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Wenbo Guo | Xue Liu | C. Lee Giles | Xinyu Xing | Alexander Ororbia | Qinglong Wang | Kaixuan Zhang | Alexander Ororbia | Qinglong Wang | Kaixuan Zhang | Xinyu Xing | Xue Liu | Wenbo Guo
[1] Urs A. Muller,et al. Learning long-range vision for autonomous off-road driving , 2009 .
[2] Razvan Pascanu,et al. Malware classification with recurrent networks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[3] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[4] Eric O. Postma,et al. Dimensionality Reduction: A Comparative Review , 2008 .
[5] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[6] Jorge Nocedal,et al. On the limited memory BFGS method for large scale optimization , 1989, Math. Program..
[7] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[8] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[9] Yan Xu,et al. Deep learning of feature representation with multiple instance learning for medical image analysis , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[10] Patrick D. McDaniel,et al. Adversarial Perturbations Against Deep Neural Networks for Malware Classification , 2016, ArXiv.
[11] S. Vavasis. Nonlinear optimization: complexity issues , 1991 .
[12] Jack W. Stokes,et al. Large-scale malware classification using random projections and neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[13] Antonio Criminisi,et al. Measuring Neural Net Robustness with Constraints , 2016, NIPS.
[14] Geoffrey E. Hinton. Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.
[15] Kevin W. Hamlen,et al. Frankenstein: Stitching Malware from Benign Binaries , 2012, WOOT.
[16] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[17] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[18] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[19] Quoc V. Le,et al. On optimization methods for deep learning , 2011, ICML.
[20] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[21] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[22] Blaine Nelson,et al. Can machine learning be secure? , 2006, ASIACCS '06.
[23] Blaine Nelson,et al. Adversarial machine learning , 2019, AISec '11.
[24] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[25] Yann LeCun,et al. Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers , 2012, ICML.
[26] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[27] David Slater,et al. Malicious Behavior Detection using Windows Audit Logs , 2015, AISec@CCS.
[28] Charles R. Johnson,et al. Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.
[29] Ian Goodfellow,et al. Deep Learning with Differential Privacy , 2016, CCS.
[30] David J. Fleet,et al. Adversarial Manipulation of Deep Representations , 2015, ICLR.
[31] Patrick D. McDaniel,et al. On the Effectiveness of Defensive Distillation , 2016, ArXiv.
[32] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[33] Dawn Xiaodong Song,et al. Delving into Transferable Adversarial Examples and Black-box Attacks , 2016, ICLR.
[34] 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).
[35] Hayit Greenspan,et al. Deep learning with non-medical training used for chest pathology identification , 2015, Medical Imaging.
[36] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[37] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[38] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[39] Daniel Kifer,et al. Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization , 2016, ArXiv.
[40] Christopher Potts,et al. Learning Word Vectors for Sentiment Analysis , 2011, ACL.
[41] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[42] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[43] Luca Maria Gambardella,et al. Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition , 2010, ArXiv.
[44] John W. Sammon,et al. A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.
[45] Zhenlong Yuan,et al. Droid-Sec: deep learning in android malware detection , 2015, SIGCOMM 2015.