Fetching the paper…
Reading the bibliography…
In the recent years, the rapid spread of mobile device has create the vast amount of mobile data.
K. Fukushima, ”Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biological cybernetics, vol.36 ,no. 4, pp. 193-202, 1980
1980
Earlier work this paper cites.
Rummelhart D E, Hinton G E, and Williams R J, “Learning representations by back-propagating errors,” Nature, vol. 323, pp. 533-536, 1986
1986
Earlier work this paper cites.
Le Cun Y, Jackel L D, Boser B, et al., “Handwritten digit recognition: Applications of neural network chips and automatic learning,” Communications Magazine, IEEE, vol.27, no. 11, pp. 41-46, 1989
1989
Earlier work this paper cites.
Bengio Y, Simard P, and Frasconi P, “Learning long-term dependencies with gradient descent is difficult,” Neural Networks, IEEE Transactions on, vol. 5, no. 2, pp. 157-166, 1994
1994
Earlier work this paper cites.
Hochreiter S, and Schmidhuber J, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997
1997
Earlier work this paper cites.
Hochreiter S, Bengio Y, Frasconi P, et al., “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies,” 2001
2001
Earlier work this paper cites.
Krumm J, Horvitz E, “Predestination: Inferring destinations from partial trajectories,” in Ubiquitous Computing (UbiComp). Springer Berlin Heidelberg, pp. 243-260, 2006
2006
Earlier work this paper cites.
Hinton G E, and Salakhutdinov R R, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504-507, 2006
2006
Earlier work this paper cites.
Hinton G E, Osindero S, and Teh Y W, “A fast learning algorithm for deep belief nets,” Neural computation, vol. 18, no. 7, pp. 1527-1554, 2006
2006
Earlier work this paper cites.
Bengio Y, Lamblin P, Popovici D, et al., “Greedy layer-wise training of deep networks,” Advances in neural information processing systems, 2007
2007
Earlier work this paper cites.
Vincent P, Larochelle H, Bengio Y, et al., “Extracting and composing robust features with denoising autoencoders,” in Proceedings of the 25th international conference on Machine learning (ICML), ACM, pp. 1096-1103, 2008
2008
Earlier work this paper cites.
Song C, Qu Z, Blumm N, et al., “Limits of predictability in human mobility,” Science, vol. 327, no. 5968, pp. 1018-1021, 2010
2010
Earlier work this paper cites.
Ahas R, Aasa A, Silm S, et al., “Daily rhythms of suburban commuters movements in the Tallinn metropolitan area: case study with mobile positioning data,” Transportation Research Part C: Emerging Technologies, vol. 18, no. 1, pp. 45-54, 2010
2010
Earlier work this paper cites.
Song C, Koren T, Wang P, et al., “Modelling the scaling properties of human mobility,” Nature Physics, vol. 6, no. 10, pp. 818-823, 2010
2010
Earlier work this paper cites.
Sevtsuk A, and Ratti C, “Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks,” Journal of Urban Technology, vol. 17, no. 1, pp. 41-60, 2010
2010
Earlier work this paper cites.
Scellato S, Musolesi M, Mascolo C, et al., “Nextplace: a spatio-temporal prediction framework for pervasive systems,” in Pervasive Computing. Springer Berlin Heidelberg, pp. 152-169, 2011
2011
Earlier work this paper cites.
Belik, Vitaly, Theo Geisel, and Dirk Brockmann, ”Natural human mobility patterns and spatial spread of infectious diseases,” Physical Review X, 1(1): 011001, 2011
2011
Cited alongside, same era.
Cho E, Myers S A, and Leskovec J, “Friendship and mobility: user movement in location-based social networks,” in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD). ACM, pp. 1082-1090, 2011
2011
Cited alongside, same era.
Laurila J K, Gatica-Perez D, Aad I, et al., “The mobile data challenge: Big data for mobile computing research,” in Pervasive Computing, no. EPFL-CONF-192489, 2012
2012
Cited alongside, same era.
Krizhevsky A, Sutskever I, and Hinton G E, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, pp. 1097-1105, 2012
2012
Cited alongside, same era.
