mlsanityData to 7 Oct 2026

A Variational Auto-Encoder Model for Stochastic Point Processes

arXiv 1904.03273 ↗First posted 5 Apr 2019cs.CV cs.LGcited by 30

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Abstract (arXiv)

We propose a novel probabilistic generative model for action sequences. The model is termed the Action Point Process VAE (APP-VAE), a variational auto-encoder that can capture the distribution over the times and categories of action sequences. Modeling the variety of possible action sequences is a challenge, which we show can be addressed via the APP-VAE's use of latent representations and non-linear functions to parameterize distributions over which event is likely to occur next in a sequence and at what time. We empirically validate the efficacy of APP-VAE for modeling action sequences on the MultiTHUMOS and Breakfast datasets.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2019.

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Citing papers per year: 2021: 5; 2022: 12; 2023: 8; 2024: 2; 2025: 1; 2026: 2.

30 papers.

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  1. 2022P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervisioncited by 52
  2. 2023Spatio-temporal Diffusion Point Processescited by 52
  3. 2022Bayesian Prompt Learning for Image-Language Model Generalizationcited by 51
  4. 2022Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Datacited by 36
  5. 2022Intention-Conditioned Long-Term Human Egocentric Action Forecastingcited by 35
  6. 2022Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimizationcited by 32
  7. 2022Rethinking Learning Approaches for Long-Term Action Anticipationcited by 29
  8. 2023Pretrained Language Models as Visual Planners for Human Assistancecited by 28
  9. 2022Predicting the Next Action by Modeling the Abstract Goalcited by 14
  10. 2022ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequencescited by 11
  11. 2023A Survey on Deep Learning Techniques for Action Anticipationcited by 11
  12. 2023DiffAnt: Diffusion Models for Action Anticipationcited by 9
18 more
  1. 2024Human Action Anticipation: A Surveycited by 8
  2. 2023Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Predictioncited by 7
  3. 2021Deep Neyman-Scott Processescited by 6
  4. 2021Review of Video Predictive Understanding: Early Action Recognition and Future Action Predictioncited by 5
  5. 2022PCENet: High Dimensional Surrogate Modeling for Learning Uncertaintycited by 5
  6. 2023Modeling Events and Interactions through Temporal Processes -- A Surveycited by 5
  7. 2023Streaming egocentric action anticipation: An evaluation scheme and approachcited by 5
  8. 2025LAST SToP For Modeling Asynchronous Time Seriescited by 4
  9. 2022Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processescited by 3
  10. 2021Individual Mobility Prediction via Attentive Marked Temporal Point Processescited by 2
  11. 2022Modeling Time-Series and Spatial Data for Recommendations and Other Applicationscited by 2
  12. 2023Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flowscited by 1
  13. 2024Pretext Training Algorithms for Event Sequence Datacited by 1
  14. 2021PLAN-B: Predicting Likely Alternative Next Best Sequences for Action Predictioncited by 0
  15. 2021Analysis of ODE2VAE with Examplescited by 0
  16. 2022Finding Islands of Predictability in Action Forecastingcited by 0
  17. 2026Neural Diffusion Intensity Models for Point Process Datacited by 0
  18. 2026ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Seriescited by 0