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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
Authors: Zijian Zhang, Xiangyu Zhao, Hao Miao, Chunxu Zhang, Hongwei Zhao, Junbo Zhang
AAAI 2023 | Venue PDF | 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}. |