Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches | Paper Citation Record

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Reference 1

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This paper cites Scalable Bayesian inference for excitatory point process networks,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Scalable Bayesian inference for excitatory point process networks,

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This paper cites Hawkes model for price and trades high- frequency dynamics,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Hawkes model for price and trades high- frequency dynamics,

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This paper cites Interval-censored Transformer Hawkes: Detecting information oper- ations using the reaction of social systems,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Interval-censored Transformer Hawkes: Detecting information oper- ations using the reaction of social systems,

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Unresolved cited work

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This paper cites Spectra of some self-exciting and mutually exciting point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Spectra of some self-exciting and mutually exciting point processes,

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This paper cites A self-correcting point process,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches A self-correcting point process,

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This paper cites Recent advance in temporal point process: from machine learning perspective,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Recent advance in temporal point process: from machine learning perspective,

Reference 8

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This paper cites Neural temporal point processes: A review,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Neural temporal point processes: A review,

Reference 9

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This paper cites Hawkes processes in finance,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Hawkes processes in finance,

Reference 10

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This paper cites Hawkes processes and their applications to finance: a review,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Hawkes processes and their applications to finance: a review,

Reference 11

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This paper cites Bayesian analysis of a Poisson process with a change-point,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Bayesian analysis of a Poisson process with a change-point,

Reference 12

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This paper cites Estimating Product Cannibalisation in Wholesale using Multivariate Hawkes Processes with Inhibition.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Estimating Product Cannibalisation in Wholesale using Multivariate Hawkes Processes with Inhibition

Reference 13

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This paper cites Bayesian inference for hawkes processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Bayesian inference for hawkes processes,

Reference 14

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This paper cites Approximate methods in bayesian point process spatial models,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Approximate methods in bayesian point process spatial models,

Reference 15

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This paper cites Fitting complex ecological point process models with integrated nested laplace approximation,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Fitting complex ecological point process models with integrated nested laplace approximation,

Reference 16

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This paper cites Vari- ational estimation in spatiotemporal systems from continuous and point-process observations,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Vari- ational estimation in spatiotemporal systems from continuous and point-process observations,

Reference 17

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This paper cites Log Gaussian Cox processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Log Gaussian Cox processes,

Reference 18

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This paper cites Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process inten- sities,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process inten- sities,

Reference 19

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This paper cites The permanental process,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches The permanental process,

Reference 20

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This paper cites MCMC for doubly- intractable distributions,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches MCMC for doubly- intractable distributions,

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This paper cites Efficient Bayesian nonparametric modelling of structured point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Efficient Bayesian nonparametric modelling of structured point processes,

Reference 22

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This paper cites Scalable nonparametric Bayesian inference on point processes with Gaussian processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Scalable nonparametric Bayesian inference on point processes with Gaussian processes,

Reference 23

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This paper cites Fast Gaussian pro- cess methods for point process intensity estimation,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Fast Gaussian pro- cess methods for point process intensity estimation,

Reference 24

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This paper cites Fast Bayesian intensity estimation for the permanental process,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Fast Bayesian intensity estimation for the permanental process,

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This paper cites Fast Kronecker inference in Gaussian processes with non-Gaussian likeli- hoods,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Fast Kronecker inference in Gaussian processes with non-Gaussian likeli- hoods,

Reference 26

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This paper cites Sparse spectral Bayesian permanental process with generalized kernel,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Sparse spectral Bayesian permanental process with generalized kernel,

Reference 27

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This paper cites Nonstationary sparse spectral permanental process,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Nonstationary sparse spectral permanental process,

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This paper cites Variational inference for Gaussian process modulated Poisson processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Variational inference for Gaussian process modulated Poisson processes,

Reference 29

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This paper cites A multitask point process predictive model,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches A multitask point process predictive model,

Reference 30

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This paper cites Large-scale Cox process inference using variational Fourier features,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Large-scale Cox process inference using variational Fourier features,

Reference 31

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This paper cites Structured variational inference in continuous cox process models,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Structured variational inference in continuous cox process models,

Reference 32

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Efficient Bayesian inference of sigmoidal Gaussian Cox processes,

