AutoSTL: Automated Spatio-Temporal Multi-Task Learning
Authors: Zijian Zhang, Xiangyu Zhao, Hao Miao, Chunxu Zhang, Hongwei Zhao, Junbo Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. |
| Researcher Affiliation | Collaboration | 1 College of Computer Science and Technology, Jilin University, China 2 School of Data Science, City University of Hong Kong, Hong Kong 3 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China 4 Department of Computer Science, Aalborg University, Denmark 5 JD Intelligent Cities Research, China 6 JD i City, JD Technology, China 7 Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong |
| Pseudocode | No | The paper describes its methodology using textual descriptions and mathematical formulas but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link to a source code repository or an explicit statement confirming the release of their code. |
| Open Datasets | Yes | We evaluate Auto STL on two commonly used real-world benchmark datasets of spatio-temporal prediction, i.e., NYC Taxi1 and PEMSD42. 1https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page 2http://pems.dot.ca.gov/ |
| Dataset Splits | No | The paper mentions using 'validation data' for optimization, but it does not specify the exact percentages or counts for training, validation, and test splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | We predict the traffic attribute of the future 1 time interval based on the historical 12 time steps, i.e., |T| = 12. In terms of model structure, we assign 1 task-specific module and 1 shared module in each hidden layer, e.g., 3 modules in one hidden layer for two-tasks learning. We stack 3 hidden layers in total. We test hidden size in {16, 32, 64, 128}. |