The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process

By Hongyuan Mei, Jason Eisner. 2017

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Neural Ordinary Differential Equations (2018, neutral): “Missing data can be addressed using generative time-series models (Álvarez and Lawrence, 2011; Futoma et al., 2017; Mei and Eisner, 2017; Soleimani et al., 2017a) or data imputation (Che et al., 2018).”
Representation Learning for Dynamic Graphs: A Survey (2019, background): “Some of the well-known TPPs include Hawkes process (Hawkes (1971); Mei and Eisner (2017)), Poisson processes (Kingman (2005)), selfcorrecting processes (Isham and Westcott (1979)), and autoregressive conditional duration processes (Engle and Russell (1998)).”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, methodology): “Equation (12) is a special case of the well-known self-exciting Hawkes process [92, 157].”
Time2Vec: Learning a Vector Representation of Time (2019, background): “Many recent studies aim at obviating the need for hand-crafting features by proposing general-purpose—as opposed to problem speciﬁc—architectures that better handle time [44, 61, 39, 24, 56, 34].”
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020, background): “Finally, a recent trend in sequence modelling employs NNs in modelling discrete event sequences observed in continuous time [48, 133, 164, 165, 182, 194] and [166] for an overview.”
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey (2020, methodology): “A part of the architecture, the Graph Hawkes Process, is an adapted continuous-time LSTM for Hawkes processes [93].”
Neural Jump Stochastic Differential Equations (2019, methodology): “For MIMIC2, we use We compare the event type classiﬁcation accuracy of our model against two other models for learning event sequences that directly simulate the next event based on the history, namely neural point process models based on an RNN [21] or LSTM [20].”
Neural Controlled Differential Equations for Irregular Time Series (2020, methodology): “For example [6, 7, 8, 9] incorporate exponential decay between observations, [10, 11] hybridise a Gaussian process with traditional neural network models, [12] approximate the underlying continuous-time process, and [13, 14] adapt recurrent neural networks by allowing some hidden state to evolve as…”
Process (2020, methodology): “There are have been attempts on RNNs based point process models [1, 2, 5, 7], which use RNNs to memorize the influence of historical events.”
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series (2019, result): “In contrast with our work that focuses on continuous-time real-valued time series, continuous time modelling of time-to-events has been addressed with point processes (Mei & Eisner, 2017) and continuous time Bayesian networks (Nodelman et al., 2002).”
Self-attention with Functional Time Representation Learning (2019, methodology): “Several methods are proposed to couple point process with RNNs to take account of temporal information [23, 22, 14, 6].”
Latent ODEs for Irregularly-Sampled Time Series (2019, methodology): “Mei and Eisner [2017] used a similar approach, but relied on a ﬁxed time discretization to estimate the Poisson intensity.”
DeepMove: Predicting Human Mobility with Attentional Recurrent Networks (2018, methodology): “Because of its powerful representation ability, RNN have been applied to many fields like click prediction [44], recommendation system [13] and mobility prediction [9, 15, 20, 23, 37, 38].”
Transformer Hawkes Process (2020, methodology): “, 2016) and Neural Hawkes Process (NHP) (Mei and Eisner, 2017).”