Reference 33

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This paper cites Heterogeneous multi-task Gaussian Cox processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Heterogeneous multi-task Gaussian Cox processes,

Reference 34

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This paper cites Dirichlet process mixtures of Beta distributions, with applications to density and intensity estimation,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Dirichlet process mixtures of Beta distributions, with applications to density and intensity estimation,

Reference 35

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Bayesian mixture modeling for spatial Pois- son process intensities, with applications to extreme value analysis,

Reference 36

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This paper cites Efficient non- parametric Bayesian Hawkes processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Efficient non- parametric Bayesian Hawkes processes,

Reference 37

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Variational inference for sparse Gaussian process modulated Hawkes process,

Reference 38

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Ef- ficient EM-variational inference for nonparametric Hawkes process,

Reference 39

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Efficient inference for nonparametric Hawkes processes using auxiliary latent variables,

Reference 40

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Variational bayesian inference for nonlinear hawkes process with gaussian process self- effects,

Reference 41

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Efficient inference for dynamic flexible interactions of neural popu- lations,

Reference 42

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Bayesian estimation of nonlinear hawkes processes,

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Bayesian nonparametric hawkes processes with appli- cations,

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This paper cites Bayesian nonparametric learning for point processes with spatial homogeneity: A spatial analysis of nba shot locations,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Bayesian nonparametric learning for point processes with spatial homogeneity: A spatial analysis of nba shot locations,

Reference 45

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This paper cites Semiparametric estimation for multivariate Hawkes processes using dependent Dirichlet processes: An application to order flow data in financial markets.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Semiparametric estimation for multivariate Hawkes processes using dependent Dirichlet processes: An application to order flow data in financial markets

Reference 46

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Observation 41eb1b0d-96c2-43ba-9ac9-55c3937779f2 · outbound

This paper cites Online nonparametric bayesian hawkes processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Online nonparametric bayesian hawkes processes,

Reference 47

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This paper cites Nonparametric bayesian estimation for multivariate hawkes processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Nonparametric bayesian estimation for multivariate hawkes processes,

Reference 48

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This paper cites Recurrent marked temporal point processes: embedding event history to vector,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Recurrent marked temporal point processes: embedding event history to vector,

Reference 49

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This paper cites The neural Hawkes process: A neurally self-modulating multivariate point process,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches The neural Hawkes process: A neurally self-modulating multivariate point process,

Reference 50

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This paper cites Modeling the intensity function of point process via recurrent neural networks,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Modeling the intensity function of point process via recurrent neural networks,

Reference 51

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This paper cites Recurrent spatio-temporal point process for check-in time prediction,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Recurrent spatio-temporal point process for check-in time prediction,

Reference 52

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This paper cites Fully neural network based model for general temporal point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Fully neural network based model for general temporal point processes,

Reference 53

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This paper cites Unipoint: Uni- versally approximating point processes intensities,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Unipoint: Uni- versally approximating point processes intensities,

Reference 54

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This paper cites Learning tem- poral point processes with intermittent observations,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Learning tem- poral point processes with intermittent observations,

Reference 55

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This paper cites On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes

Reference 56

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This paper cites Mamba hawkes process,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Mamba hawkes process,

Reference 57

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This paper cites Deep linear Hawkes processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Deep linear Hawkes processes,

Reference 58

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Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Transformer Hawkes process,

Reference 59

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This paper cites Self-attentive Hawkes process,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Self-attentive Hawkes process,

Reference 60

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Observation 63a9ced2-b2ac-4f61-828e-777c1b5c78cf · outbound

This paper cites Decomposable Transformer point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Decomposable Transformer point processes,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.477988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f52950a-2bc8-4646-820d-7158bdb7872e · outbound

This paper cites Deep fourier kernel for self-attentive point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Deep fourier kernel for self-attentive point processes,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.482608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 333451aa-ef03-4a6a-a98c-2a86a3e5987d · outbound

This paper cites Neural point process for learning spatiotemporal event dynamics,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Neural point process for learning spatiotemporal event dynamics,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.486429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a5d2c6d-5009-42a2-8e68-c0b10f631e3d · outbound

