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Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals.
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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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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On the computational power of neural nets
Hava T Siegelmann and Eduardo D Sontag · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Mixture density networks
Christopher M Bishop · 1994
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Better generative models for sequential data problems: Bidirectional recurrent mixture density networks
Mike Schuster · 2000
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Finite mixture models
Geoffrey McLachlan and David Peel · 2004
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Finite mixture and Markov switching models
Sylvia Frühwirth-Schnatter · 2006
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Recurrent neural networks are universal approximators
Anton Maximilian Schäfer and Hans Georg Zimmermann · 2006
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Asymptotic theory of statistics and probability
Anirban DasGupta · 2008
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Music recommendation
Oscar Celma · 2010
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Monotone and partially monotone neural networks
Hennie Daniels and Marina Velikova · 2010
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Frailty models in survival analysis
Andreas Wienke · 2010
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Temporal point processes: the conditional intensity function
Jakob Gulddahl Rasmussen · 2011
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Mixture modeling for marked poisson processes
Matthew A Taddy, Athanasios Kottas, et al · 2012
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Gaussian mixture models for time series modelling, forecasting, and interpolation
Emil Eirola and Amaury Lendasse · 2013
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 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
Neural autoregressive flows
David Krueger, Chin-Wei Huang, Alexandre Lacoste, and Aaron Courville · 2018
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Learning temporal point processes via reinforcement learning
Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, and Le Song · 2018
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Deep reinforcement learning of marked temporal point processes
Utkarsh Upadhyay, Abir De, and Manuel Gomez Rodriguez · 2018
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Learning conditional generative models for temporal point processes
Shuai Xiao, hongteng Xu, Junchi Yan, Mehrdad Farajtabar, Xiaokang Yang, Le Song, and Hongyuan Zha · 2018
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Improving maximum likelihood estimation of temporal point process via discriminative and adversarial learning
Junchi Yan, Xin Liu, Liangliang Shi, Changsheng Li, and Hongyuan Zha · 2018
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Uncertainty on asynchronous time event prediction
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Categorical reparameterization with Gumbel-softmax
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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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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Distilling information reliability and source trustworthiness from digital traces
Behzad Tabibian, Isabel Valera, Mehrdad Farajtabar, Le Song, Bernhard Schölkopf, and Manuel Gomez-Rodriguez · 2017
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Marin Biloš, Bertrand Charpentier, and Stephan Günnemann · 2019
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
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Recurrent poisson process unit for speech recognition
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Sum-of-squares polynomial flow
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Predicting dynamic embedding trajectory in temporal interaction networks
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Monte carlo gradient estimation in machine learning
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Deep mixture point processes: Spatio-temporal event prediction with rich contextual information
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Fully neural network based model for general temporal point processes
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Fastpoint: Scalable deep point processes
Ali Caner Türkmen, Yuyang Wang, and Alexander J Smola · 2019
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Temporal point processes. Lecture notes for Human-Centered ML, January 2019
Utkarsh Upadhyay and Manuel Gomez Rodriguez · 2019
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Latent normalizing flows for discrete sequences
Zachary M Ziegler and Alexander M Rush · 2019
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