Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Authors: Hongyuan Mei, Jason M. Eisner
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We fit our various models on several simulated and real-world datasets, and evaluated them in each case by the log-probability that they assigned to held-out data. We also compared our approach with that of Du et al. (2016) on their prediction task. |
| Researcher Affiliation | Academia | Hongyuan Mei Jason Eisner Department of Computer Science, Johns Hopkins University 3400 N. Charles Street, Baltimore, MD 21218 U.S.A EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Algorithm descriptions are provided in prose. |
| Open Source Code | Yes | Our code and data are available at https://github.com/HMEIatJHU/neurawkes. |
| Open Datasets | Yes | Retweets Dataset (Zhao et al., 2015). Meme Track Dataset (Leskovec and Krevl, 2014). The electrical medical records (MIMIC-II) dataset is a collection of de-identified clinical visit records of Intensive Care Unit patients for 7 years. These datasets are cited or are standard benchmarks, indicating public availability. |
| Dataset Splits | Yes | We divide our data into training, validation, and test sets. We use the validation set to select the optimal model and hyperparameters, and report the log-likelihood on the held-out test set. |
| Hardware Specification | Yes | the NVIDIA Corporation kindly donated two Titan X Pascal GPUs. |
| Software Dependencies | No | The paper mentions using TensorFlow and Adam for optimization but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For all models, we initialize parameters uniformly from [−0.01, 0.01] and clip gradients to 1. We use the Adam (Kingma and Ba, 2015) optimization algorithm with a learning rate of 0.001. |