1 Adversarial Perturbations of Deep Neural Networks

[1]  Ananthram Swami,et al.  Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples , 2016, ArXiv.

[2]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.

[3]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Ananthram Swami,et al.  The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).

[5]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[6]  Jost Tobias Springenberg,et al.  Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks , 2015, ICLR.

[7]  David J. Fleet,et al.  Adversarial Manipulation of Deep Representations , 2015, ICLR.

[8]  Ananthram Swami,et al.  Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).

[9]  Matthias Bethge,et al.  A note on the evaluation of generative models , 2015, ICLR.

[10]  Gabriel Kreiman,et al.  Unsupervised Learning of Visual Structure using Predictive Generative Networks , 2015, ArXiv.

[11]  Oriol Vinyals,et al.  Towards Principled Unsupervised Learning , 2015, ArXiv.

[12]  Shin Ishii,et al.  Distributional Smoothing with Virtual Adversarial Training , 2015, ICLR 2016.

[13]  Jason Yosinski,et al.  Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Ian J. Goodfellow,et al.  On distinguishability criteria for estimating generative models , 2014, ICLR.

[15]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[17]  Daan Wierstra,et al.  Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.

[18]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[19]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[20]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[21]  Andrew L. Maas.  Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[22]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[23]  Aapo Hyvärinen,et al.  Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.

[24]  Yann LeCun,et al.  What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[25]  J. Lubar,et al.  EEG Coherence Effects of Audio-Visual Stimulation (AVS) at Dominant and Twice Dominant Alpha Frequency , 2005 .

[26]  R. J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[27]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[28]  J. O. Robinson.  The Psychology of Visual Illusion , 1972 .