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A temporal point process is a stochastic process that predicts which type of events is likely to happen and when the event will occur given a history of a sequence of events.
Spectra of some self-exciting and mutually exciting point processes
Hawkes, A. G. 1971 · 1971
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Point processes , volume 12
Cox, D. R.; and Isham, V. 1980 · 1980
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Deep transformer models for time series forecasting: The influenza prevalence case
Wu, N.; Green, B.; Ben, X.; and O’Banion, S. 2020 · 2001
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Multivariate hawkes processes
Liniger, T. J. 2009 · 2009
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Open-access MIMIC-II database for intensive care research
Lee, J.; Scott, D. J.; Villarroel, M.; Clifford, G. D.; Saeed, M.; and Mark, R. G. 2011 · 2011
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Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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Hawkes model for price and trades high-frequency dynamics
Bacry, E.; and Muzy, J.-F. 2014 · 2014
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Semi-supervised learning with deep generative models
Kingma, D. P.; Mohamed, S.; Rezende, D. J.; and Welling, M. 2014 · 2014
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SNAP Datasets: Stanford large network dataset collection
Leskovec, J.; and Krevl, A. 2014 · 2014
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Constructing disease network and temporal progression model via context-sensitive hawkes process
Choi, E.; Du, N.; Chen, R.; Song, L.; and Sun, J. 2015 · 2015
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A recurrent latent variable model for sequential data
Chung, J.; Kastner, K.; Dinh, L.; Goel, K.; Courville, A. C.; and Bengio, Y. 2015 · 2015
Cited alongside, same era.
Dirichlet-hawkes processes with applications to clustering continuous-time document streams
Du, N.; Farajtabar, M.; Ahmed, A.; Smola, A. J.; and Song, L. 2015 · 2015
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
Cited alongside, same era.
Variational dropout and the local reparameterization trick
Kingma, D. P.; Salimans, T.; and Welling, M. 2015 · 2015
Cited alongside, same era.
Seismic: A self-exciting point process model for predicting tweet popularity
Zhao, Q.; Erdogdu, M. A.; He, H. Y.; Rajaraman, A.; and Leskovec, J. 2015 · 2015
Cited alongside, same era.
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
Modeling the intensity function of point process via recurrent neural networks
Xiao, S.; Yan, J.; Yang, X.; Zha, H.; and Chu, S. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 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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Recurrent poisson process unit for speech recognition
Huang, H.; Wang, H.; and Mak, B. 2019 · 2019
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
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Auxiliary deep generative models
Maaløe, L.; Sønderby, C. K.; Sønderby, S. K.; and Winther, O. 2016 · 2016
Cited alongside, same era.
The neural hawkes process: A neurally self-modulating multivariate point process
Mei, H.; and Eisner, J. 2016 · 2016
Cited alongside, same era.
tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models
Bacry, E.; Bompaire, M.; Deegan, P.; Gaïffas, S.; and Poulsen, S. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes
Zhou, K.; Zha, H.; and Song, L. 2013a
Cited in the paper.
Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes
Zhou, K.; Zha, H.; and Song, L. 2013b
Cited in the paper.
Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels
Willetts, M.; Roberts, S.; and Holmes, C. 2020 · 2020
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Self-attentive hawkes process
Zhang, Q.; Lipani, A.; Kirnap, O.; and Yilmaz, E. 2020 · 2020
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Transformer hawkes process
Zuo, S.; Jiang, H.; Li, Z.; Zhao, T.; and Zha, H. 2020 · 2020
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Semisupervised Training of Deep Generative Models for High-Dimensional Anomaly Detection
Xie, Q.; Zhang, P.; Yu, B.; and Choi, J. 2021 · 2021
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