Research map / Time series / Causal and generative time series
Flexible Intensities Matter: A comprehensive re-evaluation of Classical and Neural Temporal Point Processes
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Re-evaluates classical and neural temporal point process models under a unified comparison across intensity parameterizations, with large-scale medical benchmarks.
Problem
Recent neural temporal point process (TPP) models are reported to outperform classical baselines such as Hawkes processes, but a systematic comparison across model families and intensity parameterizations has been lacking, and large-scale medical benchmarks are scarce.
Approach
Systematic re-evaluation of classical and neural TPPs across model families and intensity parameterizations, emphasizing flexible intensity functions and large-scale medical event data.
Experimental setup
Baselines: Hawkes process
Closest papers by meaning
- Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes (NeurIPS 2026 · similarity 0.89)
- EasyTPP: Towards Open Benchmarking Temporal Point Processes (ICLR 2024 · similarity 0.89)
- Deep Continuous-Time State-Space Models for Marked Event Sequences (NeurIPS 2025 · similarity 0.86)
- Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling (NeurIPS 2026 · similarity 0.85)
- Interacting Diffusion Processes for Event Sequence Forecasting (ICML 2024 · similarity 0.85)
- Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations (ICLR 2024 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
