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..
Learning High-Order Relationships of Brain Regions
Authors: Weikang Qiu, Huangrui Chu, Selena Wang, Haolan Zuo, Xiaoxiao Li, Yize Zhao, Rex Ying
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive experiments demonstrate the effectiveness of our model. Our model outperforms the state-of-the-art predictive model by an average of 11.2%, regarding the quality of hyperedges measured by CPM, a standard protocol for studying brain connections. ... We evaluate our methods on the open-source ABIDE dataset and the restricted ABCD dataset. We quantitatively evaluate our approach by a commonly used protocol for studying brain connections, CPM (Shen et al., 2017) (Appendix B), and show that our model outperforms the state-of-the-art deep learning models by an average of 11.2% on a comprehensive benchmark. Our post-hoc analysis demonstrates that hyperedges of higher degrees are considered more significant, which indicates the significance of high-order relationships in human brains. |
| Researcher Affiliation | Academia | 1Yale University, New Haven, USA 2University of British Columbia, Vancouver, Canada 3Vector Institute, Toronto, Canada. Correspondence to: Rex Ying <EMAIL>. |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly presented in the paper. |
| Open Source Code | Yes | Source code is available at https://github.com/ Graph-and-Geometric-Learning/ Hy BRi D. |
| Open Datasets | Yes | 1) Autism Brain Imaging Data Exchange (ABIDE) (Craddock et al., 2013) is an open-source dataset. ... 2) Adolescent Brain Cognitive Development (ABCD) (Casey et al., 2018) is one of the largest public f MRI datasets. |
| Dataset Splits | Yes | We randomly split the data into train, validation, and test sets in a stratified fashion. The split ratio is 8:1:1. |
| Hardware Specification | Yes | We train our model on a machine with an Intel Xeon Gold 6326 CPU and RTX A5000 GPUs. |
| Software Dependencies | Yes | Software See Table 5 for the software we used and the versions. Table 5: software version python 3.8.13 pytorch 1.11.0 cudatoolkit 11.3 numpy 1.23.3 ai2-tango 1.2.0 nibabel 4.0.2 |
| Experiment Setup | Yes | Hyperparameter choices and other details can be found in Appendix E. Table 6: Hyperparameter choices. notation meaning value lr learning rate 1 10 3 K number of hyperedges 32 β trade-off coefficients information bottleneck 0.2 [h1, h2, h3] hidden sizes of the dim reduction MLP [32, 8, 1] B batch size 64 |