The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Authors: Hongyuan Mei, Jason M. Eisner
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 {hmei,jason}@cs.jhu.edu |
| 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. |