mlsanityData to 14 Oct 2026

Meta Temporal Point Processes

arXiv 2301.12023 ↗First posted 27 Jan 2023cs.LGcited by 17

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Abstract (arXiv)

A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time. Recent work in TPPs model the process using a neural network in a supervised learning framework, where a training set is a collection of all the sequences. In this work, we propose to train TPPs in a meta learning framework, where each sequence is treated as a different task, via a novel framing of TPPs as neural processes (NPs). We introduce context sets to model TPPs as an instantiation of NPs. Motivated by attentive NP, we also introduce local history matching to help learn more informative features. We demonstrate the potential of the proposed method on popular public benchmark datasets and tasks, and compare with state-of-the-art TPP methods.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 24 of its 39 references that are in the map. First posted on arXiv 27 Jan 2023.

Builds on

24 of 39 references.

References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  4. 2016Matching Networks for One Shot Learningcited by 2,255
  5. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  6. 2018On First-Order Meta-Learning Algorithmscited by 1,018
  7. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 448
  8. 2018Conditional Neural Processescited by 426
  9. 2018Neural Processescited by 311
  10. 2019Attentive Neural Processescited by 283
  11. 2017Meta Networkscited by 269
  12. 2018Probabilistic Model-Agnostic Meta-Learningcited by 256
12 more
  1. 2020Transformer Hawkes Processcited by 235
  2. 2018Recasting Gradient-Based Meta-Learning as Hierarchical Bayescited by 180
  3. 2019Convolutional Conditional Neural Processescited by 142
  4. 2019Intensity-Free Learning of Temporal Point Processescited by 140
  5. 2022Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modelingcited by 127
  6. 2019Fully Neural Network based Model for General Temporal Point Processescited by 122
  7. 2021Neural Temporal Point Processes: A Reviewcited by 114
  8. 2021Neural Flows: Efficient Alternative to Neural ODEscited by 101
  9. 2021Transformer Embeddings of Irregularly Spaced Events and Their Participantscited by 93
  10. 2020Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processescited by 76
  11. 2020Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviourscited by 47
  12. 2022Exploring Generative Neural Temporal Point Processcited by 30

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 7; 2025: 3; 2026: 2.

17 papers.

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  1. 2023EasyTPP: Towards Open Benchmarking Temporal Point Processescited by 68
  2. 2024Deep Continuous-Time State-Space Models for Marked Event Sequencescited by 11
  3. 2023Interacting Diffusion Processes for Event Sequence Forecastingcited by 10
  4. 2023Memory Efficient Neural Processes via Constant Memory Attention Blockcited by 9
  5. 2024TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Modelscited by 9
  6. 2024Attention as an RNNcited by 6
  7. 2024Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equationscited by 6
  8. 2023AdaFlood: Adaptive Flood Regularizationcited by 2
  9. 2023Constant Memory Attention Blockcited by 1
  10. 2024Cumulative Distribution Function based General Temporal Point Processescited by 1
  11. 2024Pretext Training Algorithms for Event Sequence Datacited by 1
  12. 2025Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processescited by 1
5 more
  1. 2024Marked Temporal Bayesian Flow Point Processescited by 0
  2. 2025Learning under Commission and Omission Event Outlierscited by 0
  3. 2025Deep Graph Neural Point Process For Learning Temporal Interactive Networkscited by 0
  4. 2026From Hawkes Processes to Attention: Time-Modulated Mechanisms for Event Sequencescited by 0
  5. 2026Amortized Variational Inference of Latent Intensities from Replicated Point Patternscited by 0