Survey paper details LLM, neural, and Bayesian approaches to temporal point processes

A new survey paper reviews the latest advancements in temporal point processes (TPPs), which are models used to analyze event sequences. The paper covers traditional Bayesian methods, newer neural network approaches, and the emerging application of large language models (LLMs) in this field. It details model design and estimation techniques across these three frameworks and discusses their practical applications, while also identifying future research challenges. AI

IMPACT Provides a comprehensive overview of LLM applications in analyzing event sequences, potentially guiding future research and development in AI-driven temporal analysis.

RANK_REASON This is a survey paper published on arXiv, categorizing it as research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• Feng Zhou
• LLM Approaches
• Temporal Point Processes

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