Parseval networks : Improving robustness to adversarial examples, International Conference on Machine Learning, pp.854-863, 2017. ,
Evaluating and understanding the robustness of adversarial logit pairing, 2018. ,
Adversarial examples are a natural consequence of test error in noise, 2019. ,
, Shake-shake regularization, 2017.
, Explaining and harnessing adversarial examples, 2014.
Identity mappings in deep residual networks, European Conference on Computer Vision, pp.630-645, 2016. ,
Benchmarking neural network robustness to common corruptions and perturbations, Proceedings of the International Conference on Learning Representations, 2019. ,
, Multilayer feedforward networks are universal approximators. Neural networks, vol.2, pp.359-366, 1989.
Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012. ,
Adversarial machine learning at scale, 2016. ,
Laplacian networks : Bounding indicator function smoothness for neural networks robustness, Open Review, 2019. ,
Towards deep learning models resistant to adversarial attacks, 2018. ,
Understanding deep convolutional networks, Phil. Trans. R. Soc. A, vol.374, p.20150203, 2016. ,
Deep contextualized word representations, Proc. of NAACL, 2018. ,
Deconstructing the ladder network architecture, International Conference on Machine Learning, pp.2368-2376, 2016. ,
L2-nonexpansive neural networks, International Conference on Learning Representations, 2019. ,
, Intriguing properties of neural networks, 2013.
Google's neural machine translation system : Bridging the gap between human and machine translation, 2016. ,