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We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures.
Fourier analysis on groups
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Spectra of some self-exciting and mutually exciting point processes
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Gomez Rodriguez, M., Leskovec, J., and Krause, A. (2010) · 2010
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stpp: An r package for plotting, simulating and analyzing spatio-temporal point patterns
Gabriel, E., Rowlingson, B., and Diggle, P. (2013) · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J. (2013) · 2013
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Recurrent marked temporal point processes: Embedding event history to vector
Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., and Song, L. (2016) · 2016
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Mimic-iii, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-w. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., and Mark, R. G. (2016) · 2016
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The neural hawkes process: A neurally self-modulating multivariate point process
Mei, H. and Eisner, J. M. (2017) · 2017
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Deep reinforcement learning of marked temporal point processes
Upadhyay, U., De, A., and Gomez Rodriguez, M. (2018) · 2018
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Traffic analysis and data application (tada)
GDOT (2019) · 2019
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Attentive neural processes
Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, S. M. A., Rosenbaum, D., Vinyals, O., and Teh, Y. W. (2019) · 2019
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Fully neural network based model for general temporal point processes
Omi, T., ueda, n., and Aihara, K. (2019) · 2019
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Multivariate spatiotemporal hawkes processes and network reconstruction
Yuan, B., Li, H., Bertozzi, A. L., Brantingham, P. J., and Porter, M. A. (2019) · 2019
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Self-attentive hawkes processes
Zhang, Q., Lipani, A., Kirnap, O., and Yilmaz, E. (2019) · 2019
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A review of self-exciting spatio-temporal point processes and their applications
Reinhart, A. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. (2017) · 2017
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Neural processes
Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F., Rezende, D. J., Eslami, S. M. A., and Teh, Y. W. (2018) · 2018
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Learning temporal point processes via reinforcement learning
Li, S., Xiao, S., Zhu, S., Du, N., Xie, Y., and Song, L. (2018) · 2018
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Wasserstein learning of deep generative point process models
Xiao, S., Farajtabar, M., Ye, X., Yan, J., Song, L., and Zha, H. (2017a)
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Modeling the intensity function of point process via recurrent neural networks
Xiao, S., Yan, J., Yang, X., Zha, H., and Chu, S. M. (2017b)
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Interpretable generative neural spatio-temporal point processes
Zhu, S., Li, S., and Xie, Y. (2019) · 2019
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Spatial-temporal-textual point processes with applications in crime linkage detection
Zhu, S. and Xie, Y. (2019) · 2019
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Adversarial anomaly detection for marked spatio-temporal streaming data
Zhu, S., Yuchi, H. S., and Xie, Y. (2020) · 2020
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