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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention
Authors: Byung-Hoon Kim, Jong Chul Ye, Jae-Jin Kim
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the HCP-Rest and the HCP-Task datasets demonstrate exceptional performance of our proposed method. |
| Researcher Affiliation | Academia | Byung-Hoon Kim Department of Psychiatry Institute of Behavioral Sciences in Medicine College of Medicine, Yonsei University EMAIL Jong Chul Ye Department of Bio/Brain Engineering Kim Jaechul Graduate School of AI KAIST EMAIL Jae-Jin Kim Department of Psychiatry Institute of Behavioral Sciences in Medicine College of Medicine, Yonsei University EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/egyptdj/stagin |
| Open Datasets | Yes | Publicly available2 f MRI data from the HCP S1200 release [45] was used for our experiments. |
| Dataset Splits | Yes | We performed 5-fold stratified cross-validation of the dynamic graphs from the dataset, and report mean and standard deviation across the folds. |
| Hardware Specification | Yes | Experiments were performed on a workstation with two NVIDIA Ge Force GTX 1080 Ti GPUs. |
| Software Dependencies | No | The paper mentions various models (e.g., GIN, GCN, Transformer encoder) and uses terms like 'Pytorch Geometric' in references, but does not specify exact version numbers for software dependencies like Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | We set the number of layers K = 4, embedding dimension D = 128, window length Γ = 50, window stride S = 3, and regularization coefficient λ = 1.0 10 5. ... Dropout rate 0.5 is applied to the final dynamic graph representation h Gdyn, and rate 0.1 is applied to the attention vectors zspace and ztime during training. One-cycle learning rate policy is employed, which the learning rate is gradually increased from 0.0005 to 0.001 during the early 20% of the training, and gradually decreased to 5.0 10 7 afterwise. Thirty training epochs were run for the HCP-Rest dataset with minibatch size 3, while ten epochs were run with minibatch size 16 for the HCP-Task dataset. |