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..
Sparse Bayesian structure learning with “dependent relevance determination” priors
Authors: Anqi Wu, Mijung Park, Oluwasanmi O Koyejo, Jonathan W Pillow
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Sec. 6, we show applications to simulated data and neuroimaging data. Beginning with simulated data, we compare performances of various regression estimators. |
| Researcher Affiliation | Academia | 1,4 Princeton Neuroscience Institute, Princeton University, EMAIL 2 The Gatsby Unit, University College London, EMAIL 3 Department of Psychology, Stanford University, EMAIL |
| Pseudocode | No | The paper describes computational procedures but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that code is made publicly available. |
| Open Datasets | Yes | We analyzed functional MRI data from the Human Connectome Project 1 collected from 215 healthy adult participants on a relational reasoning task. ... 1http://www.humanconnectomeproject.org/. |
| Dataset Splits | Yes | In each run, we randomly split the f MRI data into five sets for five-fold cross-validation, and took an average of test errors across five folds for each run. Hyperparameters were chosen by a five-fold cross-validation within the training set, and the optimal hyper parameter set was used for computing test performance. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions the 'flirt apply Xfm tool' but does not provide specific version numbers for software dependencies needed to replicate the experiment. |
| Experiment Setup | No | The paper states that hyperparameters were chosen via cross-validation but does not provide the specific values for these hyperparameters or other concrete experimental setup details like learning rates, batch sizes, or optimizer settings. |