{"total":20,"rows":[{"id":"2104.03528","title":"Neural Temporal Point Processes: A Review","created":"2021-04-08","cited_by_count":114,"area":null},{"id":"2201.00044","title":"Transformer Embeddings of Irregularly Spaced Events and Their Participants","created":"2021-12-31","cited_by_count":93,"area":null},{"id":"2210.01753","title":"HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences","created":"2022-10-04","cited_by_count":57,"area":null},{"id":"2305.12403","title":"Spatio-temporal Diffusion Point Processes","created":"2023-05-21","cited_by_count":52,"area":null},{"id":"2208.01874","title":"Exploring Generative Neural Temporal Point Process","created":"2022-08-03","cited_by_count":30,"area":null},{"id":"2306.17066","title":"On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data","created":"2023-06-29","cited_by_count":14,"area":null},{"id":"2110.09823","title":"An Empirical Study: Extensive Deep Temporal Point Process","created":"2021-10-19","cited_by_count":10,"area":null},{"id":"2210.15294","title":"Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes","created":"2022-10-27","cited_by_count":7,"area":null},{"id":"2310.16336","title":"SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process","created":"2023-10-25","cited_by_count":7,"area":null},{"id":"2311.01033","title":"Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction","created":"2023-11-02","cited_by_count":7,"area":null},{"id":"2401.04612","title":"Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes","created":"2024-01-09","cited_by_count":7,"area":null},{"id":"2210.04635","title":"FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels","created":"2022-10-10","cited_by_count":5,"area":null},{"id":"2501.14291","title":"Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches","created":"2025-01-24","cited_by_count":3,"area":null},{"id":"2112.06652","title":"DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals","created":"2021-12-08","cited_by_count":2,"area":null},{"id":"2402.00987","title":"Self-Supervised Contrastive Pre-Training for Multivariate Point Processes","created":"2024-02-01","cited_by_count":2,"area":null},{"id":"2310.16310","title":"Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification","created":"2023-10-25","cited_by_count":1,"area":null},{"id":"2102.02741","title":"Hawkes Processes on Graphons","created":"2021-02-04","cited_by_count":0,"area":null},{"id":"2308.02360","title":"Intensity-free Integral-based Learning of Marked Temporal Point Processes","created":"2023-08-04","cited_by_count":0,"area":null},{"id":"2502.04899","title":"Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects","created":"2025-02-07","cited_by_count":0,"area":null},{"id":"2510.20414","title":"Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes","created":"2025-10-23","cited_by_count":0,"area":null}],"next_cursor":null}