时间序列应用分析 深度解读：深度学习在IoT大数据和流分析中的应用(7)

【2】S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards realtime object detection with region proposal networks,” IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 6, pp. 1137–1149, 2017.

【3】J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779–788.

【4】M. Mohammadi, A. Al-Fuqaha, M. Guizani, and J.-S. Oh, “Semisupervised deep reinforcement learning in support of IoT and smart city services,” IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1–12, 2017.

【5】M. Price, J. Glass, and A. Chandrakasan, “A scalable speech recognizer with deep-neural-network acoustic models and voice-activated power gating,” in Proceedings of the IEEE ISSCC2017, 2017.

【6】Y. He, G. J. Mendis, and J. Wei, “Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism,” IEEE Transactions on Smart Grid, 2017.

【7】 Z. Yuan, Y. Lu, Z. Wang, and Y. Xue, “Droid-sec: deep learning in android malware detection,” in ACM SIGCOMM Computer Communication Review, vol. 44, no. 4. ACM, 2014, pp. 371–372.

【8】R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. ACM, 2015, pp. 1310–1321.

【9】X. Song, H. Kanasugi, and R. Shibasaki, “Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level.” IJCAI, 2016.