Continuous Conditional Dependency Network for Structured Regression
Authors: Chao Han, Mohamed Ghalwash, Zoran Obradovic
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance of CCDN on multiple datasets provides evidence of its structure recovery ability and superior effectiveness and efficiency as compared to the state-of-the-art alternatives. |
| Researcher Affiliation | Collaboration | Chao Han Center for Data Analytics and Biomedical Informatics, Temple University Philadelphia, PA 19122 USA chao.han@temple.edu Mohamed Ghalwash Center for Computational Health IBM T.J. Watson Research Center Cambridge, MA, USA Temple University, PA, USA Ain Shams University, Egypt, Cairo mohamed.ghalwash@temple.edu Zoran Obradovic Center for Data Analytics and Biomedical Informatics, Temple University Philadelphia, PA 19122 USA zoran.obradovic@temple.edu |
| Pseudocode | Yes | Algorithm 1 Iterated Conditional Mode |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | Wind power data is obtained from the Global Energy Forecasting 2012 competition1. Precipitation Forecasting. It can be downloaded from NOAA s NCDC website2. Solar Energy Forecasting. The data is available on kaggle 3. |
| Dataset Splits | Yes | We considered 4 different training sizes r = {60, 120, 180, 240}, and fix validation and testing sizes as 60. |
| Hardware Specification | No | The paper states 'The experiments on different data were running on different nodes of cluster.' but provides no specific hardware details such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The λ of SGCRF is picked from {1e 4, 1e 3, 1e 2, 0.1, 1} via 8 folds cross-validation. The neural network is set with one hidden layer with 20 hidden neurons. |