暂无分享,去创建一个
[1] Omer Levy,et al. Neural Word Embedding as Implicit Matrix Factorization , 2014, NIPS.
[2] Blaine Nelson,et al. Adversarial machine learning , 2019, AISec '11.
[3] Peter Szolovits,et al. MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.
[4] D. Sculley,et al. Hidden Technical Debt in Machine Learning Systems , 2015, NIPS.
[5] Tadayoshi Kohno,et al. Securing Embedded User Interfaces: Android and Beyond , 2013, USENIX Security Symposium.
[6] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[8] Somesh Jha,et al. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.
[9] Blaine Nelson,et al. Poisoning Attacks against Support Vector Machines , 2012, ICML.
[10] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[11] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[12] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[13] Yurong Chen,et al. Dynamic Network Surgery for Efficient DNNs , 2016, NIPS.
[14] Xiaoqian Jiang,et al. A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences , 2016, JMIR medical informatics.
[15] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[16] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[17] Ehsaneddin Asgari,et al. Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics , 2015, PloS one.
[18] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[19] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[20] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[22] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[23] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[24] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[25] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[26] Ling Huang,et al. Query Strategies for Evading Convex-Inducing Classifiers , 2010, J. Mach. Learn. Res..
[27] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[28] Claudia Eckert,et al. Support vector machines under adversarial label contamination , 2015, Neurocomputing.
[29] Bin Ma,et al. Following Devil's Footprints: Cross-Platform Analysis of Potentially Harmful Libraries on Android and iOS , 2016, 2016 IEEE Symposium on Security and Privacy (SP).
[30] Suman Nath,et al. Brahmastra: Driving Apps to Test the Security of Third-Party Components , 2014, USENIX Security Symposium.
[31] Stephen P. Boyd,et al. General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming , 2017, 1703.07870.
[32] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[33] Quoc V. Le,et al. Exploiting Similarities among Languages for Machine Translation , 2013, ArXiv.
[34] Patrick D. McDaniel,et al. Adversarial Perturbations Against Deep Neural Networks for Malware Classification , 2016, ArXiv.
[35] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[36] Christopher Krügel,et al. TriggerScope: Towards Detecting Logic Bombs in Android Applications , 2016, 2016 IEEE Symposium on Security and Privacy (SP).
[37] Eduardo Valle,et al. Exploring the space of adversarial images , 2015, 2016 International Joint Conference on Neural Networks (IJCNN).
[38] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[40] Ling Huang,et al. ANTIDOTE: understanding and defending against poisoning of anomaly detectors , 2009, IMC '09.
[41] Blaine Nelson,et al. Can machine learning be secure? , 2006, ASIACCS '06.
[42] Erik Derr,et al. Reliable Third-Party Library Detection in Android and its Security Applications , 2016, CCS.
[43] Blaine Nelson,et al. The security of machine learning , 2010, Machine Learning.
[44] Fan Zhang,et al. Stealing Machine Learning Models via Prediction APIs , 2016, USENIX Security Symposium.
[45] Gal Chechik,et al. Euclidean Embedding of Co-occurrence Data , 2004, J. Mach. Learn. Res..