · 2016
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Exponential expressivity in deep neural networks through transient chaos
Poole, B., Lahiri, S., Raghu, M., Sohl-Dickstein, J., and Ganguli, S · 2016
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Dendrites (3rd ed)
Stuart, G., Spruston, N., and Häusser, M · 2016
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Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Cited alongside, same era.
Towards deep neural network architectures robust to adversarial examples
Gu, S. and Rigazio, L · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., S. Jha, M. Fredrikson, Celik, Z. B., and Swami, A
Cited in the paper.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A
Cited in the paper.
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Adversarial perturbations of deep neural networks
Warde-Farley, D. and Goodfellow, I. J · 2016
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