Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
A Spatial Missing Value Imputation Method for Multi-view Urban Statistical Data
Authors: Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Bei Chen, Xiangjun Dong
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Intensive experiments are conducted with other state-of-the-art approaches on six real-world urban statistical datasets. The results not only show the superiority of our method against other comparative methods on different datasets, but also represent a strong generalizability of our model. |
| Researcher Affiliation | Collaboration | 1Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia 2Microsoft Research, Beijing, China 3School of Computer, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China |
| Pseudocode | Yes | Algorithm 1: SMV-NMF |
| Open Source Code | Yes | The strict proof, resource code, and parameters used to achieve the best performance on different datasets are shown in the https://github.com/SMV-NMF/SMV-NMF. |
| Open Datasets | Yes | There are six real-world urban statistical datasets (Sydney, Melbourne, Brisbane, Perth, SYD-large, and MEL-large), where -large datasets contain much more ๏ฌne-grained regions from Australian Bureau of Statistics (2017). |
| Dataset Splits | Yes | We pick up half of statistical ๏ฌelds (properties) in each urban dataset randomly as the validation set, and the other half as the test set. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU, GPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper mentions that "All parameters of the proposed method and baselines are optimized by the grid search method." and that "parameters used to achieve the best performance on different datasets are shown in the https://github.com/SMV-NMF/SMV-NMF." However, it does not explicitly state these specific parameter values or other experimental setup details within the main text of the paper. |