NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling
Authors: Kun Wang, Hao Wu, Yifan Duan, Guibin Zhang, Kai Wang, Xiaojiang Peng, Yu Zheng, Yuxuan Liang, Yang Wang
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on six real-world ST benchmarks showcase that models can gain outcomes upon the integration of the Nuwa Dynamics concept. Nuwa Dynamics also can significantly benefit a wide range of changeable ST tasks like extreme weather and long temporal step super-resolution predictions. |
| Researcher Affiliation | Collaboration | Kun Wang2, , Hao Wu4, , Yifan Duan3, Guibin Zhang6, Kai Wang8, Xiaojiang Peng7, Yu Zheng9, Yuxuan Liang5 , Yang Wang1,2,3,4 1 Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China (USTC) 2Suzhou Institute for Advanced Research, USTC 3School of Software Engineering, USTC 4 School of Computer Science, USTC 5 Hong Kong University of Science and Technology (Guangzhou) 6 Tongji University 7 Shenzhen Technology University 8 National University of Singapore 9 JD i City, JD Technology |
| Pseudocode | No | The paper describes its methodology using textual descriptions and diagrams (e.g., Figure 3), but it does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Our codes are available at https://github.com/easylearningscores/Nuwa Dynamics. |
| Open Datasets | Yes | Datasets & Backbones We extensively evaluate our proposal on five benchmarks across diverse research domains, including Taxi BJ+ (Liang et al., 2021), KTH (Schuldt et al., 2004), SEVIR (Veillette et al., 2020), Rain Net (Ayzel et al., 2020), PD, and Fire Sys (Chen et al., 2022a). |
| Dataset Splits | No | Appendix C, Table 4, states: 'N tr and N te denote the number of instances in the training and test sets.' However, the paper does not explicitly provide details on how validation sets were created or their specific sizes/percentages, nor does it refer to predefined validation splits with citations. |
| Hardware Specification | Yes | We implement different backbones using Pytorch and leveraging the A100-PCIE40GB as support. |
| Software Dependencies | No | The paper mentions implementing backbones 'using Pytorch', but it does not specify a version number for Pytorch or any other software dependencies, which is necessary for reproducibility. |
| Experiment Setup | Yes | We train all models with Adam optimizer and learning rate as 0.01. |