Researchers have developed a new neural point process model that directly parameterizes the conditional intensity function (CIF) using B-spline basis functions. This approach, predicted by a neural network, allows for exact evaluation of the negative log-likelihood (NLL) and enables efficient parallelization during training. The model also naturally supports CIF smoothness regularization and has demonstrated improved computational efficiency and predictive accuracy on synthetic and real-world datasets compared to existing neural TPP baselines. AI
IMPACT This novel approach could lead to more efficient and accurate modeling of sequential event data in various AI applications.
RANK_REASON The cluster contains a research paper detailing a new method for neural point processes. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Conditional intensity function (CIF)
- Maximum Likelihood Estimation (MLE)
- Negative log-likelihood (NLL)
- Neural point processes
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