mlsanityData to 7 Oct 2026

Fast and Flexible Temporal Point Processes with Triangular Maps

arXiv 2006.12631 ↗First posted 22 Jun 2020cs.LG stat.MLcited by 28

Not on the map. Why?

Abstract (arXiv)

Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, they are inherently sequential and therefore cannot benefit from the parallelism of modern hardware. By exploiting the recent developments in the field of normalizing flows, we design TriTPP -- a new class of non-recurrent TPP models, where both sampling and likelihood computation can be done in parallel. TriTPP matches the flexibility of RNN-based methods but permits orders of magnitude faster sampling. This enables us to use the new model for variational inference in continuous-time discrete-state systems. We demonstrate the advantages of the proposed framework on synthetic and real-world datasets.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 3; 2023: 5; 2024: 6; 2025: 4; 2026: 4.

28 papers.

Sort:

By area:

  1. 2021Neural Temporal Point Processes: A Reviewcited by 113
  2. 2024ContiFormer: Continuous-Time Transformer for Irregular Time Series Modelingcited by 61
  3. 2022Exploring Generative Neural Temporal Point Processcited by 30
  4. 2023Add and Thin: Diffusion for Temporal Point Processescited by 22
  5. 2023On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Datacited by 13
  6. 2024Unlocking Point Processes through Point Set Diffusioncited by 12
  7. 2024Poisson Variational Autoencodercited by 11
  8. 2021An Empirical Study: Extensive Deep Temporal Point Processcited by 10
  9. 2023Interacting Diffusion Processes for Event Sequence Forecastingcited by 9
  10. 2024Deep Continuous-Time State-Space Models for Marked Event Sequencescited by 9
  11. 2023Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processescited by 8
  12. 2024EventFlow: Forecasting Temporal Point Processes with Flow Matchingcited by 8
16 more
  1. 2023Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Predictioncited by 7
  2. 2025Edit-Based Flow Matching for Temporal Point Processescited by 7
  3. 2022FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernelscited by 5
  4. 2021A Differentiable Point Process with Its Application to Spiking Neural Networkscited by 4
  5. 2021Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deceptioncited by 4
  6. 2021Nonlinear Hawkes Processes in Time-Varying Systemcited by 3
  7. 2021DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signalscited by 2
  8. 2025Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approachescited by 2
  9. 2025Generative Unordered Flow for Set-Structured Data Generationcited by 2
  10. 2025TPP-SD: Accelerating Transformer Point Process Sampling with Speculative Decodingcited by 2
  11. 2024Unmixing Noise from Hawkes Process to Model Learned Physiological Eventscited by 1
  12. 2022Latent Discretization for Continuous-time Sequence Compressioncited by 0
  13. 2026Neural Diffusion Intensity Models for Point Process Datacited by 0
  14. 2026Efficient Temporal Point Processes via Monotone Alternating Splinescited by 0
  15. 2026From Jumps to Signatures: a Generative Method for Temporal Point Processescited by 0
  16. 2026Model-Based Reinforcement Learning for Heterogeneous Multi-Robot Task Assignment Under Distribution Shiftscited by 0