Easy Begun Is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Authors: Hongjun Wang, Jiyuan Chen, Tong Pan, Zipei Fan, Xuan Song, Renhe Jiang, Lingyu Zhang, Yi Xie, Zhongyi Wang, Boyuan Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our strategy can be applied to any canonical deep learning architecture without extra trainable parameters, and extensive experiments on a wide range of datasets are conducted to illustrate that, by controlling the difficulty level of ST relations as the training progresses, the model is able to capture better representation of the data and thus yields better generalization. |
| Researcher Affiliation | Collaboration | 1 SUSTech-UTokyo Joint Research Center on Super Smart City, Southern University of Science and Technology 2 Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology 3 Department of Physics, The Chinese University of Hong Kong 4 Center for Spatial Information Science, University of Tokyo 5 Information Technology Center, University of Tokyo 6 Huawei Technologies CO.LTD 7 Didichuxing Inc |
| Pseudocode | No | No explicitly labeled pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper mentions Faiss (https://github.com/facebookresearch/faiss) as a tool to accelerate computing, but it does not provide a link or statement about the availability of their own source code for the proposed methodology. |
| Open Datasets | Yes | In this section, we evaluate our proposed STC-Dropout on a wide range of spatial-temporal datasets so as to demonstrate its general applicability: METR-LA (Li et al. 2017), PEMSD7M (Yu, Yin, and Zhu 2017), Covid-19 (Panagopoulos, Nikolentzos, and Vazirgiannis 2021) and NYC-Crime (Xia et al. 2021). |
| Dataset Splits | No | Please refer to the appendix A.2-A.9 for more detailed descriptions of the datasets, baselines, experiment setup and extra results. |
| Hardware Specification | No | No specific hardware (e.g., GPU models, CPU types, or memory) used for the experiments is mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Last but not least, we explore the hyper-parametersensitivity to ρ, which controls the radius of the d(l)-dimensional ball, and α, which specifies the initial retain rate of nodes. The results of hyperparameter search from ρ α [0.05, 0.10, , 0.95] [0.1, 0.3, 0.9] on ST-GCN using both Pe MSD7M and METR-LA datasets are depicted in Figure 3. As we can see, the model performance is negatively related to the value of α. Thus, it is recommended to set its value to be relatively small to ensure only the easy samples can be retained in the early model training. Concerning ρ, we encourage maintaining ρ [0.25, 0.4] for desirable performances. |