Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
Authors: Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real-world datasets demonstrate that the proposed method could effectively detect the hidden network among markers and make decent prediction for future marked events, even when the number of markers scales to million level. |
| Researcher Affiliation | Academia | 1Shanghai Key Laboratory of Scalable Computing and Systems 2Department of Computer Science and Engineering, Shanghai Jiao Tong University 3Mo E Key Lab of Artificial Intelligence, Shanghai Jiao Tong University 4State Key Labrotary of Novel Software Technology, Nanjing University {echo740, zzx_gongshi117}@sjtu.edu.cn, gao-xf@cs.sjtu.edu.cn yanjunchi@sjtu.edu.cn, gchen@nju.edu.cn |
| Pseudocode | Yes | Algorithm 1: Efficient Random Walk based Sampling for Generation of Next Event Marker |
| Open Source Code | Yes | The codes are released at https://github.com/zhangzx-sjtu/LANTERN-Neur IPS-2019. |
| Open Datasets | Yes | We also use two real-world datasets in our experiment. Firstly, Meme Tracker dataset [11] contains hyperlinks between articles and records information flow from one site to another... Besides, we consider a large-scale dataset Weibo [26] which records the resharing of posts among 1, 787, 443 users with 413, 503, 687 following edges. |
| Dataset Splits | No | The paper describes generating synthetic datasets and using real-world datasets, but it does not specify explicit train/validation/test splits, percentages, or sample counts for these datasets. |
| Hardware Specification | Yes | The experiments are deployed on Nvidia Tesla K80 GPUs with 12G memory and we statistic the running time to discuss the model scalability. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library versions like TensorFlow or PyTorch). |
| Experiment Setup | No | The paper states, 'The implementation details for baselines and hyper-parameter settings are presented in Appendix C.' However, the specific details of these settings are not provided within the main body of the analyzed text. |