Fog Computing. Группа авторов

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      7 7 Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS 2012), 1097–1105. Curran Associates: New York.

      8 8 Taigman, Y., Yang, M., Ranzato, M.'.A., and Wolf, L. (2014). Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1701–1708. IEEE.

      9 9 Biyi Fang, Jillian Co, and Mi Zhang, DeepASL: enabling ubiquitous and non-intrusive word and sentence-level sign language translation, in Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems (SenSys), Delft, The Netherlands, 2017.

      10 10 Zhou, B., Lapedriza, A., Xiao, J. et al. (2014). Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems (NIPS 2014), 487–495. Curran Associates: New York.

      11 11 He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778. IEEE.

      12 12 Karen Simonyan and Andrew Zisserman, Very deep convolutional networks for large-scale image recognition, in International Conference on Learning Representations (ICLR), 2015.

      13 13 Song Han, Huizi Mao, and William J Dally, Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, in International Conference on Learning Representations (ICLR), 2016.

      14 14 Hao Li, Asim Kadav, Igor Durdanovic et al., Pruning filters for efficient ConvNets, in International Conference on Learning Representations (ICLR), 2016.

      15 15 Andrew G. Howard, Menglong Zhu, Bo Chen et al., Mobilenets: Efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861, 2017.

      16 16 Geoffrey Hinton, Oriol Vinyals, and Jeff Dean, Distilling the knowledge in a neural network. NIPS Deep Learning Workshop, 2014.

      17 17 Zeng, X., Cao, K., and Mi, Z. (2017). MobileDeepPill: a small-footprint Mobile deep learning system for recognizing unconstrained pill images. In: Proceedings of the 15th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys), 56–67. Niagara Falls, NY, USA. ACM: New York.

      18 18 Florian Schroff, Dmitry Kalenichenko, and James Philbin, Facenet: a unified embedding for face recognition and clustering, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

      19 19 Diederik P. Kingma and Max Welling, “Auto-encoding variational bayes.” International Conference on Learning Representations (ICLR), 2014.

      20 20 LiKamWa, R., Hou, Y., Gao, J. et al. (2016). RedEye: analog ConvNet image sensor architecture for continuous mobile vision. In: 2016 ACM/IEEE 43rd International Symposium on Computer Architecture (ISCA), 255–266. IEEE Press.

      21 21 Radu, V., Lane, N.D., Bhattacharya, S. et al. (2016). Towards multimodal deep learning for activity recognition on mobile devices. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, 185–188. ACM.

      22 22 Bhattacharya, S. and Lane, N.D. (2016). From smart to deep: robust activity recognition on smartwatches using deep learning. In: Pervasive Computing and Communication Workshops (PerCom Workshops), 2016 IEEE International Conference, pp. 1–6. IEEE.

      23 23 Chang, S., Han, W., Tang, J. et al. (2015). Heterogeneous network embedding via deep architectures. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 119–128. ACM.

      24 24 Shuochao Yao, Shaohan Hu, Yiran Zhao et al., Deepsense: A unified deep learning framework for time-series mobile sensing data processing. The World Wide Web Conference (WWW), 2017.

      25 25 Fang, B., Zeng, X., and Mi, Z. (2018). NestDNN: Resource-aware multi-tenant on-device deep learning for continuous mobile vision. In: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking (MobiCom), 115–127. New Delhi: India. ACM: New York.

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      27 27 Bolei Zhou, Aditya Khosla, Agata Lapedriza et al., Object detectors emerge in deep scene CNNs. International Conference on Learning Representations (ICLR), 2015.