Neural Ideal Point Estimation Network
Authors: Kyungwoo Song, Wonsung Lee, Il-Chul Moon
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed the five-fold cross-validation to quantitatively evaluate the variations of NIPENs, and the performance measures are RMSE, MAE, accuracy, and negative average log-likelihood (NALL) measures. We compared nine models: five baseline models in section 4.2, and four NIPEN variations |
| Researcher Affiliation | Academia | Kyungwoo Song, Wonsung Lee, Il-Chul Moon Korea Advanced Institute of Science and Technology 291 Daehak-ro, Yuseong-gu Daejeon 34141, South Korea {gtshs2,aporia,icmoon}@kaist.ac.kr |
| Pseudocode | No | The paper describes the model architecture and inference process, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: "For the research community, we released the dataset on https://github.com/gtshs2/NIPEN (Politic2013 was collected from (Gu et al. 2014))". This refers to the dataset release, not explicitly the source code for the methodology. |
| Open Datasets | Yes | We used two roll-call datasets. Table 1 provides the descriptive statistics of the two datasets: Politic2013 and Politic2016. For the research community, we released the dataset on https://github.com/gtshs2/NIPEN (Politic2013 was collected from (Gu et al. 2014)) |
| Dataset Splits | Yes | We performed the five-fold cross-validation to quantitatively evaluate the variations of NIPENs |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | We utilized the Tensorflow library (Abadi et al. 2016) to optimize the parameters. The paper mentions TensorFlow but does not specify its version number or any other software dependencies with versions. |
| Experiment Setup | No | The paper describes the model structure and parameter inference but does not explicitly provide hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific training schedules. |