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The neural Hawkes process (Mei & Eisner, 2017) is a generative model of irregularly spaced sequences of discrete events.
Spectra of some self-exciting and mutually exciting point processes
Hawkes, A. G · 1971
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Simulation of nonhomogeneous Poisson processes by thinning
Lewis, P. A. and Shedler, G. S · 1979
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What you always wanted to know about Datalog (and never dared to ask)
Ceri, S., Gottlob, G., and Tanca, L · 1989
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Learning to sportscast: A test of grounded language acquisition
Chen, D. L. and Mooney, R. J · 2008
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Multivariate Hawkes processes
Liniger, T. J · 2009
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Recurrent neural network-based language model
Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., and Khudanpur, S · 2010
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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
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SNAP Datasets: Stanford large network dataset collection , 2014
Leskovec, J. and Krevl, A · 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
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The Bayesian echo chamber: Modeling social influence via linguistic accommodation
Guo, F., Blundell, C., Wallach, H., and Heller, K · 2015
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HawkesTopic: A joint model for network inference and topic modeling from text-based cascades
He, X., Rekatsinas, T., Foulds, J., Getoor, L., and Liu, Y · 2015
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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pyDatalog , 2016
Carbonell, P., jcdouet · 2016
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
Cited alongside, same era.
Learning network of multivariate Hawkes processes: A time series approach
Etesami, J., Kiyavash, N., Zhang, K., and Singhal, K · 2016
Cited alongside, same era.
Hawkes processes for continuous time sequence classification: An application to rumour stance classification in Twitter
Lukasik, M., Srijith, P. K., Vu, D., Bontcheva, K., Zubiaga, A., and Cohn, T · 2016
Cited alongside, same era.
Learning Granger causality for Hawkes processes
Xu, H., Farajtabar, M., and Zha, H · 2016
Cited alongside, same era.
Fully neural network based model for general temporal point processes
Omi, T., Ueda, N., and Aihara, K · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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User-dependent neural sequence models for continuous-time event data
Boyd, A., Bamler, R., Mandt, S., and Smyth, P · 2020
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Neural temporal point processes [for] modelling electronic health records
Enguehard, J., Busbridge, D., Bozson, A., Woodcock, C., and Hammerla, N · 2020
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Mei, H. and Eisner, J · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Constituency parsing with a self-attentive encoder
Kitaev, N. and Klein, D · 2018
Cited alongside, same era.
Deep contextualized word representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L · 2018
Cited alongside, same era.
Online continuous-time tensor factorization based on pairwise interactive point processes
Xu, H., Luo, D., and Carin, L · 2018
Cited alongside, same era.
Theoretical limitations of self-attention in neural sequence models
Hahn, M · 2020
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DeBERTa: Decoding-enhanced bert with disentangled attention
He, P., Liu, X., Gao, J., and Chen, W · 2020
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Intensity-free learning of temporal point processes
Shchur, O., Biloš, M., and Günnemann, S · 2020
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Zuo, S., Jiang, H., Li, Z., Zhao, T., and Zha, H · 2020
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Transformer protein language models are unsupervised structure learners
Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., , and Rives, A · 2021
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Identifying coordinated accounts on social media through hidden influence and group behaviours
Sharma, K., Zhang, Y., Ferrara, E., and Liu, Y · 2021
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Learning neural point processes with latent graphs
Zhang, Q., Lipani, A., and Yilmaz, E · 2021
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Deep Fourier kernel for self-attentive point processes
Zhu, S., Zhang, M., Ding, R., and Xie, Y · 2021
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