This paper cites Transformer embeddings of irregularly spaced events and their participants,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Transformer embeddings of irregularly spaced events and their participants,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.489853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db3c9ac1-4819-4355-8c74-09ae1cbab995 · outbound

This paper cites Sparse Transformer Hawkes process for long event sequences,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Sparse Transformer Hawkes process for long event sequences,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.493382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c911703-aac6-4f2d-8c29-7f43c43e9ccc · outbound

This paper cites Interpretable Transformer Hawkes processes: Unveiling complex interactions in social networks,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Interpretable Transformer Hawkes processes: Unveiling complex interactions in social networks,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.496686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 545d01b8-988a-41db-b182-279f9d55db9a · outbound

This paper cites Federated Transformer Hawkes processes for distributed event se- quence prediction,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Federated Transformer Hawkes processes for distributed event se- quence prediction,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.501192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4c4415c-ef32-4b3c-9ed9-829ebd01445a · outbound

This paper cites Neural ordinary differential equations,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Neural ordinary differential equations,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.504730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17d06a93-d994-4f89-9e10-c31f8b6926bb · outbound

This paper cites A stochastic differential equation framework for guiding online user activities in closed loop,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches A stochastic differential equation framework for guiding online user activities in closed loop,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.508293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd9c5418-be2b-4fbf-845b-504d35f91dd9 · outbound

This paper cites Neural jump stochastic differential equations,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Neural jump stochastic differential equations,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.511559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2aef5c1-51ac-4b4d-9af9-ecf6e89fd688 · outbound

This paper cites Neural spatio-temporal point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Neural spatio-temporal point processes,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.514905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a485ff8e-f843-48ba-a1b5-ba15bc7475bd · outbound

This paper cites Latent ordinary differential equations for irregularly-sampled time series,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Latent ordinary differential equations for irregularly-sampled time series,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.517949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1267f4b-d04d-46c4-9031-f7ec0748bad4 · outbound

This paper cites Neural jump-diffusion temporal point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Neural jump-diffusion temporal point processes,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.521142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f1a3f58-28f1-4417-9c43-c88ccc4e7817 · outbound

This paper cites Intensity-free learning of temporal point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Intensity-free learning of temporal point processes,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.524356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 520b6f27-f4a6-4466-8063-81768b07d075 · outbound

This paper cites Learning quantile functions for temporal point processes with recurrent neural splines,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Learning quantile functions for temporal point processes with recurrent neural splines,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.528860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e0af138-cb7c-4ca4-b082-3fe6f85ce3ac · outbound

This paper cites Fast and flexible temporal point processes with triangular maps,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Fast and flexible temporal point processes with triangular maps,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.532457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa2bd45a-59fe-4101-a13f-28522224d47f · outbound

This paper cites Cumulative hazard function based efficient multivariate tem- poral point process learning,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Cumulative hazard function based efficient multivariate tem- poral point process learning,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.536489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f3b27b1-4368-48fc-b555-28e9bfe55be6 · outbound

This paper cites Prompt-augmented temporal point process for streaming event sequence,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Prompt-augmented temporal point process for streaming event sequence,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.539910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28aa91d2-9eef-4f01-a3ef-c94cdeca0b3f · outbound

This paper cites Language models can improve event prediction by few-shot abductive reasoning,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Language models can improve event prediction by few-shot abductive reasoning,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.543817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a4b34a5-1ca4-4d30-82b6-88e29cb8e32a · outbound

This paper cites TPP-LLM: Modeling temporal point processes by efficiently fine-tuning large language models,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches TPP-LLM: Modeling temporal point processes by efficiently fine-tuning large language models,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.547651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eb28db0-ca34-4be5-a592-d313e0cdd4f0 · outbound

This paper cites Language- tpp: Integrating temporal point processes with language models for event analysis,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Language- tpp: Integrating temporal point processes with language models for event analysis,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.551951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34d108e7-88f1-449c-a94f-9e30dc99e392 · outbound

This paper cites Retrieval of temporal event sequences from textual descriptions,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Retrieval of temporal event sequences from textual descriptions,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.921505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b8760d2-d24a-483e-bfb5-41dcb4830499 · outbound

