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A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time.
Spectra of some self-exciting and mutually exciting point processes
Alan G Hawkes · 1971
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A self-correcting point process
Valerie Isham and Mark Westcott · 1979
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An introduction to the theory of point processes. Vol. I
D. J. Daley and D. Vere-Jones · 2003
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Stochastic Differential Equations: an Introduction with Applications
Bernt Oksendal · 2013
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Siamese neural networks for one-shot image recognition
Gregory Koch et al · 2015
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Recurrent marked temporal point processes: Embedding event history to vector
Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song · 2016
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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The neural hawkes process: A neurally self-modulating multivariate point process
Hongyuan Mei and Jason M Eisner · 2017
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Meta networks
Tsendsuren Munkhdalai and Hong Yu · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
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Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
Convolutional conditional neural processes
Jonathan Gordon, Wessel P Bruinsma, Andrew YK Foong, James Requeima, Yann Dubois, and Richard E Turner · 2020
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Intensity-free learning of temporal point processes
Oleksandr Shchur, Marin Biloš, and Stephan Günnemann · 2020
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Self-attentive hawkes process
Qiang Zhang, Aldo Lipani, Omer Kirnap, and Emine Yilmaz · 2020
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Transformer hawkes process
Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, and Hongyuan Zha · 2020
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Neural flows: Efficient alternative to neural odes
Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, and Stephan Günnemann · 2021
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Identifying coordinated accounts on social media through hidden influence and group behaviours
Karishma Sharma, Yizhou Zhang, Emilio Ferrara, and Yan Liu · 2021
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Attentive neural processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2019
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Predicting dynamic embedding trajectory in temporal interaction networks
Srijan Kumar, Xikun Zhang, and Jure Leskovec · 2019
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Fully neural network based model for general temporal point processes
Takahiro Omi, Kazuyuki Aihara, et al · 2019
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Neural process family
Yann Dubois, Jonathan Gordon, and Andrew YK Foong · 2020
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Meta-learning stationary stochastic process prediction with convolutional neural processes
Andrew YK Foong, Wessel P Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, and Richard E Turner · 2020
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Conditional neural processes
Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, and SM Ali Eslami
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Neural temporal point processes: A review
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, and Stephan Günnemann · 2021
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Exploring generative neural temporal point process
Haitao Lin, Lirong Wu, Guojiang Zhao, Pai Liu, and Stan Z Li · 2022
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Transformer neural processes: Uncertainty-aware meta learning via sequence modeling
Tung Nguyen and Aditya Grover · 2022
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Transformer embeddings of irregularly spaced events and their participants
Chenghao Yang, Hongyuan Mei, and Jason Eisner · 2022
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Learning mixture of neural temporal point processes for multi-dimensional event sequence clustering
Yunhao Zhang, Junchi Yan, Xiaolu Zhang, Jun Zhou, and Xiaokang Yang · 2022
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