Fetching the paper…
Reading the bibliography…
Recently, Deep Learning (DL), especially Convolutional Neural Network (CNN), develops rapidly and is applied to many tasks, such as image classification, face recognition, image segmentation, and human detection.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , 2010, pp. 249–256
2010
Earlier work this paper cites.
T. H. Lior Wolf and I. Maoz, “Face recognition in unconstrained videos with matched background similarity,” in CVPR , 2011
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NIPS , 2012
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. Ba and R. Caruana, “Do deep nets really need to be deep?” in NIPS , 2014, pp. 2654–2662
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
S. Bhunia, M. S. Hsiao, M. Banga, and S. Narasimhan, “Hardware trojan attacks: threat analysis and countermeasures,” Proceedings of the IEEE , vol. 102, no. 8, pp. 1229–1247, 2014
2014
Earlier work this paper cites.
J. Zhang, F. Yuan, and Q. Xu, “Detrust: Defeating hardware trust verification with stealthy implicitly-triggered hardware trojans,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2014, pp. 153–166
2014
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” IJCV , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Cited alongside, same era.
C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, “Optimizing fpga-based accelerator design for deep convolutional neural networks,” in FPGA , 2015, pp. 161–170
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Cited alongside, same era.
A. Antoniou and P. Angelov, “A general purpose intelligent surveillance system for mobile devices using deep learning,” in IJCNN . IEEE, 2016
2016
Cited alongside, same era.
J. Qiu, J. Wang, S. Yao, K. Guo, B. Li, E. Zhou, J. Yu, T. Tang, N. Xu, S. Song et al. , “Going deeper with embedded fpga platform for convolutional neural network,” in FPGA . ACM, 2016, pp. 26–35
2016
Later among the works it cites.
H. Li, X. Fan, L. Jiao, W. Cao, X. Zhou, and L. Wang, “A high performance fpga-based accelerator for large-scale convolutional neural networks,” in FPL , 2016, pp. 1–9
2016
Later among the works it cites.
M. Motamedi, P. Gysel, V. Akella, and S. Ghiasi, “Design space exploration of fpga-based deep convolutional neural networks,” in DAC , 2016, pp. 575–580
2016
Later among the works it cites.
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in Security and Privacy (EuroS&P), 2016 IEEE European Symposium on . IEEE, 2016, pp. 372–387
2016
Cited alongside, same era.
S. M. Moosavi Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in CVPR , no. EPFL-CONF-218057, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S. Alfeld, X. Zhu, and P. Barford, “Data poisoning attacks against autoregressive models.” in AAAI , 2016, pp. 1452–1458
2016
Cited alongside, same era.
S. Feng, P. Setoodeh, and S. Haykin, “Smart home: Cognitive interactive people-centric internet of things,” IEEE Communications Magazine , vol. 55, no. 2, pp. 34–39, 2017
2017
Later among the works it cites.
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers et al. , “In-datacenter performance analysis of a tensor processing unit,” in ISCA . ACM, 2017, pp. 1–12
2017
Later among the works it cites.
Y. He, X. Zhang, and J. Sun, “Channel pruning for accelerating very deep neural networks,” in International Conference on Computer Vision (ICCV) , vol. 2, no. 6, 2017
2017
Later among the works it cites.
T.-J. Yang, Y.-H. Chen, and V. Sze, “Designing energy-efficient convolutional neural networks using energy-aware pruning,” arXiv preprint , 2017
2017
Later among the works it cites.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Security and Privacy (SP), 2017 IEEE Symposium on . IEEE, 2017, pp. 39–57
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang, “Trojanning attack on neural networks,” in NDSS . The Internet Society, 2018
2018
Closest in time.