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Temporal point process serves as an essential tool for modeling time-to-event data in continuous time space.
Fully neural network based model for general temporal point processes
Omi, T.; Ueda, N.; and Aihara, K. 2019 · 1905
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Continual learning with hypernetworks
Von Oswald, J.; Henning, C.; Sacramento, J.; and Grewe, B. F. 2019 · 1906
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Spectra of some self-exciting and mutually exciting point processes
Hawkes, A. G. 1971 · 1971
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Point process methodology for on-line spatio-temporal disease surveillance
Diggle, P.; Rowlingson, B.; and Su, T.-l. 2005 · 2005
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An Introduction to the Theory of Point Processes, Volume II: General Theory and Structure, by Daryl J. Daley, David Vere-Jones
Valkeila, E. 2008 · 2008
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Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H.; Nickisch, H.; and Harmeling, S. 2009 · 2009
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Meme-tracking and the dynamics of the news cycle
Leskovec, J.; Backstrom, L.; and Kleinberg, J. 2009 · 2009
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Zero-shot learning with semantic output codes
Palatucci, M.; Pomerleau, D.; Hinton, G. E.; and Mitchell, T. M. 2009 · 2009
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Seismicity models based on Coulomb stress calculations
Hainzl, S.; Steacy, D.; and Marsan, S. 2010 · 2010
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Multivariate Hawkes processes: an application to financial data
Embrechts, P.; Liniger, T.; and Lin, L. 2011 · 2011
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Self-exciting point process modeling of crime
Mohler, G. O.; Short, M. B.; Brantingham, P. J.; Schoenberg, F. P.; and Tita, G. E. 2011 · 2011
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Devise: A deep visual-semantic embedding model
Frome, A.; Corrado, G. S.; Shlens, J.; Bengio, S.; Dean, J.; Ranzato, M.; and Mikolov, T. 2013 · 2013
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G. S.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Goodfellow, I.; Harp, A.; Irving, G.; Isard, M.; Jia, Y.; Jozefowicz, R.; Kaiser, L.; Kudlur, M.; Levenberg, J.; Mané, D.; Monga, R.; Moore, S.; Murray, D.; Olah, C.; Schuster, M.; Shlens, J.; Steiner, B.; Sutskever, I.; Talwar, K.; Tucker, P.; Vanhoucke, V.; Vasudevan, V.; Viégas, F.; Vinyals, O.; Warden, P.; Wattenberg, M.; Wicke, M.; Yu, Y.; and Zheng, X. 2015 · 2015
Cited alongside, same era.
Hawkes processes in finance
Bacry, E.; Mastromatteo, I.; and Muzy, J.-F. 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
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.
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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Multi-modal cycle-consistent generalized zero-shot learning
Felix, R.; Reid, I.; Carneiro, G.; et al. 2018 · 2018
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Zero-shot recognition via semantic embeddings and knowledge graphs
Wang, X.; Ye, Y.; and Gupta, A. 2018 · 2018
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Learning hawkes processes from a handful of events
Salehi, F.; Trouleau, W.; Grossglauser, M.; and Thiran, P. 2019 · 2019
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Meta learning with relational information for short sequences
Xie, Y.; Jiang, H.; Liu, F.; Zhao, T.; and Zha, H. 2019 · 2019
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Neural likelihoods via cumulative distribution functions
Chilinski, P.; and Silva, R. 2020 · 2020
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Zoph, B.; and Le, Q. V. 2016 · 2016
Cited alongside, same era.
“Hypernetworks,” in 5th International Conference on Learning Representations, ICLR 2017
Ha, D.; Dai, A. M.; and Le, Q. V. 2017 · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J.; Pascanu, R.; Rabinowitz, N.; Veness, J.; Desjardins, G.; Rusu, A. A.; Milan, K.; Quan, J.; Ramalho, T.; Grabska-Barwinska, A.; et al. 2017 · 2017
Cited alongside, same era.
Learning without forgetting
Li, Z.; and Hoiem, D. 2017 · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
Lopez-Paz, D.; and Ranzato, M. 2017 · 2017
Cited alongside, same era.
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Mei, H.; and Eisner, J. M. 2017 · 2017
Cited alongside, same era.
A tutorial on hawkes processes for events in social media
Rizoiu, M.-A.; Lee, Y.; Mishra, S.; and Xie, L. 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.
Later among the works it cites.
Self-attentive Hawkes process
Zhang, Q.; Lipani, A.; Kirnap, O.; and Yilmaz, E. 2020 · 2020
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Meta-learning via hypernetworks
Zhao, D.; von Oswald, J.; Kobayashi, S.; Sacramento, J.; and Grewe, B. F. 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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Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates
Chiang, W.-H.; Liu, X.; and Mohler, G. 2021 · 2021
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Hyperprompt: Prompt-based task-conditioning of transformers
He, Y.; Zheng, S.; Tay, Y.; Gupta, J.; Du, Y.; Aribandi, V.; Zhao, Z.; Li, Y.; Chen, Z.; Metzler, D.; et al. 2022 · 2022
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