Jia Y, Shelhamer E, Donahue J, et al, “Caffe: Convolutional architecture for fast feature embedding,” In Proceedings of the ACM International Conference on Multimedia (MM), pp. 675-678, 2014
2014
Later among the works it cites.
2014
Later among the works it cites.
LeCun Y, Bengio Y, and Hinton G, “Deep learning,” Nature, vlo. 521, no. 7553, pp. 436-444, 2015
2015
Later among the works it cites.
Schmidhuber J, “Deep learning in neural networks: An overview,” Neural Networks, vol.61, pp. 85-117, 2015
2015
Later among the works it cites.
Cisco. “Cisco Visual Networking Index: Forecast and Methodology,” 2016-2021, 2017
2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhou G, Sohn K, and Lee H, “Online incremental feature learning with denoising autoencoders,” in International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 1453-1461, 2012
2012
Cited alongside, same era.
Baumann P, Kleiminger W, and Santini S, “The influence of temporal and spatial features on the performance of next-place prediction algorithms,” in Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. ACM, pp. 449-458, 2013
2013
Cited alongside, same era.
2013
Cited alongside, same era.
Gao, Song, et al., ”Discovering spatial interaction communities from mobile phone data,” Transactions in GIS, vol. 17, no. 3, pp. 463-481, 2013
2013
Cited alongside, same era.
Pascanu R, Mikolov T, and Bengio Y, “On the difficulty of training recurrent neural networks,” in Proceedings of The 30th International Conference on Machine Learning (ICML), pp. 1310-1318, 2013
2013
Cited alongside, same era.
Mobile, Cisco VNI. “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013-2018,” San Jose, CA 1, 2014
2014
Cited alongside, same era.
Zeng M, Nguyen L T, Yu B, et al., “Convolutional Neural Networks for human activity recognition using mobile sensors,” in Mobile Computing, Applications and Services (MobiCASE), IEEE, pp. 197-205, 2014
2014
Cited alongside, same era.
Xiao T, Zhang J, Yang K, et al., “Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification,” in Proceedings of the ACM International Conference on Multimedia (MM), pp. 177-186, 2014
2014
Cited alongside, same era.
Jiang, C., Zhang, H., Ren, Y., et al., “Machine learning paradigms for next-generation wireless networks,” IEEE Wireless Communications, vol.24, no. 2, pp. 98-105, 2017
2017
Closest in time.
Lin, Z., Yin, M., Feygin, S., et al., “Deep generative models of urban mobility,” IEEE Transactions on Intelligent Transportation Systems, 2017
2017
Closest in time.
Zhang, J., Zheng, Y. Qi, D., “Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction,” In Association for the Advancement of Artificial Intelligence (AAAI), pp. 1655-1661, 2017
2017
Closest in time.
Yang, C., Sun, M., Zhao, W.X., et al., “A neural network approach to jointly modeling social networks and mobile trajectories,” ACM Transactions on Information Systems (TOIS), vol. 35, no. 4, p. 36, 2017
2017
Closest in time.
Ouyang, X., Kawaai, S., Goh, E.G.H., et al., “Audio-visual emotion recognition using deep transfer learning and multiple temporal models,” In Proceedings of ACM International Conference on Multimodal Interaction, pp. 577-582, 2017
2017
Closest in time.
Xu, Y., Ouyang, X., Cheng, Y., et al., “Dual-mode vehicle motion pattern learning for high performance road traffic anomaly detection,” In IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) on the AI City Challenge, 2017
2017
Closest in time.
Liu, W., Wang, Z., Liu, X., et al., “A survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, pp. 11-26, 2017
2017
Closest in time.
Kasnesis, P., Patrikakis, C. and Venieris, I., “Changing the game of mobile data analysis with deep learning,” IT Professional, 2017
2017
Closest in time.
Kato, N., Fadlullah, Z.M., Mao, B., et al., “The deep learning vision for heterogeneous network traffic control: Proposal, challenges, and future perspective,” IEEE wireless communications, vol. 24, no. 3, pp. 146-153, 2017
2017
Closest in time.
Ouyang, X., Gu, K. and Zhou, P., “Spatial Pyramid Pooling Mechanism in 3D Convolutional Network for Sentence-Level Classification,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 11, pp.2167-2179, 2018
2018
Closest in time.