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Deep neural networks have had enormous impact on various domains of computer science, considerably outperforming previous state of the art machine learning techniques.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in CVPR09 , 2009
2009
Earlier work this paper cites.
G. T. Tsenov and V. M. Mladenov, “Speech recognition using neural networks,” in 10th Symposium on Neural Network Applications in Electrical Engineering , Sept 2010, pp. 181–186
2010
Earlier work this paper cites.
Y. LeCun and C. Cortes, “MNIST handwritten digit database,” 2010. [Online]. Available: http://yann.lecun.com/exdb/mnist/
2010
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 , ser. NIPS’12. USA: Curran Associates Inc., 2012, pp. 1097–1105. [Online]. Available: http://dl.acm.org/citation.cfm?id=2999134.2999257
2012
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Processing Magazine , vol. 29, no. 6, pp. 82–97, Nov 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing , May 2013, pp. 6645–6649
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
E. Tokuda, H. Pedrini, and A. Rocha, “Computer generated images vs. digital photographs: A synergetic feature and classifier combination approach,” J. Vis. Comun. Image Represent. , vol. 24, no. 8, pp. 1276–1292, Nov. 2013. [Online]. Available: http://dx.doi.org/10.1016/j.jvcir.2013.08.009
2013
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 2014
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
D. Chen and C. D. Manning, “A fast and accurate dependency parser using neural networks,” in EMNLP , 2014
2014
Cited alongside, same era.
C. Dos Santos and M. Gatti de Bayser, “Deep convolutional neural networks for sentiment analysis of short texts,” 08 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
L. Muñoz González, B. Biggio, A. Demontis, A. Paudice, V. Wongrassamee, E. C. Lupu, and F. Roli, “Towards poisoning of deep learning algorithms with back-gradient optimization,” in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security , ser. AISec ’17. New York, NY, USA: ACM, 2017, pp. 27–38. [Online]. Available: http://doi.acm.org/10.1145/3128572.3140451
2017
Later among the works it cites.
T. Gu, B. Dolan-Gavitt, and S. Garg, “Badnets: Identifying vulnerabilities in the machine learning model supply chain,” 08 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
R. W. Funda Güneş and P.-Y. Tan, “Stacked ensemble models for improved prediction accuracy,” 2017. [Online]. Available: support.sas.com/resources/papers/proceedings17/SAS0437-2017.pdf
2017
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2014
Cited alongside, same era.
S. Christian, W. Liu, and Y. Jia, “Going deeper with convolutions,” pp. 1–9, 01 2015
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 770–778, 2016
2016
Cited alongside, same era.
F. Tramer, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction apis,” in USENIX Security Symposium , 2016
2016
Cited alongside, same era.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 2818–2826, 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
Later among the works it cites.
E. R. S. D. Rezende, G. C. S. Ruppert, and T. Carvalho, “Detecting computer generated images with deep convolutional neural networks,” in 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) , Oct 2017, pp. 71–78
2017
Later among the works it cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 1800–1807, 2017
2017
Later among the works it cites.
Machine Learning as a Service, “Machine learning as a service — G2Crowd,” 2018, [Online; accessed 22-June-2018]. [Online]. Available: https://blog.g2crowd.com/blog/trends/artificial-intelligence/2018-ai/machine-learning-service-mlaas/
2018
Closest in time.
Y. Nagai, Y. Uchida, S. Sakazawa, and S. Satoh, “Digital watermarking for deep neural networks,” International Journal of Multimedia Information Retrieval , vol. 7, pp. 3–16, 2018
2018
Closest in time.
Backdoors, “Backdoors — techopedia,” 2018, [Online; accessed 22-June-2018]. [Online]. Available: https://www.techopedia.com/definition/3743/backdoor
2018
Closest in time.
Y. Adi, C. Baum, M. Cisse, B. Pinkas, and J. Keshet, “Turning your weakness into a strength: Watermarking deep neural networks by backdooring,” in 27th USENIX Security Symposium (USENIX Security 18) . Baltimore, MD: USENIX Association, 2018. [Online]. Available: https://www.usenix.org/conference/usenixsecurity18/presentation/adi
2018
Closest in time.