Awesome-Temporal-Point-Process

A temporal point process is a model that simultaneously predict item and time in tasks like recommendation system.

Papers

Fast and Flexible Temporal Point Processes with Triangular Maps(NeurIPS 2020) [Code]
Intensity-Free Learning of Temporal Point Processes(ICLR 2020) [Code]
Fully Neural Network based Model for General Temporal Point Processes(NeurIPS 2019) [Code]
Neural Jump Stochastic Differential Equations(NeurIPS 2019) [Code]
Deep Reinforcement Learning of Marked Temporal Point Processes(NeurIPS 2018) [Code]
Learning temporal point processes via reinforcement learning(NeurIPS 2018) [Code]
Improving Maximum Likelihood Estimation of Temporal Point Process via Discriminative and Adversarial Learning(IJCAI 2018)
JUMP: A Joint Predictor for User Click and Dwell Time(IJCAI 2018)
Learning Conditional Generative Models for Temporal Point Processes(AAAI 2018)
The Neural Hawkes Process(NeurIPS 2017) [Code]
Wasserstein Learning of Deep Generative Point Process Models(NeurIPS 2017) [Code]
Cascade Dynamics Modeling with Attention-based Recurrent Neural Network(IJCAI 2017)
Modeling the intensity function of point process via recurrent neural networks. (AAAI 2017) [Unofficial Pytorch]
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector(DDK 2016) [Code] [Unofficial Tensorflow] [Unofficial Pytorch]

Recent Advance in Temporal Point Process: from Machine Learning Perspective (2019)

A general framework for learning spatio-temporal point processes via reinforcement learning. [Code]