This paper cites Danmakutpp- bench: A multi-modal benchmark for temporal point process modeling and understanding,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Danmakutpp- bench: A multi-modal benchmark for temporal point process modeling and understanding,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:54.560429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d947c8ae-6a2e-4206-a1cf-d7e900b9f48a · outbound

This paper cites Maximum likelihood estimation of Hawkes’ self-exciting point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Maximum likelihood estimation of Hawkes’ self-exciting point processes,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.910343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3fe65b0d-0757-4777-ad6c-6828b70b059f · outbound

This paper cites Maximum likelihood estimation of cascade point-process neural encoding models,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Maximum likelihood estimation of cascade point-process neural encoding models,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.899221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3d93bfbe-4359-4621-9f0b-c069cd816587 · outbound

This paper cites Modeling the intensity function of point process via recurrent neural networks,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Modeling the intensity function of point process via recurrent neural networks,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.888709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5e9d1c55-2997-4843-ae08-d1b55b1bf6ee · outbound

This paper cites Learning conditional generative models for temporal point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Learning conditional generative models for temporal point processes,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.879563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 31057238-f84c-4fb7-949c-0b5f42c71215 · outbound

This paper cites Initiator: noise-contrastive estimation for marked temporal point process,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Initiator: noise-contrastive estimation for marked temporal point process,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.868803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 530aa336-fbb7-40fa-a6ab-98de45c84fd6 · outbound

This paper cites Noise-contrastive estimation for multi- variate point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Noise-contrastive estimation for multi- variate point processes,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.857750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5cd215aa-7e98-4867-b4b1-4028427ba263 · outbound

This paper cites Score-matching estimators for continuous-time point-process regression models,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Score-matching estimators for continuous-time point-process regression models,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.845770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 29ca887a-6eeb-4a5a-9db2-27696342bd51 · outbound

This paper cites Integration-free training for spatio- temporal multimodal covariate deep kernel point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Integration-free training for spatio- temporal multimodal covariate deep kernel point processes,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.835271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8a52825e-5498-4d90-b51e-15fdae559214 · outbound

This paper cites Smurf- thp: score matching-based uncertainty quantification for transformer hawkes process,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Smurf- thp: score matching-based uncertainty quantification for transformer hawkes process,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.824115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ecdeceda-ae93-46c4-83a4-b4b957c37930 · outbound

This paper cites Is score matching suitable for estimating point processes?.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Is score matching suitable for estimating point processes?

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.813285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03d22f6a-7177-4e26-998c-8e2a7fddfedd · outbound

This paper cites Vigdet: Knowledge informed neural temporal point process for coordination detection on social media,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Vigdet: Knowledge informed neural temporal point process for coordination detection on social media,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.803261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cbabada9-42cd-4100-997b-f2e5a3defe06 · outbound

This paper cites Mush: Multi-stimuli hawkes process based sybil attacker detector for user-review social networks,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Mush: Multi-stimuli hawkes process based sybil attacker detector for user-review social networks,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.790028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 32083bed-d56a-41be-9c6f-52cc582deac3 · outbound

This paper cites Framework for detecting fake retweets using deep neural network,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Framework for detecting fake retweets using deep neural network,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.778653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation edf3cd21-8646-4219-be04-aebccafa8215 · outbound

This paper cites Temporal properties of higher-order interactions in social networks,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Temporal properties of higher-order interactions in social networks,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.768234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d4499f3b-baf1-4561-a51b-3d37551f5763 · outbound

This paper cites Public opinion field effect and hawkes process join hands for information popularity prediction,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Public opinion field effect and hawkes process join hands for information popularity prediction,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.757765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d937f172-a1c3-467b-8970-bfcc7c7b5b3c · outbound

This paper cites Identifying hidden patterns of fake covid-19 news: An in-depth sentiment analysis and topic modeling approach,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Identifying hidden patterns of fake covid-19 news: An in-depth sentiment analysis and topic modeling approach,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.747146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6e4dae74-a6e9-4e3c-b15c-35ea7bb7c9ff · outbound

This paper cites Sir- hawkes: on the relationship between epidemic models and Hawkes point processes,.

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches Sir- hawkes: on the relationship between epidemic models and Hawkes point processes,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:55.735426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers