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..
Multi-task Graph Neural Architecture Search with Task-aware Collaboration and Curriculum
Authors: Yijian Qin, Xin Wang, Ziwei Zhang, Hong Chen, Wenwu Zhu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real-world datasets validate the superiority of our proposed MTGC3 model over existing baselines via customizing the optimal architecture for each task and sharing useful information among them. |
| Researcher Affiliation | Academia | Yijian Qin1,2, Xin Wang1,2 , Ziwei Zhang1, Hong Chen1, Wenwu Zhu1,2 1Department of Computer Science and Technology, Tsinghua University, 2BNRist EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: MTGC3 |
| Open Source Code | Yes | 3https://github.com/THUMNLab/Auto GL-light |
| Open Datasets | Yes | Real-world Datasets. We choose three widely-used multi-task graph classification datasets included OGB [13]: OGBG-Tox21 [14], OGBG-Tox Cast [35], and OGBG-Sider [18]. |
| Dataset Splits | Yes | We choose three widely-used multi-task graph classification datasets included OGB [13]: OGBG-Tox21 [14], OGBG-Tox Cast [35], and OGBG-Sider [18]. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers. |
| Experiment Setup | Yes | Experimental Details. We set the number of layers as 3 for synthetic datasets, and 5 for real-world datasets. For all datasets except Tox Cast, we use the task-separate head. For Tox Cast, we use the cross-mixed head with 16 chunks. We train our models with a batch size of 32 for synthetic datasets and 128 for real-world datasets. We use Adam optimizer with a learning rate of 0.001. |