Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model
Authors: Hao Wu, Yuxuan Liang, Wei Xiong, Zhengyang Zhou, Wei Huang, Shilong Wang, Kun Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and visualizations over eight human society physical and natural physical datasets demonstrates the stateof-the-art performance of Earth Farseer. |
| Researcher Affiliation | Academia | Hao Wu1, Yuxuan Liang2, Wei Xiong3*, Zhengyang Zhou1, Wei Huang3, Shilong Wang1, Kun Wang1* 1University of Science and Technology of China 2Hong Kong University of Science and Technology (Guangzhou) 3University of Tokyo 4Tsinghua University |
| Pseudocode | No | The paper describes the model architecture and components in text and diagrams (e.g., Fig 3), but it does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release our code at https://github.com/easylearningscores/Earth Farseer. |
| Open Datasets | Yes | We conduct extensive experiments on eight datasets, including two human social dynamics system (II, III), five natural scene datasets (IV, V, VI, VII, VIII) and a synthetic datasets (I) in Tab 1, for verifying the generalization ability and effectiveness of our algorithm. See dataset details in Appendix C and D. Moving MNIST (Srivastava, Mansimov, and Salakhudinov 2015), KTH (Schuldt, Laptev, and Caputo 2004), SEVIR (Veillette, Samsi, and Mattioli 2020). |
| Dataset Splits | No | Table 1 provides 'N tr' (number of training instances) and 'N te' (number of test instances) for each dataset, but it does not specify the size or percentage of a validation set, nor does the text describe the split ratios for validation. |
| Hardware Specification | Yes | We implement our model using Py Torch framework and leverage the four A100-PCIE40GB as computing support. We measure the time it takes for the model to reach optimal performance by conducting fair executions across all frameworks on a Tesla V100-40GB. |
| Software Dependencies | No | The paper states 'We implement our model using Py Torch framework' but does not provide specific version numbers for PyTorch or any other software libraries or dependencies. |
| Experiment Setup | Yes | We conduct experiments by selecting 2-14 Te Dev blocks layers, under settings with batch size as 16, training epochs as 300, and learning rate as 0.01 (Adam optimizer). |