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..
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
Authors: Kristjan Greenewald, Seyoung Park, Shuheng Zhou, Alexander Giessing
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our methodology to the discovery of time-varying spatial structures in human brain f MRI signals. and 4 Simulation study and 5 f MRI Application |
| Researcher Affiliation | Academia | Kristjan Greenewald Department of Statistics Harvard University, Seyoung Park Department of Biostatistics Yale University, Shuheng Zhou Department of Statistics University of Michigan, Alexander Giessing Department of Statistics University of Michigan |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We consider the ADHD-200 f MRI dataset (Biswal et al., 2010), and study resting state f MRIs for a variety of healthy patients in the dataset at different stages of development. |
| Dataset Splits | No | The paper uses the ADHD-200 f MRI dataset but does not specify explicit training, validation, or test dataset splits or cross-validation details. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions methods like GLasso and ADMM but does not list any specific software or library names with version numbers for reproducibility. |
| Experiment Setup | Yes | Initially we set Θ(0) = 0.25In n, where n = 100. (Simulation Study) and We use a Gaussian kernel with bandwidth h, and estimate the graphs using a variety of values of λ and h. (f MRI Application). |