State Variable Effects in Graphical Event Models
Authors: Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on simulated and real-world benchmark datasets, two specific models from the proposed framework are shown to outperform various baselines, highlighting the promising modeling power of the high-level framework. We test the two parametric SVGEMs by evaluating how well they fit simulated and real-world joint temporal datasets. |
| Researcher Affiliation | Industry | Debarun Bhattacharjya , Dharmashankar Subramanian and Tian Gao Research AI, IBM T. J. Watson Research Center {debarunb, dharmash, tgao}@us.ibm.com |
| Pseudocode | No | The paper describes the model formulation and learning process in text, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any specific links to open-source code repositories or explicitly state that the code for the described methodology is available. |
| Open Datasets | Yes | We consider datasets that have been simulated or processed from publicly available sources; experimental details around processing and hyper-parameter choices are omitted here due to space restrictions but will be available on the ar Xiv version. [Frank and Asuncion, 2010] A. Frank and A. Asuncion. UCI machine learning repository, 2010. [Leskovec and Krevl, 2014] J. Leskovec and A. Krevl. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 2014. |
| Dataset Splits | Yes | We split each dataset three-ways by event stream into train (70%), dev (15%) and test (15%) sets, optimize each model s hyper-parameters from a grid using the train/dev sets, and then learn the final model on the train set. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'MATLAB simulator' for data generation but does not specify its version or any other software dependencies with version numbers used for the experiments. |
| Experiment Setup | No | The paper states that hyper-parameter choices are 'omitted here due to space restrictions' and 'will be available on the ar Xiv version', meaning specific values or ranges are not provided in the main text. |